From a540a3f27108ea76b010fab693eb1fef75602ece Mon Sep 17 00:00:00 2001 From: David Rotermund Date: Mon, 21 Oct 2024 16:43:42 +0200 Subject: [PATCH] Initial --- avg_pooling_conv2d/L1NormLayer.py | 13 + ...20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt | Bin 0 -> 687000 bytes avg_pooling_conv2d/NNMF2d.py | 252 ++++++++++++++++ .../NOTE_THIS_ONE_DIES_AFTER_SOME_ITERATIONS | 0 avg_pooling_conv2d/append_block.py | 111 +++++++ avg_pooling_conv2d/append_parameter.py | 8 + avg_pooling_conv2d/convert_log_to_numpy.py | 31 ++ avg_pooling_conv2d/data_loader.py | 31 ++ ...0_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy | Bin 0 -> 3024 bytes avg_pooling_conv2d/get_the_data.py | 147 ++++++++++ ...nts.out.tfevents.1724150849.gp4u5.926684.0 | Bin 0 -> 29744 bytes avg_pooling_conv2d/loss_function.py | 64 ++++ avg_pooling_conv2d/make_network.py | 212 ++++++++++++++ avg_pooling_conv2d/make_optimize.py | 32 ++ .../non_linear_weigth_function.py | 26 ++ avg_pooling_conv2d/plot.py | 15 + 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max_pooling_nnmf/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724147482.gp4u4.933902.0 create mode 100644 max_pooling_nnmf/loss_function.py create mode 100644 max_pooling_nnmf/make_network.py create mode 100644 max_pooling_nnmf/make_optimize.py create mode 100644 max_pooling_nnmf/non_linear_weigth_function.py create mode 100644 max_pooling_nnmf/plot.py create mode 100644 max_pooling_nnmf/run_network.py create mode 100644 plot.py diff --git a/avg_pooling_conv2d/L1NormLayer.py b/avg_pooling_conv2d/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_conv2d/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git 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zsneQBBdv7sK-b*9+PwGFMkC+W9Wc=6=QU8h3jGIUf}Cvq}i{V&YS-zb0%~6VYZw<|6%6LX2<6)y0mdl%pqVdvjhB?Q3mHd%A5`0$Bc6R zyhoYy{_njoKX6^H-8An}=DhuTKQ<2><@oWw`M&+ None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_conv2d/NOTE_THIS_ONE_DIES_AFTER_SOME_ITERATIONS b/avg_pooling_conv2d/NOTE_THIS_ONE_DIES_AFTER_SOME_ITERATIONS new file mode 100644 index 0000000..e69de29 diff --git a/avg_pooling_conv2d/append_block.py b/avg_pooling_conv2d/append_block.py new file mode 100644 index 0000000..80142d9 --- /dev/null +++ b/avg_pooling_conv2d/append_block.py @@ -0,0 +1,111 @@ +import torch +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + if last_layer is False: + + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_conv2d/append_parameter.py b/avg_pooling_conv2d/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_conv2d/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_conv2d/convert_log_to_numpy.py b/avg_pooling_conv2d/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_conv2d/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_conv2d/data_loader.py b/avg_pooling_conv2d/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_conv2d/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/avg_pooling_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/avg_pooling_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..a1ac57232fa1099dee514a227714aae5173be27d GIT binary patch literal 3024 zcmbW#OKi+h9L4eL87dx$CM-yXia{pT8jq^iZAYN+YKQ$lE%VHVipoaf(>D(^*hJoEd4h(pZWImw*Ph@)85?DF~S{pCz9Ry zzOF*DHj!M}QT@I+a*7Kb1I{ z_|K=z{rz0L>5X63{5qR%!OqU~ZFroWZFk@)c7CPH&8P07Pq*_WJ;QwN9(uygk@w-* zb}pvpn6J@u&4ctj^96c=`5m1yPkLadtF$viSD7c%)#lOkB6B^Rmi=P$8TKXS0eY#q 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flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/avg_pooling_conv2d/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724150849.gp4u5.926684.0 b/avg_pooling_conv2d/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724150849.gp4u5.926684.0 new file mode 100644 index 0000000000000000000000000000000000000000..ecc520617200575dfa31f44562b6ab9d5f030f9f GIT binary patch literal 29744 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z3;#~KHmizN`d*8H*McQpJI torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_conv2d/make_network.py b/avg_pooling_conv2d/make_network.py new file mode 100644 index 0000000..464d9b9 --- /dev/null +++ b/avg_pooling_conv2d/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Conv2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # kernel_size=(1, 1), + # bias=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_conv2d/make_optimize.py b/avg_pooling_conv2d/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_conv2d/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_conv2d/non_linear_weigth_function.py b/avg_pooling_conv2d/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_conv2d/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_conv2d/plot.py b/avg_pooling_conv2d/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_conv2d/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_conv2d/run_network.py b/avg_pooling_conv2d/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_conv2d/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/avg_pooling_conv2d_noinbetween1x1/L1NormLayer.py b/avg_pooling_conv2d_noinbetween1x1/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/avg_pooling_conv2d_noinbetween1x1/Model_iter20_lr_-_1.0000e-02_1.0000e-03_.pt b/avg_pooling_conv2d_noinbetween1x1/Model_iter20_lr_-_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..189a11d85d6e701c5631e1673a9efb839f40d9ef GIT binary patch literal 622466 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z{$3$d3lmcdOVcS9=2n*GmZm1tEG?#*m`<5uYGGw!ZfR*Y)r!3{pKAJ7EpK0(HfX?V z_*a?lDEe1r|5r~rvA$5>LQO>EuVVVwFaCD@t9~;%cwOK>!^aCAjrz|HDk5U`x6sGf zKm5NA@3Sq`H)zvp-%bCFza-6VLX<9R0<0MSE&kFG|04c>+;fwE20ttE;Y$3@08t^OJOzYgzz??nn>|04MR zm{k55{l5;&h_#ykeP#cf=>PqMG2c-_V$%PJs3~srzhW!=k5~Wm`}3jy`GaZOr2pdu h`#Z_MUT8=D=lj2!^L)ple~sZH3he8z&;RFV{~v?E&Y%DQ literal 0 HcmV?d00001 diff --git a/avg_pooling_conv2d_noinbetween1x1/NNMF2d.py b/avg_pooling_conv2d_noinbetween1x1/NNMF2d.py new file mode 100644 index 0000000..b84d083 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/NNMF2d.py @@ -0,0 +1,252 @@ +import torch +from non_linear_weigth_function import non_linear_weigth_function + + +class NNMF2d(torch.nn.Module): + + in_channels: int + out_channels: int + weight: torch.Tensor + iterations: int + epsilon: float | None + init_min: float + init_max: float + beta: torch.Tensor | None + positive_function_type: int + local_learning: bool + local_learning_kl: bool + + def __init__( + self, + in_channels: int, + out_channels: int, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_conv2d_noinbetween1x1/append_block.py b/avg_pooling_conv2d_noinbetween1x1/append_block.py new file mode 100644 index 0000000..5dd5dac --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/append_block.py @@ -0,0 +1,66 @@ +import torch +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_conv2d_noinbetween1x1/append_parameter.py b/avg_pooling_conv2d_noinbetween1x1/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_conv2d_noinbetween1x1/convert_log_to_numpy.py b/avg_pooling_conv2d_noinbetween1x1/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_conv2d_noinbetween1x1/data_loader.py b/avg_pooling_conv2d_noinbetween1x1/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/avg_pooling_conv2d_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy b/avg_pooling_conv2d_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..d7b36645877839ae944f448c19df650e24bff2a2 GIT binary patch literal 5344 zcmbW%TZmOv9LMohV3ZFrl#xNuqOwsN@++6lgPQfotPov&@}sCHGGHa2!tn_Dh?KBK9!ojEXY1LM_;OpnIE8C${Y4&PZhM=2 zsI6z@>&>U$A@{Z(l5aHsCEsjb_^z$)7F)aJ;pSR-r1^2V&U{?H&HRd7GM|)3n}?sa z)s3-rm-2e^VCA=)x65PA?eaMD6nVV)sN7)glJ77d)4U1hRQW{nm+~a@3-V-hzkH{; zSDs>iMh@lyoZ>?HRLehAj$c)Nm*qp=vo*!PDxYSbk5G=!DW7inSmk&Tc6VD|uN?1G 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ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/avg_pooling_conv2d_noinbetween1x1/log_iter20_lr_-_1.0000e-02_1.0000e-03_/events.out.tfevents.1724150768.gp4u7.878668.0 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Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_conv2d_noinbetween1x1/make_network.py b/avg_pooling_conv2d_noinbetween1x1/make_network.py new file mode 100644 index 0000000..464d9b9 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Conv2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # kernel_size=(1, 1), + # bias=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_conv2d_noinbetween1x1/make_optimize.py b/avg_pooling_conv2d_noinbetween1x1/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_conv2d_noinbetween1x1/non_linear_weigth_function.py b/avg_pooling_conv2d_noinbetween1x1/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_conv2d_noinbetween1x1/plot.py b/avg_pooling_conv2d_noinbetween1x1/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_conv2d_noinbetween1x1/run_network.py b/avg_pooling_conv2d_noinbetween1x1/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_conv2d_noinbetween1x1/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/avg_pooling_mlp/L1NormLayer.py b/avg_pooling_mlp/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_mlp/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/avg_pooling_mlp/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/avg_pooling_mlp/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..906968d4246f966e63304e052d17473af2799df4 GIT binary patch literal 687868 zcmbTe2UHYGx4#Wa77+s?CR7A5fPxrdx@tf%fT$xXMnHmt5}al*f`Vj3L=i>Agkly^ zQBj$y8Vs0o!W@r+IcNQonc;e^>lQMjCB6` z*H_10XGm0VNVtEPGDLLl<{vKlJ2`fv|3VzPb^n9Tf3UlM*Wf^9pyRZu;hyg8b;kbx z_!nO%NEsyt$sHpj9V4QGW5PqC9fP7Gm4RWAA)?8M5JgN#q%tfp+(e<%F+rglr>JA^ z$3Co5q_1Hm@s~$MO^sHVX^!5&ufhKq=VRuY zv4(z4*;rV#e`rj2_@862eH4v+{LI*BXq2cqF#pBInqO?<*Zi*`{~2LQxj*|5lcNlml#WG}CqF4xoi zerGvvoGx->Iqd_7WQC(l(N)gt-A&GG>m+aFBj?p}mh*CSm-B49$az|O$a&6s%6WEr z$$1WX%elxtaxS8;oPTatKSTPl!y>1J2L^>G+|pwvX>>R#B!BA&jS3Go85ybBCe@X=}*s?{(| zt6{k2lARhNM$*|OI&5}`LguCKrJtf0p{Z1GShyzn3STc>FWnTyNR8xafx*G-f*<9j zr>pR5Rw zDFU@wn54}@(4Q;>YqJod%|fU)3zM~3kZZFLrp>|>Z5F0#vkOiNZMWC~F(}16pnIcxJI8Lj0?jObTw2I@kiW9Vo=W7)wY85AG z6(?&Ir)U+YY89tx3K=pjIxIXYQjtEu^+)1~q5Cdw*UE5FWc*z_dNY`A;z5lwt=(6G@9O;0w;$kmT2qZNv9g+2p%+1Vr_|bYMPSX z;)3LXk@WJ6R+M;o`I*uWMjQ1{aFU=N=3Q0#d;`If4A7AKxL3T zl3qO~?u^)V#F`D!X2Wp`Qmfh2_VPvYY&q=@`4Ty=zmuY(cKr`~wNWaL(DHk=Nmd;7 z(oI$z;{~r&Dvrw(CwRe6rYcU!6sP5D{^MZIiZitjc9tFNoc3Vn*}*RGgI!cAF3A*^ z`N6KFDz3^D*Z%F8>-L)Li7}CEedHez5*Vqd^3jy_-`Mm(QE|gZvu25miJ(gksyZav zpH?eqDlOGb+I^av+O1^8ZJFYZw(IU{yYAkfuDh@8x(C{>d#LTYN7}A?tnIpgv|aZ^ z+jUR1U01E`x*E+#W}hXLRxm~kiB>%GYU5XzmYL2Lisu7_N1ykmJ(R!E+#e#MBIx2Q zCPMLI064IM{ma*6Eig2ct{N3D(=^I7YW)9|%+?~&idX;ME_*6o%h^r+O`PIwt@^&x zUUl!e{twBDk21w4t^Uti{a^m*|EkshO{@RAR{syJ{-0X?zqo!;hb}pI3PfFonp?YA zhoR=~F4krE`=;JY)MGxK^+bINaiRgWe?G2gsB!@k0=$5y#*p!8IlSNCqgQtZljZ@QdMfnqo2 z(y32=Kz>kOYw8l6=zwMc66z39XwcBc?0x=>rIA9`rq|LlhzVow^cU6NkZXC>)P zx7w2QVIf+QzI4k=;z~&=x=||=CDihg2$WJqpov0i|7w7qqLhYfpUs{6>}(!%t36vk z7NTeCPq+MRo|Kg00BU98Kx+Bf22n~C2Wz5U)c)H5-jwa-RX*|?KJtHOD{%-7CW}6r zorhAZ?S)|sc`poS_`4Tm%;UW!VGnsijGjfJJGhqyQ zW~MOYnVHIvXC|B>&rAeEew9Qr$h$QFKkf9tlJx9rB^q zO&vPu)J=?G=b$sbIGsYAID^_+S)0j5HP3V6EQXq_&8Czr&Y@N&#@~kB=9G1S~1G_st;bQYjb zc47vFIB@~BwPa)0sQTF6kNbv>nIaRaq7v4};v){P9g)=dn5Yu(H|u5}ATu5~Lzu5}wj zu5~*@u5|}PuC(jbISQJE4jj^^E??T5XUEWbY^igB`uyH=k&;rpM6FD` zOfA3nT%nXIUe!deQ7ixbP$phyt|rA*l#<08nw@U4C{OV%hCIc$8U9Z39p>>A-(|>C ze2*bd@qLCo#Sa+r6hCChQ~Zb_Pw``hJjMSo+_M4Qv5`%O#DnOug@1ssp3~n^c%H*twj2W-)Xq^*?v%; zo$V*xYR~qIh3MIIXdm*k=~7aPb*Pnzb*bfN)1#Cs>T99~)c(5?F{J$0O2mi;lSO0A z&L-4qyP_UL-W8?{e|JTF=JBpDW5~Or0Ylyu<_vjPG-SxTq7g&h6^$A4u4uxLcZCH* z-W5$5KBEos|6YmEy>y&J>3)*qNkfmxBPVBl$2rwwK6f1T7J4HN~z*BO;kbczvUMx|22n2(_pfw)a)EX zt+o@UGvu8xgW>N^n8`fe39}gTPMFP*cfuToyc1#>@=l0j$U9*!L*5DV81hbtXUIDt zf#LtpIjoO3pYA?I7j>>P$%_ELsib}mC1e?0p0lr%-8 z($c3yQ=2wLN$tN6JI>w2W$ZWD^u3%yoR~*#E!`{FDA%3OP@{V#rDU;yTA8?tMY-

