Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
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@ -93,6 +93,63 @@ train_processing_chain = torchvision.transforms.Compose(
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)
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```
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### Data interfacing
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```python
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import torch
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import numpy as np
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class MyDataset(torch.utils.data.Dataset):
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# Initialize
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def __init__(self, train: bool = False) -> None:
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super(MyDataset, self).__init__()
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if train is True:
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self.pattern_storage: np.ndarray = np.load("train_pattern_storage.npy")
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self.label_storage: np.ndarray = np.load("train_label_storage.npy")
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else:
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self.pattern_storage = np.load("test_pattern_storage.npy")
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self.label_storage = np.load("test_label_storage.npy")
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self.pattern_storage = self.pattern_storage.astype(np.float32)
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self.pattern_storage /= np.max(self.pattern_storage)
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# How many pattern are there?
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self.number_of_pattern: int = self.label_storage.shape[0]
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def __len__(self) -> int:
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return self.number_of_pattern
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# Get one pattern at position index
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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image = torch.tensor(self.pattern_storage[index, np.newaxis, :, :])
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target = int(self.label_storage[index])
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return image, target
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```
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### Data loader
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```python
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dataset_train = MyDataset(train=True)
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dataset_test = MyDataset(train=False)
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batch_size_train = 100
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batch_size_test = 100
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train_data_load = torch.utils.data.DataLoader(
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dataset_train, batch_size=batch_size_train, shuffle=True
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)
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test_data_load = torch.utils.data.DataLoader(
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dataset_test, batch_size=batch_size_test, shuffle=False
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)
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```
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and you need the data from [here](https://davrot.github.io/pytutorial/pytorch/MNIST/)
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## [What makes it learn?](https://pytorch.org/docs/stable/optim.html)
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### [Optimizer Algorithms](https://pytorch.org/docs/stable/optim.html#algorithms)
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@ -220,3 +277,224 @@ plt.ylabel("Train Performance")
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plt.title(which_scalar)
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plt.show()
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```
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## MNIST with Adam, ReduceLROnPlateau, cross-entropy on CPU
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Operations you will see that are not explained yet:
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|---|---|
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|[network.train()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train)| : "Sets the module in training mode."|
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|[optimizer.zero_grad()](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html)| : "Sets the gradients of all optimized [torch.Tensor](https://pytorch.org/docs/stable/tensors.html#torch.Tensor)s to zero." For every mini batch we (need to) clean the gradient which is used for training the parameters. |
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|[optimizer.step()](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step)| : "Performs a single optimization step (parameter update)."|
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|[loss.backward()](https://pytorch.org/docs/stable/generated/torch.Tensor.backward.html)| : "Computes the gradient of current tensor w.r.t. graph leaves."|
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|[lr_scheduler.step(train_loss)](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html#torch.optim.lr_scheduler.ReduceLROnPlateau)| : After an epoch the learning rate (might be) changed. For other [Learning rate scheduler](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) .step() might have no parameter.|
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|[network.eval()](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval)| : "Sets the module in evaluation mode."|
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|[with torch.no_grad():](https://pytorch.org/docs/stable/generated/torch.no_grad.html)| : "Context-manager that disabled gradient calculation."|
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```python
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import os
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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import torch
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import torchvision # type:ignore
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import numpy as np
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import time
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from torch.utils.tensorboard import SummaryWriter
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class MyDataset(torch.utils.data.Dataset):
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# Initialize
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def __init__(self, train: bool = False) -> None:
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super(MyDataset, self).__init__()
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if train is True:
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self.pattern_storage: np.ndarray = np.load("train_pattern_storage.npy")
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self.label_storage: np.ndarray = np.load("train_label_storage.npy")
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else:
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self.pattern_storage = np.load("test_pattern_storage.npy")
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self.label_storage = np.load("test_label_storage.npy")
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self.pattern_storage = self.pattern_storage.astype(np.float32)
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self.pattern_storage /= np.max(self.pattern_storage)
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# How many pattern are there?
