777 lines
25 KiB
Python
777 lines
25 KiB
Python
import torch
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import time
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from network.Parameter import Config
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from torch.utils.tensorboard import SummaryWriter
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from network.SbSLayer import SbSLayer
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from network.NNMFLayer import NNMFLayer
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from network.save_weight_and_bias import save_weight_and_bias
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from network.SbSReconstruction import SbSReconstruction
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def add_weight_and_bias_to_histogram(
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network: torch.nn.modules.container.Sequential,
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tb: SummaryWriter,
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iteration_number: int,
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) -> None:
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for id in range(0, len(network)):
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# ################################################
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# Log the SbS Weights
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# ################################################
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if (isinstance(network[id], SbSLayer) is True) or (
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isinstance(network[id], NNMFLayer) is True
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):
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if network[id]._w_trainable is True:
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try:
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tb.add_histogram(
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f"Weights Layer {id}",
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network[id].weights,
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iteration_number,
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)
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except ValueError:
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pass
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# ################################################
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# Log the Conv2 Weights and Biases
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# ################################################
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if isinstance(network[id], torch.nn.modules.conv.Conv2d) is True:
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if network[id]._w_trainable is True:
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try:
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tb.add_histogram(
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f"Weights Layer {id}",
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network[id]._parameters["weight"].data,
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iteration_number,
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)
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except ValueError:
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pass
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try:
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tb.add_histogram(
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f"Bias Layer {id}",
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network[id]._parameters["bias"].data,
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iteration_number,
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)
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except ValueError:
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pass
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tb.flush()
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# loss_mode == 0: "normal" SbS loss function mixture
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# loss_mode == 1: cross_entropy
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def loss_function(
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h: torch.Tensor,
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labels: torch.Tensor,
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device: torch.device,
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default_dtype: torch.dtype,
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loss_mode: int = 0,
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number_of_output_neurons: int = 10,
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loss_coeffs_mse: float = 0.0,
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loss_coeffs_kldiv: float = 0.0,
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) -> torch.Tensor | None:
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assert loss_mode >= 0
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assert loss_mode <= 1
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h = h.squeeze(-1).squeeze(-1)
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assert h.ndim == 2
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if loss_mode == 0:
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# Convert label into one hot
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target_one_hot: torch.Tensor = torch.zeros(
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(
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labels.shape[0],
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number_of_output_neurons,
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),
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device=device,
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dtype=default_dtype,
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)
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target_one_hot.scatter_(
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1,
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labels.to(device).unsqueeze(1),
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torch.ones(
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(labels.shape[0], 1),
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device=device,
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dtype=default_dtype,
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),
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)
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h_y1 = torch.log(h + 1e-20)
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my_loss: torch.Tensor = (
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torch.nn.functional.mse_loss(
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h,
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target_one_hot,
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reduction="sum",