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positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_nnmf/append_block.py b/avg_pooling_nnmf/append_block.py new file mode 100644 index 0000000..a8de5d2 --- /dev/null +++ b/avg_pooling_nnmf/append_block.py @@ -0,0 +1,149 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_nnmf/append_parameter.py b/avg_pooling_nnmf/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_nnmf/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_nnmf/convert_log_to_numpy.py b/avg_pooling_nnmf/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_nnmf/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_nnmf/data_loader.py b/avg_pooling_nnmf/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_nnmf/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/avg_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/avg_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..2ac820d008fc098fa2395816d967b8655ebf95ed GIT binary patch literal 7984 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torchvision # type: ignore +from data_loader import data_loader + +from torchvision.transforms import v2 # type: ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git 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zKXX}%Yya!gw!{l%ld3F=E@JVX*`aVI8k%kdgwk8nBmLgM9k5JFNo7S>cifphfBoVF zp=?%_{oM;$$OG?>0p%_!Ae4O1bGAzw{)McQQc^j+=ICWOE^Ga#BnoAVs+m`7Wf58U5N|#sj64R6cugC9Z*a;k$q&p**K5AG%)1`nWB?@Aw)iAe8($ z(Yd}U`2WY9Qc5ag=iHM3W9jdCbB<89s>)i=Lbf>6doIklT?z+2-8B#(Q*61m){eKq&bg#TmbJTm!R6N=c>DH@QD3cWp{b70SO torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_nnmf/make_network.py b/avg_pooling_nnmf/make_network.py new file mode 100644 index 0000000..0a2c532 --- /dev/null +++ b/avg_pooling_nnmf/make_network.py @@ -0,0 +1,215 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # NNMF2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # epsilon=epsilon, + # positive_function_type=positive_function_type, + # beta=beta, + # iterations=iterations, + # local_learning=False, + # local_learning_kl=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_nnmf/make_optimize.py b/avg_pooling_nnmf/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_nnmf/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_nnmf/non_linear_weigth_function.py b/avg_pooling_nnmf/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_nnmf/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_nnmf/plot.py b/avg_pooling_nnmf/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_nnmf/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_nnmf/run_network.py b/avg_pooling_nnmf/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_nnmf/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/avg_pooling_nnmf_noinbetween1x1/L1NormLayer.py b/avg_pooling_nnmf_noinbetween1x1/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git 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float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_nnmf_noinbetween1x1/append_block.py b/avg_pooling_nnmf_noinbetween1x1/append_block.py new file mode 100644 index 0000000..e0e9c9b --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/append_block.py @@ -0,0 +1,109 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_nnmf_noinbetween1x1/append_parameter.py b/avg_pooling_nnmf_noinbetween1x1/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_nnmf_noinbetween1x1/convert_log_to_numpy.py b/avg_pooling_nnmf_noinbetween1x1/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_nnmf_noinbetween1x1/data_loader.py b/avg_pooling_nnmf_noinbetween1x1/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git 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v2 # type: ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/avg_pooling_nnmf_noinbetween1x1/log_iter20_lr_-_1.0000e-02_1.0000e-03_/events.out.tfevents.1724149649.gp4u3.1010554.0 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cross_entropy +def loss_function( + h: torch.Tensor, + labels: torch.Tensor, + loss_mode: int = 0, + number_of_output_neurons: int = 10, + loss_coeffs_mse: float = 0.0, + loss_coeffs_kldiv: float = 0.0, +) -> torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_nnmf_noinbetween1x1/make_network.py b/avg_pooling_nnmf_noinbetween1x1/make_network.py new file mode 100644 index 0000000..0a2c532 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/make_network.py @@ -0,0 +1,215 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # NNMF2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # epsilon=epsilon, + # positive_function_type=positive_function_type, + # beta=beta, + # iterations=iterations, + # local_learning=False, + # local_learning_kl=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_nnmf_noinbetween1x1/make_optimize.py b/avg_pooling_nnmf_noinbetween1x1/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_nnmf_noinbetween1x1/non_linear_weigth_function.py b/avg_pooling_nnmf_noinbetween1x1/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_nnmf_noinbetween1x1/plot.py b/avg_pooling_nnmf_noinbetween1x1/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_nnmf_noinbetween1x1/run_network.py b/avg_pooling_nnmf_noinbetween1x1/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_nnmf_noinbetween1x1/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_conv2d/L1NormLayer.py b/basis_conv2d/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_conv2d/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, 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self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/basis_conv2d/append_block.py b/basis_conv2d/append_block.py new file mode 100644 index 0000000..80142d9 --- /dev/null +++ b/basis_conv2d/append_block.py @@ -0,0 +1,111 @@ +import torch +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + if last_layer is False: + + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_conv2d/append_parameter.py b/basis_conv2d/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_conv2d/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_conv2d/convert_log_to_numpy.py b/basis_conv2d/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/basis_conv2d/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_conv2d/data_loader.py b/basis_conv2d/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_conv2d/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/basis_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..383fa25674406d27617dba3c912841ff2cdaebf1 GIT binary patch literal 4064 zcmbW#OK4SB7zN<%qg6z#w2BrwIF#CJOAr$ui7}cTHNIkEQjPD&iRMPFCQ4E)D8e3C zp%2=DqTs|cDD9*rP?bJ48+0z{KoFIKgBq-e0}+b@t9Pw+QvRWLvia`XYybP4dr!7? zwzh8`o`e0L^>_8{JlNk|e)awGt^)(*m){TU?K)I` z>6tZi=T{{)A5|sClGpr9aR2`d{^G&Mjg@uyBJHp~`IR1z^~mq^WUQnApr>GMk*C9% z{2qMnPwr2!PWp>}A8V`p0en{e5FU34tDA{+to$+DC|ALs%d_BKc{Y4Tu7-b;YvA+p z9Qdw07jC+Y)y>2DO!<6xhVoi?om>a^$_wB(xs^H(CTRZeeGe--lW%IRw5tC5$K(?#Bbe1>v*milXuzpI??Q{IYvnsWMt z@;2mk%IU+(*CH?OPj@J9M?OG%`bV8d>%2(+tbPajU#L%?R-gWXj`X1VpO`-Vt@`v? z_33}qUx&Wz>F?F2PpVIUqyBpIuhX8spgvv9rw^&$iT*A1>3`Lyhv-QEr2Yo1s&+8>ce4lM9*!;`X=q^2kO)MeIl*< zx-ID6(|meZefo~((|^-$EAOHCwC)q=hw8(@>Hgc$zrozo#qS4)G29=S>wUQGm40|f z^I}*fr5Py_j}gnBPn%w5L6-`=s<5=D|3l+nGmt8J*KDwA+RGIzORX znR{CAE2Z^50=6 z_5NHp=Ii+qIOu+!`D*r4=5nBQUS=HGZ_|8}yK-fID4rj7I?pqgQ|3A^nEjb?WMAj! 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torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/basis_conv2d/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724159951.gp4u1.916226.0 b/basis_conv2d/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724159951.gp4u1.916226.0 new file mode 100644 index 0000000000000000000000000000000000000000..b8bf6d49390c4144f05299ae39a5245561d369f8 GIT binary patch literal 40534 zcmaLgbzBwM|37d*3=lCe0O=CApooPygP_hmU|=h#h=JHBpjfPdEnuzKidbu52clpg zqH7^0U}2$12$D+t?q_CyoOyrGPST0#4}|L{D@Oxjz`hcRs7Vm}I-lYrLi_|kJc#ER zM58L@$2N-^0OA##q!U}D3)z=EjA>G>B2$2)%LokfT+H$ex5-btd3%L 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zkA8zL3onE$6uZnNmG&8hT*EJS;S;_t7jb~0RK0NP?-pm%@PhlLvEz^>Vwd@(a^Czx&UyNG_*5!L7GNk<`{f61XZY-Fl`P6s zK5x+)K0E7l=e?vXAeGDA3%TVpLgBS~h%CTRs_r4r8rF8@V8QG!*PJ2RDJhkmDqLx%C@p7Q+fLJ)Z<{k9F|@yDT_$uDAk(V z%sRgquwO>Y0t}^*0}8uTd<7^E$)Zf9T~PEsplsh~<6mNzKS^b7NfEbouD=IR`pN9gL`3Vj%lzs{nf8D?XC_QCSx>D6;>BCMLKq+*8 zC@D)x torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_conv2d/make_network.py b/basis_conv2d/make_network.py new file mode 100644 index 0000000..768db46 --- /dev/null +++ b/basis_conv2d/make_network.py @@ -0,0 +1,168 @@ +import torch +from append_block import append_block +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1], + kernel_size=(2, 2), + dilation=(1, 1), + padding=(0, 0), + stride=(2, 2), + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_conv2d/make_optimize.py b/basis_conv2d/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_conv2d/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_conv2d/non_linear_weigth_function.py b/basis_conv2d/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_conv2d/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_conv2d/plot.py b/basis_conv2d/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_conv2d/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_conv2d/run_network.py b/basis_conv2d/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_conv2d/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_mlp/L1NormLayer.py b/basis_mlp/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_mlp/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_mlp/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt 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zDJ(W2G%SG*>Z^QjyL2WQ6hs7CAakPMn}Y^h!G%tlp||gIDaoA@Z3<@CNPSH`urn+w$OONDEL>nW|&}Uj@Tpewi-?w%j9>d9Gj&Y?|TJ=Xl zb81GQgr-dva}1A;@gHsfVgzlPnPcYCF^Ny-%p4vgZ()vcqhsRL>Y(8j;V^-BpX4SRyN}fEudpqvZ{vRG4gZFF+w`VzV(P)O8PS; zG+T3lF-B+DjK1l(X~;Yq(uNJ^B6Et4tjhQx$tB$SF49%vE;RKCkjM1qy zO%L=mOfYkdj;+yjG^gDI None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/basis_mlp/append_block.py b/basis_mlp/append_block.py new file mode 100644 index 0000000..b6796c4 --- /dev/null +++ b/basis_mlp/append_block.py @@ -0,0 +1,151 @@ +import torch +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool= False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + bias=False, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + if last_layer is False: + + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_mlp/append_parameter.py b/basis_mlp/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_mlp/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_mlp/convert_log_to_numpy.py b/basis_mlp/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/basis_mlp/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_mlp/data_loader.py b/basis_mlp/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_mlp/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git 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get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/basis_mlp/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724146550.gp4u2.724157.0 b/basis_mlp/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724146550.gp4u2.724157.0 new file mode 100644 index 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zdGuehl-NAD7$_sDK%o4nykD*?fk$96sFbf%W_fY6?_8kNf3dhNR^DWle#4Tbd$-$e z1j@ElAW$mD)e{`Lz`KwJP$^%j%<{y&TE~ELg#I)MD{ryNoT=$j(1=&?o#-eP2$ah2 z8iZ}`2CoY~L#0CH^wA!VfYLg}Pm7heS!G5>s$_QS%UCeW!BiknD!*jm_vRh^-Ty!; O7&b|Nb9T3LGu~ literal 0 HcmV?d00001 diff --git a/basis_mlp/loss_function.py b/basis_mlp/loss_function.py new file mode 100644 index 0000000..e256840 --- /dev/null +++ b/basis_mlp/loss_function.py @@ -0,0 +1,64 @@ +import torch + + +# loss_mode == 0: "normal" SbS loss function mixture +# loss_mode == 1: cross_entropy +def loss_function( + h: torch.Tensor, + labels: torch.Tensor, + loss_mode: int = 0, + number_of_output_neurons: int = 10, + loss_coeffs_mse: float = 0.0, + loss_coeffs_kldiv: float = 0.0, +) -> torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_mlp/make_network.py b/basis_mlp/make_network.py new file mode 100644 index 0000000..335bc3c --- /dev/null +++ b/basis_mlp/make_network.py @@ -0,0 +1,208 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer = block_id == len(number_of_output_channels)-1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, 2, 2)), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + .squeeze(0) + .squeeze(0) + ) + + network.append( + torch.nn.Unfold( + kernel_size=(2, 2), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1] // 4, + kernel_size=(1, 1), + bias=False, + ).to(torch_device) + ) + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_mlp/make_optimize.py b/basis_mlp/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_mlp/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_mlp/non_linear_weigth_function.py b/basis_mlp/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_mlp/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_mlp/plot.py b/basis_mlp/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_mlp/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_mlp/run_network.py b/basis_mlp/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_mlp/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_nnmf/L1NormLayer.py b/basis_nnmf/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_nnmf/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_nnmf/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/basis_nnmf/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 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+++ b/basis_nnmf/NNMF2d.py @@ -0,0 +1,252 @@ +import torch +from non_linear_weigth_function import non_linear_weigth_function + + +class NNMF2d(torch.nn.Module): + + in_channels: int + out_channels: int + weight: torch.Tensor + iterations: int + epsilon: float | None + init_min: float + init_max: float + beta: torch.Tensor | None + positive_function_type: int + local_learning: bool + local_learning_kl: bool + + def __init__( + self, + in_channels: int, + out_channels: int, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/basis_nnmf/append_block.py b/basis_nnmf/append_block.py new file mode 100644 index 0000000..a8de5d2 --- /dev/null +++ b/basis_nnmf/append_block.py @@ -0,0 +1,149 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_nnmf/append_parameter.py b/basis_nnmf/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_nnmf/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_nnmf/convert_log_to_numpy.py b/basis_nnmf/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/basis_nnmf/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_nnmf/data_loader.py b/basis_nnmf/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_nnmf/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/basis_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..c6366c25e5e06ee9162488559449cb9177a32198 GIT binary patch literal 6048 zcmbW%*^5_Y9LMn|=t5d(h>SMs5Ye=l{Zi91%_l9(a`|QMrJhM=(k%ToE|n5JM1R15 zBo!^EvelL`Y~RjBg_mvZ{IY~8$9Yu zrq1Y0DyCdIsd%LLpFbysfB!5w7{&29$DTunaM1J2_wfWhM?b)QdhY)apP}czkMLP~ z?*15`r|0u_A#ePI{6al{wl9*meoB6^oX{)2P;o%7q2fA1XsKb+%V zoG()Tt8;vAt>1^mdVcL3|L*=J%71e%r_8LBsutfHJ<7ImD>Mh?