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self.number_of_pattern: int = self.label_storage.shape[0]
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def __len__(self) -> int:
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return self.number_of_pattern
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# Get one pattern at position index
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def __getitem__(self, index: int) -> tuple[torch.Tensor, int]:
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image = torch.tensor(self.pattern_storage[index, np.newaxis, :, :])
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target = int(self.label_storage[index])
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return image, target
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# Some parameters
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input_number_of_channel: int = 1
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input_dim_x: int = 24
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input_dim_y: int = 24
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number_of_output_channels_conv1: int = 32
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number_of_output_channels_conv2: int = 64
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number_of_output_channels_flatten1: int = 576
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number_of_output_channels_full1: int = 10
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kernel_size_conv1: tuple[int, int] = (5, 5)
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kernel_size_pool1: tuple[int, int] = (2, 2)
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kernel_size_conv2: tuple[int, int] = (5, 5)
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kernel_size_pool2: tuple[int, int] = (2, 2)
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stride_conv1: tuple[int, int] = (1, 1)
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stride_pool1: tuple[int, int] = (2, 2)
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stride_conv2: tuple[int, int] = (1, 1)
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stride_pool2: tuple[int, int] = (2, 2)
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padding_conv1: int = 0
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padding_pool1: int = 0
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padding_conv2: int = 0
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padding_pool2: int = 0
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network = torch.nn.Sequential(
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torch.nn.Conv2d(
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in_channels=input_number_of_channel,
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out_channels=number_of_output_channels_conv1,
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kernel_size=kernel_size_conv1,
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stride=stride_conv1,
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padding=padding_conv1,
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),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d(
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kernel_size=kernel_size_pool1, stride=stride_pool1, padding=padding_pool1
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),
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torch.nn.Conv2d(
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in_channels=number_of_output_channels_conv1,
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out_channels=number_of_output_channels_conv2,
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kernel_size=kernel_size_conv2,
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stride=stride_conv2,
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padding=padding_conv2,
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),
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torch.nn.ReLU(),
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torch.nn.MaxPool2d(
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kernel_size=kernel_size_pool2, stride=stride_pool2, padding=padding_pool2
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),
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torch.nn.Flatten(
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start_dim=1,
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),
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torch.nn.Linear(
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in_features=number_of_output_channels_flatten1,
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out_features=number_of_output_channels_full1,
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bias=True,
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),
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torch.nn.Softmax(dim=1),
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)
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test_processing_chain = torchvision.transforms.Compose(
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transforms=[torchvision.transforms.CenterCrop((24, 24))],
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)
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train_processing_chain = torchvision.transforms.Compose(
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transforms=[torchvision.transforms.RandomCrop((24, 24))],
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)
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dataset_train = MyDataset(train=True)
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dataset_test = MyDataset(train=False)
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batch_size_train = 100
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batch_size_test = 100
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train_data_load = torch.utils.data.DataLoader(
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dataset_train, batch_size=batch_size_train, shuffle=True
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)
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test_data_load = torch.utils.data.DataLoader(
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dataset_test, batch_size=batch_size_test, shuffle=False
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)
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# -------------------------------------------
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# The optimizer
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optimizer = torch.optim.Adam(network.parameters(), lr=0.01)
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# The LR Scheduler
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lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
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number_of_test_pattern: int = dataset_test.__len__()
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number_of_train_pattern: int = dataset_train.__len__()
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number_of_epoch: int = 50
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tb = SummaryWriter(log_dir="run")
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loss_function = torch.nn.CrossEntropyLoss()
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for epoch_id in range(0, number_of_epoch):
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print(f"Epoch: {epoch_id}")
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t_start: float = time.perf_counter()
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train_loss: float = 0.0
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train_correct: int = 0
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train_number: int = 0
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test_correct: int = 0
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test_number: int = 0
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# Switch the network into training mode
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network.train()
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# This runs in total for one epoch split up into mini-batches
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for image, target in train_data_load:
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# Clean the gradient
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optimizer.zero_grad()
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output = network(train_processing_chain(image))
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loss = loss_function(output, target)
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train_loss += loss.item()
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train_correct += (output.argmax(dim=1) == target).sum().numpy()
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train_number += target.shape[0]
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# Calculate backprop
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loss.backward()
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# Update the parameter
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optimizer.step()
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# Update the learning rate
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lr_scheduler.step(train_loss)
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t_training: float = time.perf_counter()
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# Switch the network into evalution mode
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network.eval()
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with torch.no_grad():
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for image, target in test_data_load:
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output = network(test_processing_chain(image))
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test_correct += (output.argmax(dim=1) == target).sum().numpy()
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test_number += target.shape[0]
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t_testing = time.perf_counter()
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perfomance_test_correct: float = 100.0 * test_correct / test_number
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perfomance_train_correct: float = 100.0 * train_correct / train_number
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tb.add_scalar("Train Loss", train_loss, epoch_id)
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tb.add_scalar("Train Number Correct", train_correct, epoch_id)
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tb.add_scalar("Test Number Correct", test_correct, epoch_id)
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print(f"Training: Loss={train_loss:.5f} Correct={perfomance_train_correct:.2f}%")
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print(f"Testing: Correct={perfomance_test_correct:.2f}%")
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print(
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f"Time: Training={(t_training-t_start):.1f}sec, Testing={(t_testing-t_training):.1f}sec"
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)
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torch.save(network, "Model_MNIST_A_" + str(epoch_id) + ".pt")
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print()
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tb.flush()
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tb.close()
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```
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