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)
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* loss_coeffs_mse
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+ torch.nn.functional.kl_div(h_y1, target_one_hot + 1e-20, reduction="sum")
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* loss_coeffs_kldiv
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) / (loss_coeffs_kldiv + loss_coeffs_mse)
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return my_loss
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elif loss_mode == 1:
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my_loss = torch.nn.functional.cross_entropy(
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h.squeeze(-1).squeeze(-1), labels.to(device)
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)
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return my_loss
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else:
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return None
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def loss_function_reconstruction(
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h_reco: torch.Tensor,
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h_input: torch.Tensor,
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loss_mode: int = 0,
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loss_coeffs_mse: float = 0.0,
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loss_coeffs_kldiv: float = 0.0,
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) -> torch.Tensor | None:
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assert loss_mode >= 0
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assert loss_mode <= 0
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assert h_reco.ndim == 4
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assert h_input.ndim == 4
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assert h_reco.shape[0] == h_input.shape[0]
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assert h_reco.shape[1] == h_input.shape[1]
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assert h_reco.shape[2] == h_input.shape[2]
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assert h_reco.shape[3] == h_input.shape[3]
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if loss_mode == 0:
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h_reco_log = torch.log(h_reco + 1e-20)
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my_loss: torch.Tensor = (
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torch.nn.functional.mse_loss(
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h_reco,
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h_input,
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reduction="sum",
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)
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* loss_coeffs_mse
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+ torch.nn.functional.kl_div(h_reco_log, h_input + 1e-20, reduction="sum")
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* loss_coeffs_kldiv
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) / (loss_coeffs_kldiv + loss_coeffs_mse)
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return my_loss
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else:
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return None
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def forward_pass_train(
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input: torch.Tensor,
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labels: torch.Tensor,
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the_dataset_train,
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cfg: Config,
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network: torch.nn.modules.container.Sequential,
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device: torch.device,
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default_dtype: torch.dtype,
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) -> list[torch.Tensor]:
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h_collection = []
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h_collection.append(
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the_dataset_train.pattern_filter_train(input, cfg)
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.type(dtype=default_dtype)
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.to(device=device)
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)
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for id in range(0, len(network)):
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if isinstance(network[id], SbSLayer) is True:
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h_collection.append(network[id](h_collection[-1], labels))
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else:
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h_collection.append(network[id](h_collection[-1]))
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return h_collection
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def forward_pass_test(
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input: torch.Tensor,
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labels: torch.Tensor | None,
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the_dataset_test,
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cfg: Config,
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network: torch.nn.modules.container.Sequential,
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device: torch.device,
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default_dtype: torch.dtype,
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mini_batch_id: int = -1,
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overwrite_number_of_spikes: int = -1,
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) -> list[torch.Tensor]:
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h_collection = []
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h_collection.append(
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the_dataset_test.pattern_filter_test(input, cfg)
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.type(dtype=default_dtype)
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.to(device=device)
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)
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for id in range(0, len(network)):
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if (cfg.extract_noisy_pictures is True) or (overwrite_number_of_spikes != -1):
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if isinstance(network[id], SbSLayer) is True:
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h_collection.append(
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network[id](
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h_collection[-1],
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layer_id=id,
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labels=labels,
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extract_noisy_pictures=cfg.extract_noisy_pictures,