#DObgs;Ojp632ln&0;0 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ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/basis_nnmf/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724146431.gp4u1.872865.0 b/basis_nnmf/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724146431.gp4u1.872865.0 new 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"normal" SbS loss function mixture +# loss_mode == 1: cross_entropy +def loss_function( + h: torch.Tensor, + labels: torch.Tensor, + loss_mode: int = 0, + number_of_output_neurons: int = 10, + loss_coeffs_mse: float = 0.0, + loss_coeffs_kldiv: float = 0.0, +) -> torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_nnmf/make_network.py b/basis_nnmf/make_network.py new file mode 100644 index 0000000..071d9fb --- /dev/null +++ b/basis_nnmf/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, 2, 2)), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + .squeeze(0) + .squeeze(0) + ) + + network.append( + torch.nn.Unfold( + kernel_size=(2, 2), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2d( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1] // 4, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=False, + local_learning_kl=False, + ).to(torch_device) + ) + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_nnmf/make_optimize.py b/basis_nnmf/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_nnmf/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_nnmf/non_linear_weigth_function.py b/basis_nnmf/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_nnmf/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_nnmf/plot.py b/basis_nnmf/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_nnmf/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_nnmf/run_network.py b/basis_nnmf/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_nnmf/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_nnmf_autograd/L1NormLayer.py b/basis_nnmf_autograd/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_nnmf_autograd/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_nnmf_autograd/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/basis_nnmf_autograd/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..cb83d1310e5be3b6a1809ce0f62466e5cdfd6453 GIT binary patch literal 775732 zcmbT82V4_N*Z)C85fOU|1+6fFYn{?Jn#SWazCE^&Y591zjG#YCd=jy@-wM!WMpP$^v92v zk%Q5In2^xOzzB6H=i(F?$pt!hb;3WPU7TG1L6?8fHPA66NFCHQc2cBokG4jm{y%<_ 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b/basis_nnmf_autograd/NNMF2dAutograd.py new file mode 100644 index 0000000..3a72b72 --- /dev/null +++ b/basis_nnmf_autograd/NNMF2dAutograd.py @@ -0,0 +1,121 @@ +import torch +from non_linear_weigth_function import non_linear_weigth_function + + +class NNMF2dAutograd(torch.nn.Module): + + in_channels: int + out_channels: int + weight: torch.Tensor + iterations: int + epsilon: float | None + init_min: float + init_max: float + beta: torch.Tensor | None + positive_function_type: int + local_learning: bool + local_learning_kl: bool + + def __init__( + self, + in_channels: int, + out_channels: int, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + # --------------------- + + # Prepare h + h = torch.full( + (input.shape[0], self.out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(self.out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, self.iterations): + reconstruction = torch.nn.functional.linear(h, positive_weights.T) + reconstruction = reconstruction + 1e-20 + if self.epsilon is None: + h = h * torch.nn.functional.linear( + (input / reconstruction), positive_weights + ) + else: + h = h * ( + 1 + + self.epsilon + * torch.nn.functional.linear( + (input / reconstruction), positive_weights + ) + ) + h = h / (h.sum(-1, keepdim=True) + 10e-20) + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + assert torch.isfinite(h).all() + + return h diff --git a/basis_nnmf_autograd/append_block.py b/basis_nnmf_autograd/append_block.py new file mode 100644 index 0000000..697cffb --- /dev/null +++ b/basis_nnmf_autograd/append_block.py @@ -0,0 +1,149 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2dAutograd import NNMF2dAutograd +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dAutograd( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_nnmf_autograd/append_parameter.py b/basis_nnmf_autograd/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_nnmf_autograd/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_nnmf_autograd/convert_log_to_numpy.py b/basis_nnmf_autograd/convert_log_to_numpy.py new file mode 100644 index 0000000..6a1343a --- /dev/null +++ b/basis_nnmf_autograd/convert_log_to_numpy.py @@ -0,0 +1,30 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_nnmf_autograd/data_loader.py b/basis_nnmf_autograd/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_nnmf_autograd/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_nnmf_autograd/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/basis_nnmf_autograd/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..76d4ed3a6638ca14284894bbe9c99f4607a1492e GIT binary patch literal 5872 zcmbW%No-eD6bA64iIKQ!oTDyYh_Q$bRuCzJauGp5fj=lH$nXkn0hvk*I3RGGqT*CV 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torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/basis_nnmf_autograd/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724148624.gp4u2.729873.0 b/basis_nnmf_autograd/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724148624.gp4u2.729873.0 new file mode 100644 index 0000000000000000000000000000000000000000..272b3a1f39e67aeb0fc33f8906e78f6397b41679 GIT binary patch literal 59292 zcmaLgcRbhM|M-9FS!fRpN|cDq2G8m|+a5=yMN5mckR(K*Ek%^}9!h&kAsUK?_AaH; z(B37a@q4_^=l%0I_wTu!^ZN63Ik%qI%l+{jkM(?V)B3;v{w{ahWH2YA_Dkc`f^Nn& zd_%_g2id!Zj}Hp#*v7-&&^By*Q0SBp-zj54#@UBXn>=|;$PD|45dX08A@<|{Tjf2$ 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assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_nnmf_autograd/make_network.py b/basis_nnmf_autograd/make_network.py new file mode 100644 index 0000000..9fa1cf2 --- /dev/null +++ b/basis_nnmf_autograd/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2dAutograd import NNMF2dAutograd +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, 2, 2)), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + .squeeze(0) + .squeeze(0) + ) + + network.append( + torch.nn.Unfold( + kernel_size=(2, 2), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dAutograd( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1] // 4, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=False, + local_learning_kl=False, + ).to(torch_device) + ) + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_nnmf_autograd/make_optimize.py b/basis_nnmf_autograd/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_nnmf_autograd/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_nnmf_autograd/non_linear_weigth_function.py b/basis_nnmf_autograd/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_nnmf_autograd/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_nnmf_autograd/plot.py b/basis_nnmf_autograd/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_nnmf_autograd/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_nnmf_autograd/run_network.py b/basis_nnmf_autograd/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_nnmf_autograd/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_nnmf_convnnmf/L1NormLayer.py b/basis_nnmf_convnnmf/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_nnmf_convnnmf/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_nnmf_convnnmf/NNMF2dConvGroupedAutograd.py b/basis_nnmf_convnnmf/NNMF2dConvGroupedAutograd.py new file mode 100644 index 0000000..776da80 --- /dev/null +++ b/basis_nnmf_convnnmf/NNMF2dConvGroupedAutograd.py @@ -0,0 +1,208 @@ +import torch +from non_linear_weigth_function import non_linear_weigth_function + + +class NNMF2dConvGrouped(torch.nn.Module): + + in_channels: int + out_channels: int + weight: torch.Tensor + iterations: int + epsilon: float | None + init_min: float + init_max: float + beta: torch.Tensor | None + positive_function_type: int + convolution_contribution_map: None | torch.Tensor = None + convolution_contribution_map_enable: bool + convolution_ip_norm: bool + kernel_size: tuple[int, ...] + stride: tuple[int, ...] + padding: str | tuple[int, ...] + dilation: tuple[int, ...] + output_size: None | torch.Tensor = None + groups: int + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: tuple[int, int], + groups: int = 1, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + convolution_contribution_map_enable: bool = False, + stride: tuple[int, int] = (1, 1), + padding: str | tuple[int, int] = (0, 0), + dilation: tuple[int, int] = (1, 1), + convolution_ip_norm: bool = True, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + valid_padding_strings = {"same", "valid"} + if isinstance(padding, str): + if padding not in valid_padding_strings: + raise ValueError( + f"Invalid padding string {padding!r}, should be one of {valid_padding_strings}" + ) + if padding == "same" and any(s != 1 for s in stride): + raise ValueError( + "padding='same' is not supported for strided convolutions" + ) + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.groups = groups + assert ( + in_channels % self.groups == 0 + ), f"Can't divide without rest {in_channels} / {self.groups}" + self.in_channels = in_channels // self.groups + self.out_channels = out_channels + + self.iterations = iterations + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.convolution_contribution_map_enable = convolution_contribution_map_enable + self.convolution_ip_norm = convolution_ip_norm + + self.weight = torch.nn.parameter.Parameter( + torch.empty( + (out_channels, self.in_channels, *kernel_size), **factory_kwargs + ) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}," + s += f"kernel_size={self.kernel_size}," + s += f"stride={self.stride}, iterations={self.iterations}" + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", groups={self.groups}" + + if self.padding != (0,) * len(self.padding): + s += f", padding={self.padding}" + if self.dilation != (1,) * len(self.dilation): + s += f", dilation={self.dilation}" + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + if input.ndim == 2: + input = input.unsqueeze(-1) + if input.ndim == 3: + input = input.unsqueeze(-1) + + if self.output_size is None: + self.output_size = torch.tensor( + torch.nn.functional.conv2d( + torch.zeros( + 1, + input.shape[1], + input.shape[2], + input.shape[3], + device=self.weight.device, + dtype=self.weight.dtype, + ), + torch.zeros_like(self.weight), + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups, + ).shape, + requires_grad=False, + ) + assert self.output_size is not None + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + + positive_weights = positive_weights / ( + positive_weights.sum(dim=-1, keepdim=True) + 10e-20 + ) + + input = input / (input.sum((1, 2, 3), keepdim=True) + 10e-20) + + # Prepare h + self.output_size[0] = input.shape[0] + h = torch.full( + self.output_size.tolist(), + 1.0 / float(self.output_size[1]), + device=input.device, + dtype=input.dtype, + ) + + if self.convolution_ip_norm: + pass + else: + h = h / (h.sum((1, 2, 3), keepdim=True) + 10e-20) + + for _ in range(0, self.iterations): + + factor_x_div_r: torch.Tensor = input / ( + torch.nn.functional.conv_transpose2d( + h, + positive_weights, + stride=self.stride, + padding=self.padding, # type: ignore + dilation=self.dilation, + groups=self.groups, + ) + + 10e-20 + ) + + if self.epsilon is None: + h = h * torch.nn.functional.conv2d( + factor_x_div_r, + positive_weights, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups, + ) + else: + h = h * ( + 1 + + self.epsilon + * torch.nn.functional.conv2d( + factor_x_div_r, + positive_weights, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups, + ) + ) + if self.convolution_ip_norm: + h = h / (h.sum(1, keepdim=True) + 10e-20) + else: + h = h / (h.sum((1, 2, 3), keepdim=True) + 10e-20) + + assert torch.isfinite(h).all() + return h diff --git a/basis_nnmf_convnnmf/__pycache__/L1NormLayer.cpython-312.pyc 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epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + network.append( + NNMF2dConvGrouped( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + return test_image diff --git a/basis_nnmf_convnnmf/append_parameter.py b/basis_nnmf_convnnmf/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_nnmf_convnnmf/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_nnmf_convnnmf/convert_log_to_numpy.py b/basis_nnmf_convnnmf/convert_log_to_numpy.py new file mode 100644 index 0000000..6a1343a --- /dev/null +++ b/basis_nnmf_convnnmf/convert_log_to_numpy.py @@ -0,0 +1,30 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_nnmf_convnnmf/data_loader.py b/basis_nnmf_convnnmf/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_nnmf_convnnmf/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_nnmf_convnnmf/get_the_data.py b/basis_nnmf_convnnmf/get_the_data.py new file mode 100644 index 0000000..fc61064 --- /dev/null +++ b/basis_nnmf_convnnmf/get_the_data.py @@ -0,0 +1,147 @@ +import torch +import torchvision # type: ignore +from data_loader import data_loader + +from torchvision.transforms import v2 # type: ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + 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label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_nnmf_convnnmf/make_network.py