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mini_batch_id=mini_batch_id,
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overwrite_number_of_spikes=overwrite_number_of_spikes,
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)
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)
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else:
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h_collection.append(network[id](h_collection[-1]))
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else:
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h_collection.append(network[id](h_collection[-1]))
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return h_collection
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def run_optimizer(
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network: torch.nn.modules.container.Sequential,
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optimizer: list,
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cfg: Config,
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) -> None:
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for id in range(0, len(network)):
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if (isinstance(network[id], SbSLayer) is True) or (
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isinstance(network[id], NNMFLayer) is True
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):
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network[id].update_pre_care()
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for optimizer_item in optimizer:
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if optimizer_item is not None:
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optimizer_item.step()
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for id in range(0, len(network)):
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if (isinstance(network[id], SbSLayer) is True) or (
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isinstance(network[id], NNMFLayer) is True
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):
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network[id].update_after_care(
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cfg.learning_parameters.learning_rate_threshold_w
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/ float(
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network[id]._number_of_input_neurons
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# * network[id]._kernel_size[0]
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# * network[id]._kernel_size[1]
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),
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)
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# ####################################
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# Update the learning rate
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# ####################################
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def run_lr_scheduler(
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cfg: Config,
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lr_scheduler,
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optimizer,
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performance_for_batch: float,
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my_loss_for_batch: float,
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tb,
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logging,
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) -> None:
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# Inter-epoch learning rate adaptation
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for lr_scheduler_item in lr_scheduler:
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if (
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(lr_scheduler_item is not None)
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and (performance_for_batch >= 0.0)
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and (my_loss_for_batch >= 0.0)
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):
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if cfg.learning_parameters.lr_scheduler_use_performance is True:
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lr_scheduler_item.step(100.0 - performance_for_batch)
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else:
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lr_scheduler_item.step(my_loss_for_batch)
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tb.add_scalar(
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"Train Error",
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100.0 - performance_for_batch,
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cfg.epoch_id,
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)
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tb.add_scalar("Train Loss", my_loss_for_batch, cfg.epoch_id)
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tb.add_scalar(
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"Learning Rate Scale WF",
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optimizer[0].param_groups[-1]["lr"],
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cfg.epoch_id,
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)
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tb.flush()
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def deal_with_gradient_scale(epoch_id: int, mini_batch_number: int, network):
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if (epoch_id == 0) and (mini_batch_number == 0):
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for id in range(0, len(network)):
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if isinstance(network[id], SbSLayer) is True:
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network[id].after_batch(True)
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else:
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for id in range(0, len(network)):
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if isinstance(network[id], SbSLayer) is True:
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network[id].after_batch()
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def loop_train(
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cfg: Config,
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network: torch.nn.modules.container.Sequential,
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my_loader_train: torch.utils.data.dataloader.DataLoader,
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the_dataset_train,
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optimizer: list,
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device: torch.device,
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default_dtype: torch.dtype,
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logging,
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adapt_learning_rate: bool,
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tb: SummaryWriter,