b/basis_nnmf_convnnmf/make_network.py new file mode 100644 index 0000000..1e62358 --- /dev/null +++ b/basis_nnmf_convnnmf/make_network.py @@ -0,0 +1,171 @@ +import torch +from append_block import append_block +from NNMF2dConvGroupedAutograd import NNMF2dConvGrouped +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # network.append( + # NNMF2dConvGrouped( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1], + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # device=torch_device, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_nnmf_convnnmf/make_optimize.py b/basis_nnmf_convnnmf/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_nnmf_convnnmf/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_nnmf_convnnmf/non_linear_weigth_function.py b/basis_nnmf_convnnmf/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_nnmf_convnnmf/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_nnmf_convnnmf/plot.py b/basis_nnmf_convnnmf/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_nnmf_convnnmf/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_nnmf_convnnmf/run_network.py b/basis_nnmf_convnnmf/run_network.py new file mode 100644 index 0000000..48ab2a5 --- /dev/null +++ b/basis_nnmf_convnnmf/run_network.py @@ -0,0 +1,253 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.001, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 + batch_size_test: int = 50 + number_of_epoch: int = 500 + + print(batch_size_train, batch_size_test) + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_nnmf_groups1/L1NormLayer.py b/basis_nnmf_groups1/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_nnmf_groups1/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_nnmf_groups1/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/basis_nnmf_groups1/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 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out_channels: int, + groups: int = 1, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.groups = groups + assert ( + in_channels % self.groups == 0 + ), f"Can't divide without rest {in_channels} / {self.groups}" + self.in_channels = in_channels // self.groups + assert ( + out_channels % self.groups == 0 + ), f"Can't divide without rest {out_channels} / {self.groups}" + self.out_channels = out_channels // self.groups + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty( + (self.groups, self.out_channels, self.in_channels), **factory_kwargs + ) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d_grouped = FunctionalNNMF2dGrouped.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + s += f", groups={self.groups}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=-1, keepdim=True) + 10e-20 + ) + assert self.groups * self.in_channels == input.shape[1] + + input = input.reshape( + ( + input.shape[0], + self.groups, + self.in_channels, + input.shape[-2], + input.shape[-1], + ) + ) + input = input / (input.sum(dim=2, keepdim=True) + 10e-20) + + h_dyn = self.functional_nnmf2d_grouped( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + h_dyn = h_dyn.reshape( + ( + h_dyn.shape[0], + h_dyn.shape[1] * h_dyn.shape[2], + h_dyn.shape[3], + h_dyn.shape[4], + ) + ) + h_dyn = h_dyn / (h_dyn.sum(dim=1, keepdim=True) + 10e-20) + + return h_dyn + + +@torch.jit.script +def grouped_linear_einsum_h_weights(h, weights): + return torch.einsum("bgoxy,goi->bgixy", h, weights) + + +@torch.jit.script +def grouped_linear_einsum_reconstruction_weights(reconstruction, weights): + return torch.einsum("bgixy,goi->bgoxy", reconstruction, weights) + + +@torch.jit.script +def grouped_linear_einsum_h_input(h, reconstruction): + return torch.einsum("bgoxy,bgixy->goi", h, reconstruction) + + +class FunctionalNNMF2dGrouped(torch.autograd.Function): + + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + ( + input.shape[0], + input.shape[1], + out_channels, + input.shape[-2], + input.shape[-1], + ), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + for _ in range(0, iterations): + + reconstruction = grouped_linear_einsum_h_weights(h, weight) + reconstruction += 1e-20 + + if epsilon is None: + h *= grouped_linear_einsum_reconstruction_weights( + (input / reconstruction), weight + ) + else: + h *= 1 + epsilon * grouped_linear_einsum_reconstruction_weights( + (input / reconstruction), weight + ) + h /= h.sum(2, keepdim=True) + 10e-20 + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + big_r = grouped_linear_einsum_h_weights(h, weight) + + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + grouped_linear_einsum_h_weights(h * grad_output, weight) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -grouped_linear_einsum_h_input( + h, (factor_x_div_r * grad_input) + ) + + grad_weight += grouped_linear_einsum_h_input( + (h * grad_output), + factor_x_div_r, + ) + + else: + if ctx.local_learning_kl: + + grad_weight = -grouped_linear_einsum_h_input( + h, + factor_x_div_r, + ) + + else: + grad_weight = -grouped_linear_einsum_h_input( + h, + (2 * (input - big_r)), + ) + + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/basis_nnmf_groups1/append_block.py b/basis_nnmf_groups1/append_block.py new file mode 100644 index 0000000..b37aa0a --- /dev/null +++ b/basis_nnmf_groups1/append_block.py @@ -0,0 +1,151 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2dGrouped import NNMF2dGrouped +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + groups: int = 1, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dGrouped( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + groups=groups, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_nnmf_groups1/append_parameter.py b/basis_nnmf_groups1/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_nnmf_groups1/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_nnmf_groups1/convert_log_to_numpy.py b/basis_nnmf_groups1/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/basis_nnmf_groups1/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_nnmf_groups1/data_loader.py b/basis_nnmf_groups1/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_nnmf_groups1/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_nnmf_groups1/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/basis_nnmf_groups1/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..dd2d5752e7b8ace86359c277cc561db3daa0fcd7 GIT binary patch literal 7456 zcmbW%ORQa09mnxSoEQgsq8Jc~Ziw1~1rJEImeOt>EssJuJPNe7beHy)0(~5AOMw>S z@{u@_oM<8mqiqaWpB%JO6p$SUP9)}x9I4q(e8zZY>dboo-|tb^&^y`r+~5DdUVE?e z)ot7F80|POok*uv?O!^!Z+X@FYSo4V>sGC)RvkFHe0+KDkv&J3_b>hX|6BGRI<|D~ 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z^Qg$(-w=DhYwUTC@F~V?!i(|7*zYrA`zQ2!el7NXPqFW(qu=$;ivAui-aqwzzv)R-Zha>q-@WB*g~K{&mPD{?&~KTTd^#~b0r{tJ(>>&fBl_!I2;wA4HP6rW-K4!??L z_=mU;FUFr^$4^hh{yncjI5~brewOiuK{$CH754iYQty81qJC7kq2B&6_Iz{U3HE$i?0HP_V*lY} zJ|lO&9{WAa@weRnld*sA-yodKEAkh7|KyAP7xg3Roxj10`%k_1CzJdBCgiTCEj-1G z{nKy#jNJMABA=6cKhsky`%lν*D}=NlHTvGb3x<2B)|$Jp^R_I*$2H*d+Ee@gEA z?27&wx%EBy;`=9e{Pc9Zf5#hyldj0^9~OCCc!aHQ3Xj8!-=mD3Z$iCp3r`Do^gG{- zT=#|N)cgIWlkxto9~7<%4-40YM}?ch&{THqZ4-40Y vM}?ch tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git 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zs{{h2e6Q5_Zprh&7Vk%;LZw;Tsc^-5(Sp+*iSi<=YtbvThd0c`PWR3K35x#1HZ z-oPV@msBcLuD9Oz8q8&>=hiMnnZPQiPLi)KnY7>)P!8^+1OlbJ=JsvYJ9tOeG%6J; z?_0EjPqJusFBB8yC005APdEynWVsnAr&57H`Oy_eOc~x9Z1GSk6)FRl6u@<&f2LD= z5anf7IlIYnbno7hzCfw1tONpOFE?Df!hCn2^rBLs(zom6P_V_b%LaH7Wg@E#`X{e# zG#LW_Qf(`}f#rcfDX(T(w3rS5QmqG-3YDX_etHL#x;h>D6J-*sG_D0O zC9j$L>4&bJD6V8>M%abdmG)OsP{-VEul*ARcI6>VSI)lBVe@}-GwMnmwRH#$cw7k_ zHCXmu?JbHcZIHC$b>&iNv^;(_=V~0-m4`81xl#={4{6;~gA$fvxd;t-TQ;p^BrLq zURQcOzKFVyVy|0G7TA@?FkR{02VAM+TpontN)~s720X3|G;^;L75a_hN+~3*cwO1- zT+WXYmQU+L^T4h=j_FE`);61_axT=~nOOru10GjS298*-zxoWtmA*(?@w)QXEP)^t zSGFA2D+0Un1g0xr&Fi$WF=0f#zvn!X20X3=u2 torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_nnmf_groups1/make_network.py b/basis_nnmf_groups1/make_network.py new file mode 100644 index 0000000..16180fa --- /dev/null +++ b/basis_nnmf_groups1/make_network.py @@ -0,0 +1,216 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2dGrouped import NNMF2dGrouped +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, + groups: list[int] = [1, 1, 1, 1], + groups_pool: list[int] = [1, 1, 1, 1], +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + groups=groups[block_id], + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, 2, 2)), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + .squeeze(0) + .squeeze(0) + ) + + network.append( + torch.nn.Unfold( + kernel_size=(2, 2), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dGrouped( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1] // 4, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=False, + local_learning_kl=False, + groups=groups_pool[block_id], + ).to(torch_device) + ) + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_nnmf_groups1/make_optimize.py b/basis_nnmf_groups1/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_nnmf_groups1/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_nnmf_groups1/non_linear_weigth_function.py b/basis_nnmf_groups1/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_nnmf_groups1/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_nnmf_groups1/plot.py b/basis_nnmf_groups1/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_nnmf_groups1/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_nnmf_groups1/run_network.py b/basis_nnmf_groups1/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_nnmf_groups1/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/basis_nnmf_groups5/HAD_TO_CHANGE_THE_NUMBER_OF_NEURONS_TO_35_65_95 b/basis_nnmf_groups5/HAD_TO_CHANGE_THE_NUMBER_OF_NEURONS_TO_35_65_95 new file mode 100644 index 0000000..e69de29 diff --git a/basis_nnmf_groups5/L1NormLayer.py b/basis_nnmf_groups5/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/basis_nnmf_groups5/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/basis_nnmf_groups5/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/basis_nnmf_groups5/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..2b37269b0329d4e27f0b44d4a5635f49ab116267 GIT binary patch literal 231092 zcmbT82Ut_f*7pHHiXb8uL{t=1Y)DsHW^GhZ5s}!iB0WgWCa9>f1EQcJqGG{{Viyqs zA=wi|IrfUZ9DDD*e>0gJ-Xv#;e*;}DEdv9sKmK`W z*=YqNMnuMi#Ka*~#$u^SusQwhu+Z?>$)S8iND?0zB1ue)O_nxRpWQUr^k4c}UlVnoS#S&5 z7n2+kl@b^C`y6zPwCUjB#WWXru0J8{GWgdVgQ4oqB{dQx=yP8d3iwzlXI~59`3@?2loI$^Tr?Mm^4!O}Hk# zq#f1HPIkfC0k+)Ehh<^4rd9Awu^*?)m6e`<|bj$=_O$?=q+I) 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zRXf4T%<=Hiz(ttAY=v5jA~@ZHMUd~B3Kt*pgiPr>P~yK0R?o_Wxo`j3cDFx<_x-QN zFRXrzU+i7l2OM7xfPEU$P-S!zIOCNBJE;j!XqPSA+qDelJadD)lqSMojVr>} z1%-y#+m88v0X!4&pHahG#{(CP;v$AW?ca|$2LZ9)K-K>TeR|!B@StVEL0gxt=Z?TP zXt}+u&_52s(8R*h(8|ox*viDh+|_c-7BVXK4PoqolMqIy+CxX=D+HostW za>)O49KS8=SA>QyUlIOSpxKqW(xzep0zb|27a&iregAWyf1CxwzoH~(o?7_d!|hiT zA-ms3G5RaYjx`Gjzg$4zr*XaXr^keuzmM{_#t3DmFA(B#j(^&(FwR525A(Oy+}ozv z`mbSbfBJnGvePI zVu1a>hM7$&F0=HG_-+ht>IWq*t{6%_bs zzn+@REx(WQx4GuedLzHW+&n$%cP|^`zs)s&mbds7WGJz9f17Lm%=7parpN8~Vg5GP{CP|I*L&w?_uq#3Tc7=TL-$t{)8)U7 z^0zMg^S0@)C_XEG8|803_UFyXzn}#CHp<^7=Rdnoe?bZSZIr*w&3|@OennB=^xG(Z zo0|XZ4*ZHTVe@aJ{B36b^YZ`K3ueNW-$D5=N8_(IfIqL_9saZk6c9-KEtr4pS-3li zh|Kt}PoFqi@BiXV{O{lW*YkMEf4;EXI{F_kxc?LV`NAXmpFeYpNX{1${<+W>n8 None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.groups = groups + assert ( + in_channels % self.groups == 0 + ), f"Can't divide without rest {in_channels} / {self.groups}" + self.in_channels = in_channels // self.groups + assert ( + out_channels % self.groups == 0 + ), f"Can't divide without rest {out_channels} / {self.groups}" + self.out_channels = out_channels // self.groups + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty( + (self.groups, self.out_channels, self.in_channels), **factory_kwargs + ) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d_grouped = FunctionalNNMF2dGrouped.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + s += f", groups={self.groups}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=-1, keepdim=True) + 10e-20 + ) + assert self.groups * self.in_channels == input.shape[1] + + input = input.reshape( + ( + input.shape[0], + self.groups, + self.in_channels, + input.shape[-2], + input.shape[-1], + ) + ) + input = input / (input.sum(dim=2, keepdim=True) + 10e-20) + + h_dyn = self.functional_nnmf2d_grouped( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + h_dyn = h_dyn.reshape( + ( + h_dyn.shape[0], + h_dyn.shape[1] * h_dyn.shape[2], + h_dyn.shape[3], + h_dyn.shape[4], + ) + ) + h_dyn = h_dyn / (h_dyn.sum(dim=1, keepdim=True) + 10e-20) + + return h_dyn + + +@torch.jit.script +def grouped_linear_einsum_h_weights(h, weights): + return torch.einsum("bgoxy,goi->bgixy", h, weights) + + +@torch.jit.script +def grouped_linear_einsum_reconstruction_weights(reconstruction, weights): + return torch.einsum("bgixy,goi->bgoxy", reconstruction, weights) + + +@torch.jit.script +def grouped_linear_einsum_h_input(h, reconstruction): + return torch.einsum("bgoxy,bgixy->goi", h, reconstruction) + + +class FunctionalNNMF2dGrouped(torch.autograd.Function): + + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + ( + input.shape[0], + input.shape[1], + out_channels, + input.shape[-2], + input.shape[-1], + ), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + for _ in range(0, iterations): + + reconstruction = grouped_linear_einsum_h_weights(h, weight) + reconstruction += 1e-20 + + if epsilon is None: + h *= grouped_linear_einsum_reconstruction_weights( + (input / reconstruction), weight + ) + else: + h *= 1 + epsilon * grouped_linear_einsum_reconstruction_weights( + (input / reconstruction), weight + ) + h /= h.sum(2, keepdim=True) + 10e-20 + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + big_r = grouped_linear_einsum_h_weights(h, weight) + + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + grouped_linear_einsum_h_weights(h * grad_output, weight) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -grouped_linear_einsum_h_input( + h, (factor_x_div_r * grad_input) + ) + + grad_weight += grouped_linear_einsum_h_input( + (h * grad_output), + factor_x_div_r, + ) + + else: + if ctx.local_learning_kl: + + grad_weight = -grouped_linear_einsum_h_input( + h, + factor_x_div_r, + ) + + else: + grad_weight = -grouped_linear_einsum_h_input( + h, + (2 * (input - big_r)), + ) + + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/basis_nnmf_groups5/append_block.py b/basis_nnmf_groups5/append_block.py new file mode 100644 index 0000000..b37aa0a --- /dev/null +++ b/basis_nnmf_groups5/append_block.py @@ -0,0 +1,151 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2dGrouped import NNMF2dGrouped +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + groups: int = 1, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dGrouped( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + groups=groups, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/basis_nnmf_groups5/append_parameter.py b/basis_nnmf_groups5/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/basis_nnmf_groups5/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/basis_nnmf_groups5/convert_log_to_numpy.py b/basis_nnmf_groups5/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/basis_nnmf_groups5/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/basis_nnmf_groups5/data_loader.py b/basis_nnmf_groups5/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/basis_nnmf_groups5/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/basis_nnmf_groups5/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/basis_nnmf_groups5/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..64c6c9da185724375b62673e92714a6c03e2b6ff GIT binary patch literal 4544 zcmbW$Uuaf!9LMorS6Me1RCu9u2=UQ+khyGsY|EpyO|$7-&E=oD^K5&bZTV+&v%n(w zT?i2|2u6Yxa>}bNA{owXS)QVqAw$Kq8u+$zSi;u*yA?k_gh}09M>s-!1B$?@jm4(mUk-02b8y3 z-l81uQr>2{SB{@kzRvR3l;fky*IRx>Ii67dpyh{^j2G#8e3|=Y_=@ho+4}YB<6G6o1)SmK z*mYWerTTb@`go!GxKaIwt$&yLc!m1-cJ*8TK|ChxLtibs6MWq-&X4<>f?&~ zfiv7s?zUOKq(0uSKAyxGKBWG3>o=>9pHUw_t3E!W{toL;sE=P%ANQ$`-&eoK`bX8r zy1&O$>f^)e@3j7T^|7AU0!L z{0h$S*Eq*|K3B5+$H*gm3di^aPVgJp@3%j8*FHa!NBBJ)<1;wHI$w{g?;rFt{H^xm3)l_V{%>%EzrZoR zgcGdy@mS}Z;*YpK!}@>7@i}r=w*A$=Kdd~)dcFjIN8bni6o07wxcdJ11MRQa{$Fu~ z^?S!y*C#mFe*6bc@fDompKy-9$8ON}7f#!Za1qCNElzL~_P7J5cqh*A7M$a5?1pUb z8XVzv9OLyk!FnE#TgX$~i!-c!Iqo8N!?wQ_M|eAqaSu*#Gxk{Lkz$=khO7H=g}!^t z_HV-xF5wt&zzOcdKFCw7^UAQ!H^=&Y+%DU%`$c$%_6K=_hqOO%iigQFJc9GUZny2% 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seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/basis_nnmf_groups5/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724157361.gp4u4.959581.0 b/basis_nnmf_groups5/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724157361.gp4u4.959581.0 new file mode 100644 index 0000000000000000000000000000000000000000..de6426abe00eb49f4f2f1447ed888a5a6a0769b8 GIT binary patch literal 45514 zcmaLgd0b6f`1tXRMN%3x;T&^hXjWJxcB_oZl(9@Hr3@iM3L#{O5JHAaLNaHb$($iX zk~tbQM@7GL*Z2Nf>-nzlUiZ)YdOml*U(XpjXPr)V 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za$=k%YJ<8&0_9pJ5GeaA@%LtL;H%(+Oe$2q@4EngC*|DPZL5f~npdvaYK7dAPtF0# zTIXmWP##d?;#)o88JHGKDpa=YxUmk{rK0NKTA~cA&HYB_{d_CbO!4J5P|jomf%3Bg zzuoi(zH*CXQlYYG@_8?yJaqBG2BKWbD?55yp}mE3;WJSY69|;Q6!^r>m2j6jm*}yC z%2`t{ZUf4}+G(4Kav87uw95*$_1&}p>~bd)2$bat{B?usJl=mlLz=iQsgwW#ND=W5F&*_No_4 zl;ONG@U1m^+w71A*kvpe2$Z#zc=(s`H-PdKlM0n*9V7`Tdwy#lN0cjgWzUvYXmikq zuRyt!2?WX&O4ZLTzk|vcCKW1g{Or{jC>?Up0is;VD-#-7qa?4-H-Ivn2?R=GC9aOh zz5sR^%cMeOzx$qp%m6QeTPR-E?M;5m22C)Z3vXh8n--6lxujU?>Y;V<>Lq+ zJ@=SEpj4e@Ju;{cJp2ENNrlQWw@;=3l_ndVtAX<1--?Sw8ObYuUQ~VFlUnN;Q06m%KoUX&_KeR$^`aqaA^A8j}i@M%`?qz%ENYa_$o4241vJcmhzN<*#v@H;6< vU%$K}%FVnoP0tE#aGn torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/basis_nnmf_groups5/make_network.py b/basis_nnmf_groups5/make_network.py new file mode 100644 index 0000000..963d85a --- /dev/null +++ b/basis_nnmf_groups5/make_network.py @@ -0,0 +1,216 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2dGrouped import NNMF2dGrouped +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [35, 65, 95, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, + groups: list[int] = [5, 5, 5, 1], + groups_pool: list[int] = [5, 5, 1, 1], +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + groups=groups[block_id], + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, 2, 2)), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + .squeeze(0) + .squeeze(0) + ) + + network.append( + torch.nn.Unfold( + kernel_size=(2, 2), + stride=(2, 2), + padding=(0, 0), + dilation=(1, 1), + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2dGrouped( + in_channels=test_image.shape[1], + out_channels=test_image.shape[1] // 4, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=False, + local_learning_kl=False, + groups=groups_pool[block_id], + ).to(torch_device) + ) + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=0.1, + track_running_stats=False, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/basis_nnmf_groups5/make_optimize.py b/basis_nnmf_groups5/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/basis_nnmf_groups5/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/basis_nnmf_groups5/non_linear_weigth_function.py b/basis_nnmf_groups5/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/basis_nnmf_groups5/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/basis_nnmf_groups5/plot.py b/basis_nnmf_groups5/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/basis_nnmf_groups5/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/basis_nnmf_groups5/run_network.py b/basis_nnmf_groups5/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/basis_nnmf_groups5/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/max_pooling_mlp/L1NormLayer.py b/max_pooling_mlp/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/max_pooling_mlp/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/max_pooling_mlp/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/max_pooling_mlp/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..c826147c2fae74b74328babc89d44e420ad458eb GIT binary patch literal 687868 zcmbT830zIx_x~HEM3bRJ5|SttQt6((k0F%8&5$vvxY2Y^10qwdG?-E{hK!jhq`GJC zYYGvOd7kHa3gN%^-S?sI*Yo?oey^YB)jIFB_UWAaS!O7j*1COhzyT&4vmS94~d8l7mNbKw?ULt`Q%!$adEVxr@W1`1)}LU>s3h|qW= zxqb(!yq@|1!Q&GqOrU!k$Y`*BkTpF@bWC(`EQvjBX6LP8%yO4gDmK8j}Hl*JS9X33yu}SgJm%>ljG!#)JHc7GWm~jHfE|G zGYe`;$0Fi_CnQ8h{y7HSM{XVv)QFBwh!NBWX1{2m{>8>Y&How-o)Qr+3#K1pVw}8* zdbg%QE&kpuByvi~w7B5V5R#`bc{BBnmO$mU;%|KpU_>Rnp|nbBS2 z;zQ!YgTqMTHOXkH9<>Q-@?WEWPSr}ihb>bU850s#_aJ|EYpveRj@9h1-D2YYIbj?1 zer;L7HSHyDrw-aX1Zk6x;UaJUcLRoy)=3PZ?PDYjA28HN?m&94L$ch_L8hA~(^LO` 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znc)8?DF1B={(V^fyH(}DUC#frDeV3enE!0=Ep!wY*Z=RI))eJTrT^Yk{`Ooh^ None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/max_pooling_nnmf/append_block.py b/max_pooling_nnmf/append_block.py new file mode 100644 index 0000000..a8de5d2 --- /dev/null +++ b/max_pooling_nnmf/append_block.py @@ -0,0 +1,149 @@ +import torch +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + NNMF2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + local_learning=local_learning, + local_learning_kl=local_learning_kl, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/max_pooling_nnmf/append_parameter.py b/max_pooling_nnmf/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/max_pooling_nnmf/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/max_pooling_nnmf/convert_log_to_numpy.py b/max_pooling_nnmf/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/max_pooling_nnmf/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/max_pooling_nnmf/data_loader.py b/max_pooling_nnmf/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/max_pooling_nnmf/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/max_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy 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z>UEwSe1N>dx?h)mJ&*8~?O*F{;4SoLSnppGYrVO-eqJrCd4+SEem(CeJY~>%6<&>vvqv`V86|;aoD1i+b95eeE}bWwlXJ(pa_%~ZQ@#Dy`)xR9 k&Q0gsx#e6qx1FQ&xO3?|;hda1&Xsf5Ih=O;ch3IxKbOF`M*si- literal 0 HcmV?d00001 diff --git a/max_pooling_nnmf/get_the_data.py b/max_pooling_nnmf/get_the_data.py new file mode 100644 index 0000000..fc61064 --- /dev/null +++ b/max_pooling_nnmf/get_the_data.py @@ -0,0 +1,147 @@ +import torch +import torchvision # type: ignore +from data_loader import data_loader + +from torchvision.transforms import v2 # type: ignore +import numpy as np + + +def get_the_data( + dataset: str, + batch_size_train: int, + batch_size_test: int, + torch_device: torch.device, + input_dim_x: int, + input_dim_y: int, + flip_p: float = 0.5, + jitter_brightness: float = 0.5, + jitter_contrast: float = 0.1, + jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/max_pooling_nnmf/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724147482.gp4u4.933902.0 b/max_pooling_nnmf/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724147482.gp4u4.933902.0 new file mode 100644 index 0000000000000000000000000000000000000000..3d5b0453337a48e9355e238838d8315329c41cdd GIT binary patch literal 61284 zcmaLgcU;cj|M>B)WR#Q@Nr=!+y0TxFeZ5X0GbGt1J7i?9vRATai^|H1>=9WZl95Ux z%E+!%>i7Db@8{>7=l6Wi`TY64oyYsSf1j_e@p@h7b#c}CpZ}F_aowkX{L;Ck1}9&3 zGpjLn=9r1o9JOu>3jd-p&8UE4YawG0eA{`23q=;I#{V4`!q zPG_^J6C$nuySid}yheIyV#KDSM2^ zSpB!V_~xXn-~9h~<_2a4bv%3nW?B62tbKwu`P98C7XQs`zQp#|H#~GY`9HXtDu$=K 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b/max_pooling_nnmf/loss_function.py new file mode 100644 index 0000000..e256840 --- /dev/null +++ b/max_pooling_nnmf/loss_function.py @@ -0,0 +1,64 @@ +import torch + + +# loss_mode == 0: "normal" SbS loss function mixture +# loss_mode == 1: cross_entropy +def loss_function( + h: torch.Tensor, + labels: torch.Tensor, + loss_mode: int = 0, + number_of_output_neurons: int = 10, + loss_coeffs_mse: float = 0.0, + loss_coeffs_kldiv: float = 0.0, +) -> torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/max_pooling_nnmf/make_network.py b/max_pooling_nnmf/make_network.py new file mode 100644 index 0000000..1fc1bc5 --- /dev/null +++ b/max_pooling_nnmf/make_network.py @@ -0,0 +1,215 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from NNMF2d import NNMF2d +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32, 64, 96, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + network.append(torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # NNMF2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # epsilon=epsilon, + # positive_function_type=positive_function_type, + # beta=beta, + # iterations=iterations, + # local_learning=False, + # local_learning_kl=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/max_pooling_nnmf/make_optimize.py b/max_pooling_nnmf/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/max_pooling_nnmf/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/max_pooling_nnmf/non_linear_weigth_function.py b/max_pooling_nnmf/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/max_pooling_nnmf/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/max_pooling_nnmf/plot.py b/max_pooling_nnmf/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/max_pooling_nnmf/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/max_pooling_nnmf/run_network.py b/max_pooling_nnmf/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/max_pooling_nnmf/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/plot.py b/plot.py new file mode 100644 index 0000000..1425974 --- /dev/null +++ b/plot.py @@ -0,0 +1,92 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("./basis_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy") +plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k", label="basis nnmf") + +data = np.load("./basis_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy") +plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k--", label="basis mlp") + +data = np.load( + "./basis_nnmf_autograd/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k:", label="basis nnmf autograd" +) + +data = np.load("./basis_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy") + +plt.loglog( + data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), "k-.", label="basis conv2d" +) + + +# ---- + +data = np.load( + "./max_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="nnmf max pooling") + +data = np.load( + "./avg_pooling_nnmf/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="nnmf average pooling" +) + +data = np.load( + "./avg_pooling_nnmf_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + label="nnmf average noinbetween1x1", +) +# ---- +data = np.load( + "./avg_pooling_conv2d/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + label="conv2d average pooling (breaks during learning)", +) + +data = np.load( + "./avg_pooling_conv2d_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + label="conv2d average noinbetween1x1", +) + +# ---- + +data = np.load( + "./max_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog(data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="mlp max pooling") + +data = np.load( + "./avg_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], 100.0 * (1.0 - data[:, 1] / 10000.0), label="mlp average pooling" +) + +data = np.load( + "./avg_pooling_mlp_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy" +) +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + label="mlp average noinbetween1x1", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show()