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lr_scheduler,
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last_test_performance: float,
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order_id: float | int | None = None,
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) -> tuple[float, float, float, float]:
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correct_in_minibatch: int = 0
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loss_in_minibatch: float = 0.0
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number_of_pattern_in_minibatch: int = 0
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mini_batch_number: int = -1
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full_loss: float = 0.0
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full_correct: float = 0.0
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full_count: float = 0.0
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epoch_id: int = cfg.epoch_id
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my_loss_for_batch: float = -1.0
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performance_for_batch: float = -1.0
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time_forward: float = 0.0
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time_backward: float = 0.0
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with torch.enable_grad():
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for h_x, h_x_labels in my_loader_train:
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time_mini_batch_start: float = time.perf_counter()
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# ############################################################
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# Reset the gradient after an update (or the first loop pass)
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# ############################################################
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if number_of_pattern_in_minibatch == 0:
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# Reset the gradient of the torch optimizers
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for optimizer_item in optimizer:
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if optimizer_item is not None:
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optimizer_item.zero_grad()
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loss_in_minibatch = 0.0
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mini_batch_number += 1
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correct_in_minibatch = 0
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time_forward = 0.0
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time_backward = 0.0
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# ####################################
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# Update the learning rate
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# ####################################
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if adapt_learning_rate is True:
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run_lr_scheduler(
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cfg=cfg,
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lr_scheduler=lr_scheduler,
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optimizer=optimizer,
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performance_for_batch=performance_for_batch,
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my_loss_for_batch=my_loss_for_batch,
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tb=tb,
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logging=logging,
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)
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logging.info(
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(
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f"\t\t\tLearning rate: "
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f"weights:{optimizer[0].param_groups[-1]['lr']:^15.3e} "
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)
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)
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if last_test_performance < 0:
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logging.info("")
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else:
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if isinstance(network[-1], SbSReconstruction) is False:
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logging.info(
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(
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f"\t\t\tLast test performance: "
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f"{last_test_performance/100.0:^6.2%}"
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)
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)
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else:
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logging.info(
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(
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f"\t\t\tLast test performance: "
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f"{last_test_performance:^6.2e}"
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)
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)
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logging.info("----------------")
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number_of_pattern_in_minibatch += h_x_labels.shape[0]
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full_count += h_x_labels.shape[0]
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# #####################################################
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# The network does the forward pass (training)
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# #####################################################
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h_collection = forward_pass_train(
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input=h_x,
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labels=h_x_labels,
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the_dataset_train=the_dataset_train,
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cfg=cfg,
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network=network,
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device=device,
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default_dtype=default_dtype,
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)
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# #####################################################
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# Calculate the loss function
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# #####################################################
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if isinstance(network[-1], SbSReconstruction) is False:
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my_loss: torch.Tensor | None = loss_function(