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zhb4v)y8()oB#vv)p&iJ9Cs0;iZ7C(zr+!MFi9 zo3WPB#^}VETT1ICvm)=O&+ZXBDSev zM6NB2JUkFZN_rBAQ^ii=39HGwT)yvQ(r)s)oF9w8q>@c(X2z0eg`Y@JWh%+cjUp$yDoJBwBpJWilN5Uf z6Z_Y}WPr{OME-Un8S4~8e$-k=cCAe#Hs`mI@wyAh2aT2F&FOIRAbtyZoUns#UqZ1*^O41gmgfo_6b0u|sm2*+EC$ z=zecx+v|K;?dy{C^nDq+Z`us@McY$WUgyHm&68SJFykxuPN6}YWVXREp(yG>!U)kcHUbE|(lh_k=W7tDuud)rPt7(@? z0)>QBR^!ZVwqJ7-D_1&>8jf#bMfNxG|M2v`+uvHCCWik9`K;gIwjTU(l%nj23v zHaD9%(aOx!)XK!t*vi_%(%Q<@f>&b;lZmD#rY53N3TmT&7{`}Tq|S~KqQd_FnWE{@ zLd9nW0)a>;iN5G1;+;2))~@pJ%ZanSrzXwsTcneFF+vRmVpzv}bD+r&p|29S;OMHxJU!&cqulM{mCM{chj9FjI zj#=NG`OBT9`_@g4_!#rPn4>?OC4w}*;(6^;H#yQ`w42ZFY%;Mgrp}XeC22xw%e)*6nF%$ zeJHkKQ@--Ew8z<+qT>*0Z;HY!u_<47`2PdNS!~KzPWEM3 z+{a>b#HM`ZV_yb;y(wwy None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/max_pooling_mlp/append_block.py b/max_pooling_mlp/append_block.py new file mode 100644 index 0000000..b6796c4 --- /dev/null +++ b/max_pooling_mlp/append_block.py @@ -0,0 +1,151 @@ +import torch +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool= False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + bias=False, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + if last_layer is False: + + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/max_pooling_mlp/append_parameter.py b/max_pooling_mlp/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/max_pooling_mlp/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/max_pooling_mlp/convert_log_to_numpy.py b/max_pooling_mlp/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/max_pooling_mlp/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/max_pooling_mlp/data_loader.py b/max_pooling_mlp/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/max_pooling_mlp/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/max_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/max_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..d5981ee6d00735ba4ff0f92ec1a41ac963ad73a0 GIT binary patch literal 5904 zcmbW&X^2&29LMn|Du{Z~6^eE^ggIjjaz}GFeX`VYy*VzanP+llG`G3Vs1cGKiY(f= zW~)XW+l*+r+G3j8u~LFah6pQYIks=crEGa4rTxFZTRbnDH;2#sp5OC7=Q(F|{p9JB zO0$l0tK8~_w)XD%T@Aych7lb@8k(Ypj?S(XU9HRJb#}G2AN&2-)+OETM}ObFu(jNN 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if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/max_pooling_mlp/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724147264.gp4u3.1004033.0 b/max_pooling_mlp/log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_/events.out.tfevents.1724147264.gp4u3.1004033.0 new file mode 100644 index 0000000000000000000000000000000000000000..33cbeaa20f6a1749bd4bb905390f3b3ba0b6816e GIT binary patch literal 59624 zcmaLgby!qQ`@nH*Kxs(<=~}S^b&p~k2E{pmN&cJWkG{a3ZcGgc$*eftR0GcUTCY54|@pFE?v zIL~iJP^Z=dn`<@;@|!VdW}xrP@qrVY&zU=Y`uMF)B-yEHAt@xR@b4`)0yuKs^# zu4$%eFvxFCkk$WY-p53%y6x^;{Wo*|ebif{-OA=${)5x4Me*L>yC>Wmvgf7Cf7hSd zu(?oU5nj&$M!fS_9J40f`M+g{>al7X5x8U%P13{>TAu3w@%+e26|pu&j97nKNNV=t zCm{a9>p8>)#U6hWvjziVtx+|P#3m*@5uZQU83Bmnc0AZBPq7X~R5myuXt-#{1LAJH 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z*?M66SXudkR!;l*Pr2^r^6@}9l?XV>W(xG7I9vxPLy45FlxCT6^oTi7j_hBqQof{> zJ%V$U+XucH0F>K^fTNU-$G!5?;1)khq+Df@;tzaYcItn9w>-;Nw6bYVzEW>&msp?- zBm$0dfdW+ppRfe83?ovuQu>6vUp*dv_9S;%wMSOIrj?C^d}Z%nt>L-gA|l`@rMvOW zUjBgJP-xT@KNedlZSnIK!Tw;2cWd2yudIASEA^ziODuA3!L$F1iGZV&u9i+6W@-m! zxrs>GO6k=zXwjbaP-(5RPgcIAl~Z2+QBFpulED^VLIfP8bY6YB=>_F!4_}zwCzDznL{fN`4lQA=l+3z8D|O+ zaFo*B%WGT{;qiDVk#d#ibSswv>DrR(Drt@;CH6L%bNlou4J>x}LtV3sY3l&j2IUFSGZx_0b+SXO?a zl|JMDDMxNdi~-7tM8Hu>pFL;WzMl$|i;0x0Z2kS torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/max_pooling_mlp/make_network.py b/max_pooling_mlp/make_network.py new file mode 100644 index 0000000..d23f4a4 --- /dev/null +++ b/max_pooling_mlp/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + + network.append(torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Conv2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # kernel_size=(1, 1), + # bias=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/max_pooling_mlp/make_optimize.py b/max_pooling_mlp/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/max_pooling_mlp/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/max_pooling_mlp/non_linear_weigth_function.py b/max_pooling_mlp/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/max_pooling_mlp/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/max_pooling_mlp/plot.py b/max_pooling_mlp/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/max_pooling_mlp/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/max_pooling_mlp/run_network.py b/max_pooling_mlp/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/max_pooling_mlp/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/max_pooling_nnmf/L1NormLayer.py b/max_pooling_nnmf/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/max_pooling_nnmf/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git 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positive_function_type: int + local_learning: bool + local_learning_kl: bool + + def __init__( + self, + in_channels: int, + out_channels: int, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_mlp_noinbetween1x1/append_block.py b/avg_pooling_mlp_noinbetween1x1/append_block.py new file mode 100644 index 0000000..ebcd39e --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/append_block.py @@ -0,0 +1,105 @@ +import torch +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + bias=False, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_mlp_noinbetween1x1/append_parameter.py b/avg_pooling_mlp_noinbetween1x1/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_mlp_noinbetween1x1/convert_log_to_numpy.py b/avg_pooling_mlp_noinbetween1x1/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_mlp_noinbetween1x1/data_loader.py b/avg_pooling_mlp_noinbetween1x1/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/avg_pooling_mlp_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy b/avg_pooling_mlp_noinbetween1x1/data_log_iter20_lr_-_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..89f4213211011bf0c0e2b1e8963da6c86a776d94 GIT binary patch literal 3856 zcmbW#U5Hgx7{>86foTqv7ZxT4o6wHMDQm{eubQPbGir{FnNwq>`#5tZE7O>XT57L=H(5W z`}zml!eimd+HiAhXtcIGtF0VaTB~QZk=>(vMjN}f?H(O&p8I=GW9L}&?BB$M&U)5yRXuwu`=7s=;lICi6VW);_3t`x2nSogKa6MC`o$4E%hpSd;@P%-D$gt}Li{zksS{Fgl6Jogh@!D<+}NO`C9X&e4Y7}e7(8-xUHeX z)_HQL`C@sod69gBxm#XhZpt^BAD5S!cgf4l2ju1E33-KiTwZBzKVfUQ$=0ux-)!D5 zcbTVYe>Bg=34f>kt1SNtC)}p~YRjjqk1tbxi{($NkL${BwR}oBUZA|&^3%%k1Tk0C^hsMI{!xAWv--H5{ozjQov%LrO?~{6`uIHcH(UQh@`!&|AGf|A z|4AMOtUp)t@r5|yUo{_3sehOCFH#?OtB-5A#BDg-ZT;5!;a_xryhMGxR{cTi>->oS zP#>Ss{qd#hZ?V1{@dEOMTl1a6J=WLvM7%SO&sg!`;N zOMQGfPPl66j>(_C_8BTaHE^$Mx5a-IuCc@9IwF% 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+ jitter_saturation: float = 0.1, + jitter_hue: float = 0.15, + da_auto_mode: bool = False, +) -> tuple[ + torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + torchvision.transforms.RandomCrop((input_dim_x, input_dim_y)), + torchvision.transforms.RandomHorizontalFlip(p=flip_p), + torchvision.transforms.ColorJitter( + brightness=jitter_brightness, + contrast=jitter_contrast, + saturation=jitter_saturation, + hue=jitter_hue, + ), + ], + ) + + return ( + train_dataloader, + test_dataloader, + train_processing_chain, + test_processing_chain, + ) diff --git a/avg_pooling_mlp_noinbetween1x1/log_iter20_lr_-_1.0000e-02_1.0000e-03_/events.out.tfevents.1724149524.gp4u5.923398.0 b/avg_pooling_mlp_noinbetween1x1/log_iter20_lr_-_1.0000e-02_1.0000e-03_/events.out.tfevents.1724149524.gp4u5.923398.0 new file mode 100644 index 0000000000000000000000000000000000000000..8c5d7aa6392782dc0dd8096311c5df358f2e9613 GIT binary patch literal 38376 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zC|we8l)q(o>zr`-nW#*Xa+N6=ak=1#_iy4lTT~X&%00*f>3ofT2X+}G2{_6nGQ76% zv@UpFZj+>JrSd0{|1p^^P~Obfog*rXY30%t?r7(MCVhcYy?_K9&3~lWXlr1GG zTd90rjyW>@J5c8ByRA}|(8}*#3Y6Wv3H%+t7W+xSQ7W%W#r;~c5GZXVDOcId%{KJ^ E0pBP+qyPW_ literal 0 HcmV?d00001 diff --git a/avg_pooling_mlp_noinbetween1x1/loss_function.py b/avg_pooling_mlp_noinbetween1x1/loss_function.py new file mode 100644 index 0000000..e256840 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/loss_function.py @@ -0,0 +1,64 @@ +import torch + + +# loss_mode == 0: "normal" SbS loss function mixture +# loss_mode == 1: cross_entropy +def loss_function( + h: torch.Tensor, + labels: torch.Tensor, + loss_mode: int = 0, + number_of_output_neurons: int = 10, + loss_coeffs_mse: float = 0.0, + loss_coeffs_kldiv: float = 0.0, +) -> torch.Tensor | None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_mlp_noinbetween1x1/make_network.py b/avg_pooling_mlp_noinbetween1x1/make_network.py new file mode 100644 index 0000000..464d9b9 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Conv2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # kernel_size=(1, 1), + # bias=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_mlp_noinbetween1x1/make_optimize.py b/avg_pooling_mlp_noinbetween1x1/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_mlp_noinbetween1x1/non_linear_weigth_function.py b/avg_pooling_mlp_noinbetween1x1/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_mlp_noinbetween1x1/plot.py b/avg_pooling_mlp_noinbetween1x1/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_mlp_noinbetween1x1/run_network.py b/avg_pooling_mlp_noinbetween1x1/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/avg_pooling_nnmf/L1NormLayer.py b/avg_pooling_nnmf/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_nnmf/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/avg_pooling_nnmf/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt b/avg_pooling_nnmf/Model_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..fa4e10dffbdf8a634fa356bbc89486ab5ace2118 GIT binary patch literal 688842 zcmbTe2UHYGx4%siB#1~7K@m|D6eTGlOjlPKKtxan1DKHLprmOAGm0ol1#?0%V~&^z z(*ZGJ02OmY5p&J~{i>!%5AW+e_y4WSS?+#nS54RacI~bJz0TkOZ5<7brcE{e`tjCq 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b/avg_pooling_mlp/NNMF2d.py new file mode 100644 index 0000000..b84d083 --- /dev/null +++ b/avg_pooling_mlp/NNMF2d.py @@ -0,0 +1,252 @@ +import torch +from non_linear_weigth_function import non_linear_weigth_function + + +class NNMF2d(torch.nn.Module): + + in_channels: int + out_channels: int + weight: torch.Tensor + iterations: int + epsilon: float | None + init_min: float + init_max: float + beta: torch.Tensor | None + positive_function_type: int + local_learning: bool + local_learning_kl: bool + + def __init__( + self, + in_channels: int, + out_channels: int, + device=None, + dtype=None, + iterations: int = 20, + epsilon: float | None = None, + init_min: float = 0.0, + init_max: float = 1.0, + beta: float | None = None, + positive_function_type: int = 0, + local_learning: bool = False, + local_learning_kl: bool = False, + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + + super().__init__() + + self.positive_function_type = positive_function_type + self.init_min = init_min + self.init_max = init_max + + self.in_channels = in_channels + self.out_channels = out_channels + + self.iterations = iterations + self.local_learning = local_learning + self.local_learning_kl = local_learning_kl + + self.weight = torch.nn.parameter.Parameter( + torch.empty((out_channels, in_channels), **factory_kwargs) + ) + + if beta is not None: + self.beta = torch.nn.parameter.Parameter(torch.empty((1), **factory_kwargs)) + self.beta.data[0] = beta + else: + self.beta = None + + self.reset_parameters() + self.functional_nnmf2d = FunctionalNNMF2d.apply + + self.epsilon = epsilon + + def extra_repr(self) -> str: + s: str = f"{self.in_channels}, {self.out_channels}" + + if self.epsilon is not None: + s += f", epsilon={self.epsilon}" + s += f", pfunctype={self.positive_function_type}" + s += f", local_learning={self.local_learning}" + + if self.local_learning: + s += f", local_learning_kl={self.local_learning_kl}" + + return s + + def reset_parameters(self) -> None: + torch.nn.init.uniform_(self.weight, a=self.init_min, b=self.init_max) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + + positive_weights = non_linear_weigth_function( + self.weight, self.beta, self.positive_function_type + ) + positive_weights = positive_weights / ( + positive_weights.sum(dim=1, keepdim=True) + 10e-20 + ) + + h_dyn = self.functional_nnmf2d( + input, + positive_weights, + self.out_channels, + self.iterations, + self.epsilon, + self.local_learning, + self.local_learning_kl, + ) + + return h_dyn + + +class FunctionalNNMF2d(torch.autograd.Function): + @staticmethod + def forward( # type: ignore + ctx, + input: torch.Tensor, + weight: torch.Tensor, + out_channels: int, + iterations: int, + epsilon: float | None, + local_learning: bool, + local_learning_kl: bool, + ) -> torch.Tensor: + + # Prepare h + h = torch.full( + (input.shape[0], out_channels, input.shape[-2], input.shape[-1]), + 1.0 / float(out_channels), + device=input.device, + dtype=input.dtype, + ) + + h = h.movedim(1, -1) + input = input.movedim(1, -1) + for _ in range(0, iterations): + reconstruction = torch.nn.functional.linear(h, weight.T) + reconstruction += 1e-20 + if epsilon is None: + h *= torch.nn.functional.linear((input / reconstruction), weight) + else: + h *= 1 + epsilon * torch.nn.functional.linear( + (input / reconstruction), weight + ) + h /= h.sum(-1, keepdim=True) + 10e-20 + h = h.movedim(-1, 1) + input = input.movedim(-1, 1) + + # ########################################################### + # Save the necessary data for the backward pass + # ########################################################### + ctx.save_for_backward(input, weight, h) + ctx.local_learning = local_learning + ctx.local_learning_kl = local_learning_kl + + assert torch.isfinite(h).all() + return h + + @staticmethod + @torch.autograd.function.once_differentiable + def backward(ctx, grad_output: torch.Tensor) -> tuple[ # type: ignore + torch.Tensor, + torch.Tensor | None, + None, + None, + None, + None, + None, + ]: + + # ############################################## + # Default values + # ############################################## + grad_weight: torch.Tensor | None = None + + # ############################################## + # Get the variables back + # ############################################## + (input, weight, h) = ctx.saved_tensors + + # The back prop gradient + h = h.movedim(1, -1) + grad_output = grad_output.movedim(1, -1) + input = input.movedim(1, -1) + big_r = torch.nn.functional.linear(h, weight.T) + big_r_div = 1.0 / (big_r + 1e-20) + + factor_x_div_r = input * big_r_div + + grad_input: torch.Tensor = ( + torch.nn.functional.linear(h * grad_output, weight.T) * big_r_div + ) + + del big_r_div + + # The weight gradient + if ctx.local_learning is False: + del big_r + + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (factor_x_div_r * grad_input) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + + grad_weight += torch.nn.functional.linear( + (h * grad_output) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ) + .T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + + else: + if ctx.local_learning_kl: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + factor_x_div_r.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ).T, + ) + else: + grad_weight = -torch.nn.functional.linear( + h.reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + h.shape[3], + ).T, + (2 * (input - big_r)) + .reshape( + grad_input.shape[0] * grad_input.shape[1] * grad_input.shape[2], + grad_input.shape[3], + ) + .T, + ) + grad_input = grad_input.movedim(-1, 1) + assert torch.isfinite(grad_input).all() + assert torch.isfinite(grad_weight).all() + + return ( + grad_input, + grad_weight, + None, + None, + None, + None, + None, + ) diff --git a/avg_pooling_mlp/append_block.py b/avg_pooling_mlp/append_block.py new file mode 100644 index 0000000..b1f7d97 --- /dev/null +++ b/avg_pooling_mlp/append_block.py @@ -0,0 +1,150 @@ +import torch +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def append_block( + network: torch.nn.Sequential, + out_channels: int, + test_image: torch.Tensor, + parameter_cnn_top: list[torch.nn.parameter.Parameter], + parameter_nnmf: list[torch.nn.parameter.Parameter], + parameter_norm: list[torch.nn.parameter.Parameter], + torch_device: torch.device, + dilation: tuple[int, int] | int = 1, + padding: tuple[int, int] | int = 0, + stride: tuple[int, int] | int = 1, + kernel_size: tuple[int, int] = (5, 5), + epsilon: float | None = None, + positive_function_type: int = 0, + beta: float | None = None, + iterations: int = 20, + local_learning: bool = False, + local_learning_kl: bool = False, + momentum: float = 0.1, + track_running_stats: bool = False, + last_layer: bool = False, +) -> torch.Tensor: + + kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]] + + if kernel_size[0] < 1: + kernel_size_internal[0] = test_image.shape[-2] + + if kernel_size[1] < 1: + kernel_size_internal[1] = test_image.shape[-1] + + # Main + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + # I need the output size + mock_output = ( + torch.nn.functional.conv2d( + torch.zeros( + 1, + 1, + test_image.shape[2], + test_image.shape[3], + ), + torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])), + stride=stride, + padding=padding, + dilation=dilation, + ) + .squeeze(0) + .squeeze(0) + ) + network.append( + torch.nn.Unfold( + kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]), + dilation=dilation, + padding=padding, + stride=stride, + ) + ) + test_image = network[-1](test_image) + + network.append( + torch.nn.Fold( + output_size=mock_output.shape, + kernel_size=(1, 1), + dilation=1, + padding=0, + stride=1, + ) + ) + test_image = network[-1](test_image) + + network.append(L1NormLayer()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + bias=False, + ).to(torch_device) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + momentum=momentum, + track_running_stats=track_running_stats, + device=torch_device, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + if last_layer is False: + + network.append(torch.nn.ReLU()) + test_image = network[-1](test_image) + + network.append( + torch.nn.Conv2d( + in_channels=test_image.shape[1], + out_channels=out_channels, + kernel_size=(1, 1), + stride=(1, 1), + padding=(0, 0), + bias=True, + device=torch_device, + ) + ) + # Init the cnn top layers 1x1 conv2d layers + for name, param in network[-1].named_parameters(): + with torch.no_grad(): + if name == "bias": + param.data *= 0 + if name == "weight": + assert param.shape[-2] == 1 + assert param.shape[-1] == 1 + param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye( + param.shape[0], dtype=param.dtype, device=param.device + ) + param[param.shape[0] :, :, 0, 0] = 0 + param[:, param.shape[0] :, 0, 0] = 0 + + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_cnn_top) + + if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1): + network.append( + torch.nn.BatchNorm2d( + num_features=test_image.shape[1], + device=torch_device, + momentum=momentum, + track_running_stats=track_running_stats, + ) + ) + test_image = network[-1](test_image) + append_parameter(module=network[-1], parameter_list=parameter_norm) + + return test_image diff --git a/avg_pooling_mlp/append_parameter.py b/avg_pooling_mlp/append_parameter.py new file mode 100644 index 0000000..b972e39 --- /dev/null +++ b/avg_pooling_mlp/append_parameter.py @@ -0,0 +1,8 @@ +import torch + + +def append_parameter( + module: torch.nn.Module, parameter_list: list[torch.nn.parameter.Parameter] +): + for netp in module.parameters(): + parameter_list.append(netp) diff --git a/avg_pooling_mlp/convert_log_to_numpy.py b/avg_pooling_mlp/convert_log_to_numpy.py new file mode 100644 index 0000000..05a5427 --- /dev/null +++ b/avg_pooling_mlp/convert_log_to_numpy.py @@ -0,0 +1,31 @@ +import os +import glob + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +from tensorboard.backend.event_processing import event_accumulator # type: ignore +import numpy as np + + +def get_data(path: str = "log_cnn"): + acc = event_accumulator.EventAccumulator(path) + acc.Reload() + + which_scalar = "Test Number Correct" + te = acc.Scalars(which_scalar) + + np_temp = np.zeros((len(te), 2)) + + for id in range(0, len(te)): + np_temp[id, 0] = te[id].step + np_temp[id, 1] = te[id].value + + print(np_temp[:, 1] / 100) + np_temp = np.nan_to_num(np_temp) + return np_temp + + +for path in glob.glob("log_*"): + print(path) + data = get_data(path) + np.save("data_" + path + ".npy", data) diff --git a/avg_pooling_mlp/data_loader.py b/avg_pooling_mlp/data_loader.py new file mode 100644 index 0000000..0a0d430 --- /dev/null +++ b/avg_pooling_mlp/data_loader.py @@ -0,0 +1,31 @@ +import torch + + +def data_loader( + pattern: torch.Tensor, + labels: torch.Tensor, + worker_init_fn, + generator, + batch_size: int = 128, + shuffle: bool = True, + torch_device: torch.device = torch.device("cpu"), +) -> torch.utils.data.dataloader.DataLoader: + + assert pattern.ndim >= 3 + + pattern_storage: torch.Tensor = pattern.to(torch_device).type(torch.float32) + if pattern_storage.ndim == 3: + pattern_storage = pattern_storage.unsqueeze(1) + pattern_storage /= pattern_storage.max() + + label_storage: torch.Tensor = labels.to(torch_device).type(torch.int64) + + dataloader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(pattern_storage, label_storage), + batch_size=batch_size, + shuffle=shuffle, + worker_init_fn=worker_init_fn, + generator=generator, + ) + + return dataloader diff --git a/avg_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy b/avg_pooling_mlp/data_log_iter20_lr_1.0000e-03_1.0000e-02_1.0000e-03_.npy new file mode 100644 index 0000000000000000000000000000000000000000..cc4bf474887e2e07b783a245831322a59054bdb6 GIT binary patch literal 4592 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torch.utils.data.dataloader.DataLoader, + torch.utils.data.dataloader.DataLoader, + torchvision.transforms.Compose, + torchvision.transforms.Compose, +]: + if dataset == "MNIST": + tv_dataset_train = torchvision.datasets.MNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.MNIST( + root="data", train=False, download=True + ) + elif dataset == "FashionMNIST": + tv_dataset_train = torchvision.datasets.FashionMNIST( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.FashionMNIST( + root="data", train=False, download=True + ) + elif dataset == "CIFAR10": + tv_dataset_train = torchvision.datasets.CIFAR10( + root="data", train=True, download=True + ) + tv_dataset_test = torchvision.datasets.CIFAR10( + root="data", train=False, download=True + ) + else: + raise NotImplementedError("This dataset is not implemented.") + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + torch.random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + + if dataset == "MNIST" or dataset == "FashionMNIST": + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=tv_dataset_train.data, + labels=tv_dataset_train.targets, + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=tv_dataset_test.data, + labels=tv_dataset_test.targets, + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + train_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.RandomCrop((input_dim_x, input_dim_y))], + ) + else: + + train_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_train, + pattern=torch.tensor(tv_dataset_train.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_train.targets), + shuffle=True, + worker_init_fn=seed_worker, + generator=g, + ) + + test_dataloader = data_loader( + torch_device=torch_device, + batch_size=batch_size_test, + pattern=torch.tensor(tv_dataset_test.data).movedim(-1, 1), + labels=torch.tensor(tv_dataset_test.targets), + shuffle=False, + worker_init_fn=seed_worker, + generator=g, + ) + + # Data augmentation filter + test_processing_chain = torchvision.transforms.Compose( + transforms=[torchvision.transforms.CenterCrop((input_dim_x, input_dim_y))], + ) + + if da_auto_mode: + train_processing_chain = torchvision.transforms.Compose( + transforms=[ + v2.AutoAugment( + policy=torchvision.transforms.AutoAugmentPolicy( + v2.AutoAugmentPolicy.CIFAR10 + ) + ), + torchvision.transforms.CenterCrop((input_dim_x, input_dim_y)), + ], + ) + else: + train_processing_chain = 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None: + + assert loss_mode >= 0 + assert loss_mode <= 1 + + assert h.ndim == 2 + + if loss_mode == 0: + + # Convert label into one hot + target_one_hot: torch.Tensor = torch.zeros( + ( + labels.shape[0], + number_of_output_neurons, + ), + device=h.device, + dtype=h.dtype, + ) + + target_one_hot.scatter_( + 1, + labels.to(h.device).unsqueeze(1), + torch.ones( + (labels.shape[0], 1), + device=h.device, + dtype=h.dtype, + ), + ) + + my_loss: torch.Tensor = ((h - target_one_hot) ** 2).sum(dim=0).mean( + dim=0 + ) * loss_coeffs_mse + + my_loss = ( + my_loss + + ( + (target_one_hot * torch.log((target_one_hot + 1e-20) / (h + 1e-20))) + .sum(dim=0) + .mean(dim=0) + ) + * loss_coeffs_kldiv + ) + + my_loss = my_loss / (abs(loss_coeffs_kldiv) + abs(loss_coeffs_mse)) + + return my_loss + + elif loss_mode == 1: + my_loss = torch.nn.functional.cross_entropy(h, labels.to(h.device)) + return my_loss + else: + return None diff --git a/avg_pooling_mlp/make_network.py b/avg_pooling_mlp/make_network.py new file mode 100644 index 0000000..464d9b9 --- /dev/null +++ b/avg_pooling_mlp/make_network.py @@ -0,0 +1,212 @@ +import torch +from append_block import append_block +from L1NormLayer import L1NormLayer +from append_parameter import append_parameter + + +def make_network( + input_dim_x: int, + input_dim_y: int, + input_number_of_channel: int, + iterations: int, + torch_device: torch.device, + epsilon: bool | None = None, + positive_function_type: int = 0, + beta: float | None = None, + # Conv: + number_of_output_channels: list[int] = [32 * 1, 64 * 1, 96 * 1, 10], + kernel_size_conv: list[tuple[int, int]] = [ + (5, 5), + (5, 5), + (-1, -1), # Take the whole input image x and y size + (1, 1), + ], + stride_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + padding_conv: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_conv: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + # Pool: + kernel_size_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), # No pooling layer + (-1, -1), # No pooling layer + ], + stride_pool: list[tuple[int, int]] = [ + (2, 2), + (2, 