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h=h_collection[-1],
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labels=h_x_labels,
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device=device,
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default_dtype=default_dtype,
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loss_mode=cfg.learning_parameters.loss_mode,
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number_of_output_neurons=int(
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cfg.network_structure.number_of_output_neurons
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),
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loss_coeffs_mse=float(cfg.learning_parameters.loss_coeffs_mse),
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loss_coeffs_kldiv=float(cfg.learning_parameters.loss_coeffs_kldiv),
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)
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else:
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assert cfg.learning_parameters.lr_scheduler_use_performance is False
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my_loss = loss_function_reconstruction(
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h_reco=h_collection[-1],
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h_input=network[-2].last_input_data,
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loss_mode=cfg.learning_parameters.loss_mode,
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loss_coeffs_mse=float(cfg.learning_parameters.loss_coeffs_mse),
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loss_coeffs_kldiv=float(cfg.learning_parameters.loss_coeffs_kldiv),
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)
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assert my_loss is not None
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time_after_forward_and_loss: float = time.perf_counter()
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# #####################################################
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# Backward pass
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# #####################################################
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my_loss.backward()
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loss_in_minibatch += my_loss.item()
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full_loss += my_loss.item()
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time_after_backward: float = time.perf_counter()
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# #####################################################
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# Performance measures
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# #####################################################
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if isinstance(network[-1], SbSReconstruction) is False:
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correct_in_minibatch += (
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(h_collection[-1].argmax(dim=1).squeeze().cpu() == h_x_labels)
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.sum()
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.item()
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)
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full_correct += (
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(h_collection[-1].argmax(dim=1).squeeze().cpu() == h_x_labels)
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.sum()
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.item()
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)
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# We measure the scale of the propagated error
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# during the first minibatch
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# then we remember this size and scale
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# the future error with it
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# Kind of deals with the vanishing /
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# exploding gradients
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deal_with_gradient_scale(
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epoch_id=epoch_id,
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mini_batch_number=mini_batch_number,
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network=network,
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)
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# Measure the time for one mini-batch
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time_forward += time_after_forward_and_loss - time_mini_batch_start
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time_backward += time_after_backward - time_after_forward_and_loss
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if number_of_pattern_in_minibatch >= cfg.get_update_after_x_pattern():
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if isinstance(network[-1], SbSReconstruction) is False:
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logging.info(
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(
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f"{epoch_id:^6}=>{mini_batch_number:^6} "
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f"\t\tTraining {number_of_pattern_in_minibatch^6} pattern "
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f"with {correct_in_minibatch/number_of_pattern_in_minibatch:^6.2%} "
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f"\tForward time: \t{time_forward:^6.2f}sec"
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)
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)
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logging.info(
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(
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f"\t\t\tLoss: {loss_in_minibatch/number_of_pattern_in_minibatch:^15.3e} "
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f"\t\t\tBackward time: \t{time_backward:^6.2f}sec "
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)
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)
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else:
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logging.info(
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(
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f"{epoch_id:^6}=>{mini_batch_number:^6} "
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f"\t\tTraining {number_of_pattern_in_minibatch^6} pattern "
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f"\t\t\tForward time: \t{time_forward:^6.2f}sec"
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)
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)
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logging.info(
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(
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f"\t\t\tLoss: {loss_in_minibatch/number_of_pattern_in_minibatch:^15.3e} "