2), + (-1, -1), + (-1, -1), + ], + padding_pool: list[tuple[int, int]] = [ + (0, 0), + (0, 0), + (0, 0), + (0, 0), + ], + dilation_pool: list[tuple[int, int]] = [ + (1, 1), + (1, 1), + (1, 1), + (1, 1), + ], + enable_onoff: bool = False, +) -> tuple[ + torch.nn.Sequential, + list[list[torch.nn.parameter.Parameter]], + list[str], +]: + + assert len(number_of_output_channels) == len(kernel_size_conv) + assert len(number_of_output_channels) == len(stride_conv) + assert len(number_of_output_channels) == len(padding_conv) + assert len(number_of_output_channels) == len(dilation_conv) + assert len(number_of_output_channels) == len(kernel_size_pool) + assert len(number_of_output_channels) == len(stride_pool) + assert len(number_of_output_channels) == len(padding_pool) + assert len(number_of_output_channels) == len(dilation_pool) + + if enable_onoff: + input_number_of_channel *= 2 + + parameter_cnn_top: list[torch.nn.parameter.Parameter] = [] + parameter_nnmf: list[torch.nn.parameter.Parameter] = [] + parameter_norm: list[torch.nn.parameter.Parameter] = [] + + test_image = torch.ones( + (1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device + ) + + network = torch.nn.Sequential() + network = network.to(torch_device) + + for block_id in range(0, len(number_of_output_channels)): + + test_image = append_block( + network=network, + out_channels=number_of_output_channels[block_id], + test_image=test_image, + dilation=dilation_conv[block_id], + padding=padding_conv[block_id], + stride=stride_conv[block_id], + kernel_size=kernel_size_conv[block_id], + epsilon=epsilon, + positive_function_type=positive_function_type, + beta=beta, + iterations=iterations, + torch_device=torch_device, + parameter_cnn_top=parameter_cnn_top, + parameter_nnmf=parameter_nnmf, + parameter_norm=parameter_norm, + last_layer=block_id == len(number_of_output_channels) - 1, + ) + + if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0): + + network.append(torch.nn.AvgPool2d(kernel_size=(2, 2), stride=(2, 2))) + test_image = network[-1](test_image) + + # network.append(torch.nn.ReLU()) + # test_image = network[-1](test_image) + + # mock_output = ( + # torch.nn.functional.conv2d( + # torch.zeros( + # 1, + # 1, + # test_image.shape[2], + # test_image.shape[3], + # ), + # torch.zeros((1, 1, 2, 2)), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # .squeeze(0) + # .squeeze(0) + # ) + + # network.append( + # torch.nn.Unfold( + # kernel_size=(2, 2), + # stride=(2, 2), + # padding=(0, 0), + # dilation=(1, 1), + # ) + # ) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Fold( + # output_size=mock_output.shape, + # kernel_size=(1, 1), + # dilation=1, + # padding=0, + # stride=1, + # ) + # ) + # test_image = network[-1](test_image) + + # network.append(L1NormLayer()) + # test_image = network[-1](test_image) + + # network.append( + # torch.nn.Conv2d( + # in_channels=test_image.shape[1], + # out_channels=test_image.shape[1] // 4, + # kernel_size=(1, 1), + # bias=False, + # ).to(torch_device) + # ) + + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_nnmf) + + # network.append( + # torch.nn.BatchNorm2d( + # num_features=test_image.shape[1], + # device=torch_device, + # momentum=0.1, + # track_running_stats=False, + # ) + # ) + # test_image = network[-1](test_image) + # append_parameter(module=network[-1], parameter_list=parameter_norm) + + network.append(torch.nn.Softmax(dim=1)) + test_image = network[-1](test_image) + + network.append(torch.nn.Flatten()) + test_image = network[-1](test_image) + + parameters: list[list[torch.nn.parameter.Parameter]] = [ + parameter_cnn_top, + parameter_nnmf, + parameter_norm, + ] + + name_list: list[str] = [ + "cnn_top", + "nnmf", + "batchnorm2d", + ] + + return ( + network, + parameters, + name_list, + ) diff --git a/avg_pooling_mlp/make_optimize.py b/avg_pooling_mlp/make_optimize.py new file mode 100644 index 0000000..ab1a4e0 --- /dev/null +++ b/avg_pooling_mlp/make_optimize.py @@ -0,0 +1,32 @@ +import torch + + +def make_optimize( + parameters: list[list[torch.nn.parameter.Parameter]], + lr_initial: list[float], + eps=1e-10, +) -> tuple[ + list[torch.optim.Adam | None], + list[torch.optim.lr_scheduler.ReduceLROnPlateau | None], +]: + list_optimizer: list[torch.optim.Adam | None] = [] + list_lr_scheduler: list[torch.optim.lr_scheduler.ReduceLROnPlateau | None] = [] + + assert len(parameters) == len(lr_initial) + + for i in range(0, len(parameters)): + if len(parameters[i]) > 0: + list_optimizer.append(torch.optim.Adam(parameters[i], lr=lr_initial[i])) + else: + list_optimizer.append(None) + + for i in range(0, len(list_optimizer)): + if list_optimizer[i] is not None: + pass + list_lr_scheduler.append( + torch.optim.lr_scheduler.ReduceLROnPlateau(list_optimizer[i], eps=eps) # type: ignore + ) + else: + list_lr_scheduler.append(None) + + return (list_optimizer, list_lr_scheduler) diff --git a/avg_pooling_mlp/non_linear_weigth_function.py b/avg_pooling_mlp/non_linear_weigth_function.py new file mode 100644 index 0000000..053a9b6 --- /dev/null +++ b/avg_pooling_mlp/non_linear_weigth_function.py @@ -0,0 +1,26 @@ +import torch + + +def non_linear_weigth_function( + weight: torch.Tensor, beta: torch.Tensor | None, positive_function_type: int +) -> torch.Tensor: + + if positive_function_type == 0: + positive_weights = torch.abs(weight) + + elif positive_function_type == 1: + assert beta is not None + positive_weights = weight + max_value = torch.abs(positive_weights).max() + if max_value > 80: + positive_weights = 80.0 * positive_weights / max_value + positive_weights = torch.exp((torch.tanh(beta) + 1.0) * 0.5 * positive_weights) + + elif positive_function_type == 2: + assert beta is not None + positive_weights = (torch.tanh(beta * weight) + 1.0) * 0.5 + + else: + positive_weights = weight + + return positive_weights diff --git a/avg_pooling_mlp/plot.py b/avg_pooling_mlp/plot.py new file mode 100644 index 0000000..ad22d33 --- /dev/null +++ b/avg_pooling_mlp/plot.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt + +data = np.load("data_log.npy") +plt.loglog( + data[:, 0], + 100.0 * (1.0 - data[:, 1] / 10000.0), + "k", +) + +plt.legend() +plt.xlabel("Epoch") +plt.ylabel("Error [%]") +plt.title("CIFAR10") +plt.show() diff --git a/avg_pooling_mlp/run_network.py b/avg_pooling_mlp/run_network.py new file mode 100644 index 0000000..8d0a43f --- /dev/null +++ b/avg_pooling_mlp/run_network.py @@ -0,0 +1,251 @@ +import os + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +import argh + +import time +import numpy as np +import torch + +rand_seed: int = 21 +torch.manual_seed(rand_seed) +torch.cuda.manual_seed(rand_seed) +np.random.seed(rand_seed) + +from torch.utils.tensorboard import SummaryWriter + +from make_network import make_network +from get_the_data import get_the_data +from loss_function import loss_function +from make_optimize import make_optimize + + +def main( + lr_initial_nnmf: float = 0.01, + lr_initial_cnn_top: float = 0.001, + lr_initial_norm: float = 0.001, + iterations: int = 20, + dataset: str = "CIFAR10", # "CIFAR10", "FashionMNIST", "MNIST" + only_print_network: bool = False, +) -> None: + + da_auto_mode: bool = False # Automatic Data Augmentation from TorchVision + lr_limit: float = 1e-9 + + torch_device: torch.device = ( + torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") + ) + torch.set_default_dtype(torch.float32) + + # Some parameters + batch_size_train: int = 50 # 0 + batch_size_test: int = 50 # 0 + number_of_epoch: int = 500 + + loss_mode: int = 0 + loss_coeffs_mse: float = 0.5 + loss_coeffs_kldiv: float = 1.0 + print( + "loss_mode: ", + loss_mode, + "loss_coeffs_mse: ", + loss_coeffs_mse, + "loss_coeffs_kldiv: ", + loss_coeffs_kldiv, + ) + + if dataset == "MNIST" or dataset == "FashionMNIST": + input_number_of_channel: int = 1 + input_dim_x: int = 24 + input_dim_y: int = 24 + else: + input_number_of_channel = 3 + input_dim_x = 28 + input_dim_y = 28 + + train_dataloader, test_dataloader, train_processing_chain, test_processing_chain = ( + get_the_data( + dataset, + batch_size_train, + batch_size_test, + torch_device, + input_dim_x, + input_dim_y, + flip_p=0.5, + jitter_brightness=0.5, + jitter_contrast=0.1, + jitter_saturation=0.1, + jitter_hue=0.15, + da_auto_mode=da_auto_mode, + ) + ) + + ( + network, + parameters, + name_list, + ) = make_network( + input_dim_x=input_dim_x, + input_dim_y=input_dim_y, + input_number_of_channel=input_number_of_channel, + iterations=iterations, + torch_device=torch_device, + ) + + print(network) + + print() + print("Information about used parameters:") + number_of_parameter: int = 0 + for i, parameter_list in enumerate(parameters): + count_parameter: int = 0 + for parameter_element in parameter_list: + count_parameter += parameter_element.numel() + print(f"{name_list[i]}: {count_parameter}") + number_of_parameter += count_parameter + print(f"total number of parameter: {number_of_parameter}") + + if only_print_network: + exit() + + ( + optimizers, + lr_schedulers, + ) = make_optimize( + parameters=parameters, + lr_initial=[ + lr_initial_cnn_top, + lr_initial_nnmf, + lr_initial_norm, + ], + ) + + my_string: str = "_lr_" + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f"{lr_schedulers[i].get_last_lr()[0]:.4e}_" # type: ignore + else: + my_string += "-_" + + default_path: str = f"iter{iterations}{my_string}" + log_dir: str = f"log_{default_path}" + + tb = SummaryWriter(log_dir=log_dir) + + for epoch_id in range(0, number_of_epoch): + print() + print(f"Epoch: {epoch_id}") + t_start: float = time.perf_counter() + + train_loss: float = 0.0 + train_correct: int = 0 + train_number: int = 0 + test_correct: int = 0 + test_number: int = 0 + + # Switch the network into training mode + network.train() + + # This runs in total for one epoch split up into mini-batches + for image, target in train_dataloader: + + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].zero_grad() # type: ignore + + output = network(train_processing_chain(image)) + + loss = loss_function( + h=output, + labels=target, + number_of_output_neurons=output.shape[1], + loss_mode=loss_mode, + loss_coeffs_mse=loss_coeffs_mse, + loss_coeffs_kldiv=loss_coeffs_kldiv, + ) + + assert loss is not None + train_loss += loss.item() + train_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + train_number += target.shape[0] + + # Calculate backprop + loss.backward() + + # Update the parameter + # Clean the gradient + for i in range(0, len(optimizers)): + if optimizers[i] is not None: + optimizers[i].step() # type: ignore + + perfomance_train_correct: float = 100.0 * train_correct / train_number + # Update the learning rate + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_schedulers[i].step(train_loss) # type: ignore + + my_string = "Actual lr: " + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + my_string += f" {lr_schedulers[i].get_last_lr()[0]:.4e} " # type: ignore + else: + my_string += " --- " + + print(my_string) + t_training: float = time.perf_counter() + + # Switch the network into evalution mode + network.eval() + + with torch.no_grad(): + + for image, target in test_dataloader: + output = network(test_processing_chain(image)) + + test_correct += (output.argmax(dim=1) == target).sum().cpu().numpy() + test_number += target.shape[0] + + t_testing = time.perf_counter() + + perfomance_test_correct: float = 100.0 * test_correct / test_number + + tb.add_scalar("Train Loss", train_loss / float(train_number), epoch_id) + tb.add_scalar("Train Number Correct", train_correct, epoch_id) + tb.add_scalar("Test Number Correct", test_correct, epoch_id) + + print( + f"Training: Loss={train_loss / float(train_number):.5f} Correct={perfomance_train_correct:.2f}%" + ) + print(f"Testing: Correct={perfomance_test_correct:.2f}%") + print( + f"Time: Training={(t_training - t_start):.1f}sec, Testing={(t_testing - t_training):.1f}sec" + ) + + tb.flush() + + lr_check: list[float] = [] + for i in range(0, len(lr_schedulers)): + if lr_schedulers[i] is not None: + lr_check.append(lr_schedulers[i].get_last_lr()[0]) # type: ignore + + lr_check_max = float(torch.tensor(lr_check).max()) + + if lr_check_max < lr_limit: + torch.save(network, f"Model_{default_path}.pt") + tb.close() + print("Done (lr_limit)") + return + + torch.save(network, f"Model_{default_path}.pt") + print() + + tb.close() + print("Done (loop end)") + + return + + +if __name__ == "__main__": + argh.dispatch_command(main) diff --git a/avg_pooling_mlp_noinbetween1x1/L1NormLayer.py b/avg_pooling_mlp_noinbetween1x1/L1NormLayer.py new file mode 100644 index 0000000..6816b3a --- /dev/null +++ b/avg_pooling_mlp_noinbetween1x1/L1NormLayer.py @@ -0,0 +1,13 @@ +import torch + + +class L1NormLayer(torch.nn.Module): + + epsilon: float + + def __init__(self, epsilon: float = 10e-20) -> None: + super().__init__() + self.epsilon = epsilon + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input / (input.sum(dim=1, keepdim=True) + self.epsilon) diff --git a/avg_pooling_mlp_noinbetween1x1/Model_iter20_lr_-_1.0000e-02_1.0000e-03_.pt b/avg_pooling_mlp_noinbetween1x1/Model_iter20_lr_-_1.0000e-02_1.0000e-03_.pt new file mode 100644 index 0000000000000000000000000000000000000000..76148b9c17966fe1e433edadce64a564cbd0f7d9 GIT binary patch literal 623372 zcmcG!30O_t`~RO5DnbzwMTSC25>k8Jkqi+^q|mGrO=njKk>)Z~s7#s3P-Kc!d)=85 zA~TuiIb&pq|6XUG=XrcT&-eSkzQ61G{c>67wbt5a@BMzSd#`n$z4v&h#%d}m&6=tF 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