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f"\t\t\tBackward time: \t{time_backward:^6.2f}sec "
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)
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)
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my_loss_for_batch = loss_in_minibatch / number_of_pattern_in_minibatch
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performance_for_batch = (
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100.0 * correct_in_minibatch / number_of_pattern_in_minibatch
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)
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# ################################################
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# Update the weights and biases
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# ################################################
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|
run_optimizer(network=network, optimizer=optimizer, cfg=cfg)
|
|
|
|
# ################################################
|
|
# Save the Weights and Biases
|
|
# ################################################
|
|
save_weight_and_bias(
|
|
cfg=cfg,
|
|
network=network,
|
|
iteration_number=epoch_id,
|
|
order_id=order_id,
|
|
)
|
|
|
|
# ################################################
|
|
# Log the Weights and Biases
|
|
# ################################################
|
|
add_weight_and_bias_to_histogram(
|
|
network=network,
|
|
tb=tb,
|
|
iteration_number=epoch_id,
|
|
)
|
|
|
|
# ################################################
|
|
# Mark mini batch as done
|
|
# ################################################
|
|
number_of_pattern_in_minibatch = 0
|
|
|
|
return (
|
|
my_loss_for_batch,
|
|
performance_for_batch,
|
|
(full_loss / full_count),
|
|
(100.0 * full_correct / full_count),
|
|
)
|
|
|
|
|
|
def loop_test(
|
|
epoch_id: int,
|
|
cfg: Config,
|
|
network: torch.nn.modules.container.Sequential,
|
|
my_loader_test: torch.utils.data.dataloader.DataLoader,
|
|
the_dataset_test,
|
|
device: torch.device,
|
|
default_dtype: torch.dtype,
|
|
logging,
|
|
tb: SummaryWriter | None,
|
|
overwrite_number_of_spikes: int = -1,
|
|
) -> float:
|
|
|
|
test_correct = 0
|
|
test_count = 0
|
|
test_complete: int = the_dataset_test.__len__()
|
|
|
|
logging.info("")
|
|
logging.info("Testing:")
|
|
mini_batch_id: int = 0
|
|
|
|
for h_x, h_x_labels in my_loader_test:
|
|
time_0 = time.perf_counter()
|
|
|
|
h_collection = forward_pass_test(
|
|
input=h_x,
|
|
labels=h_x_labels,
|
|
the_dataset_test=the_dataset_test,
|
|
cfg=cfg,
|
|
network=network,
|
|
device=device,
|
|
default_dtype=default_dtype,
|
|
mini_batch_id=mini_batch_id,
|
|
overwrite_number_of_spikes=overwrite_number_of_spikes,
|
|
)
|
|
h_h: torch.Tensor = h_collection[-1].detach().clone().cpu()
|
|
|
|
test_correct += (h_h.argmax(dim=1).squeeze() == h_x_labels).sum().numpy()
|
|
test_count += h_h.shape[0]
|
|
performance = 100.0 * test_correct / test_count
|
|
time_1 = time.perf_counter()
|
|
time_measure_a = time_1 - time_0
|
|
|
|
logging.info(
|
|
(
|
|
f"\t\t{test_count} of {test_complete}"
|
|
f" with {performance/100:^6.2%} \t Time used: {time_measure_a:^6.2f}sec"
|
|
)
|
|
)
|
|
mini_batch_id += 1
|
|
|
|
logging.info("")
|
|
|
|
if tb is not None:
|
|
tb.add_scalar("Test Error", 100.0 - performance, epoch_id)
|
|
tb.flush()
|
|
|
|
return performance
|
|
|
|
|
|
def loop_test_mix(
|
|
epoch_id: int,
|
|
cfg: Config,
|
|
network: torch.nn.modules.container.Sequential,
|
|
my_loader_test: torch.utils.data.dataloader.DataLoader,
|
|
the_dataset_test,
|
|
device: torch.device,
|
|
default_dtype: torch.dtype,
|
|
logging,
|
|
tb: SummaryWriter | None,
|
|
overwrite_number_of_spikes: int = -1,
|
|
) -> tuple[float, float]:
|
|
|
|
test_correct_a_0: int = 0
|
|
test_correct_a_1: int = 0
|
|
test_correct_b_0: int = 0
|
|
test_correct_b_1: int = 0
|
|
|
|
test_count: int = 0
|
|
test_complete: int = the_dataset_test.__len__()
|
|
|
|
logging.info("")
|
|
logging.info("Testing:")
|
|
mini_batch_id: int = 0
|
|
|
|
for h_x, h_x_labels in my_loader_test:
|
|
assert len(h_x_labels) == 2
|
|
label_a = h_x_labels[0]
|
|
label_b = h_x_labels[1]
|
|
assert label_a.shape[0] == label_b.shape[0]
|
|
assert h_x.shape[0] == label_b.shape[0]
|
|
|
|
time_0 = time.perf_counter()
|
|
|
|
h_collection = forward_pass_test(
|
|
input=h_x,
|
|
labels=label_a,
|
|
the_dataset_test=the_dataset_test,
|
|
cfg=cfg,
|
|
network=network,
|
|
device=device,
|
|
default_dtype=default_dtype,
|
|
mini_batch_id=mini_batch_id,
|
|
overwrite_number_of_spikes=overwrite_number_of_spikes,
|
|
)
|
|
h_h: torch.Tensor = h_collection[-1].detach().clone().cpu()
|
|
|
|
# -------------
|
|
|
|
for id in range(0, h_h.shape[0]):
|
|
temp = h_h[id, ...].squeeze().argsort(descending=True)
|
|
test_correct_a_0 += float(label_a[id] == int(temp[0]))
|
|
test_correct_a_1 += float(label_a[id] == int(temp[1]))
|
|
test_correct_b_0 += float(label_b[id] == int(temp[0]))
|
|
test_correct_b_1 += float(label_b[id] == int(temp[1]))
|
|
|
|
test_count += h_h.shape[0]
|
|
performance_a_0: float = 100.0 * test_correct_a_0 / test_count
|
|
performance_a_1: float = 100.0 * test_correct_a_1 / test_count
|
|
performance_b_0: float = 100.0 * test_correct_b_0 / test_count
|
|
performance_b_1: float = 100.0 * test_correct_b_1 / test_count
|
|
time_1 = time.perf_counter()
|
|
time_measure_a = time_1 - time_0
|
|
|
|
logging.info(
|
|
(
|
|
f"\t\t{test_count} of {test_complete}"
|
|
f" with {performance_a_0/100:^6.2%}, "
|
|
f"{performance_a_1/100:^6.2%}, "
|
|
f"{performance_b_0/100:^6.2%}, "
|
|
f"{performance_b_1/100:^6.2%} \t "
|
|
f"Time used: {time_measure_a:^6.2f}sec"
|
|
)
|
|
)
|
|
mini_batch_id += 1
|
|
|
|
logging.info("")
|
|
|
|
if tb is not None:
|
|
tb.add_scalar("Test Error A0", 100.0 - performance_a_0, epoch_id)
|
|
tb.add_scalar("Test Error A1", 100.0 - performance_a_1, epoch_id)
|
|
tb.add_scalar("Test Error B0", 100.0 - performance_b_0, epoch_id)
|
|
tb.add_scalar("Test Error B1", 100.0 - performance_b_1, epoch_id)
|
|
tb.flush()
|
|
|
|
return performance_a_0, performance_a_1, performance_b_0, performance_b_1
|
|
|
|
|
|
def loop_test_reconstruction(
|
|
epoch_id: int,
|
|
cfg: Config,
|
|
network: torch.nn.modules.container.Sequential,
|
|
my_loader_test: torch.utils.data.dataloader.DataLoader,
|
|
the_dataset_test,
|
|
device: torch.device,
|
|
default_dtype: torch.dtype,
|
|
logging,
|
|
tb: SummaryWriter | None,
|
|
) -> float:
|
|
|
|
test_count: int = 0
|
|
test_loss: float = 0.0
|
|
test_complete: int = the_dataset_test.__len__()
|
|
|
|
logging.info("")
|
|
logging.info("Testing:")
|
|
|
|
for h_x, h_x_labels in my_loader_test:
|
|
time_0 = time.perf_counter()
|
|
|
|
h_collection = forward_pass_test(
|
|
input=h_x,
|
|
labels=None,
|
|
the_dataset_test=the_dataset_test,
|
|
cfg=cfg,
|
|
network=network,
|
|
device=device,
|
|
default_dtype=default_dtype,
|
|
)
|
|
|
|
my_loss: torch.Tensor | None = loss_function_reconstruction(
|
|
h_reco=h_collection[-1],
|
|
h_input=network[-2].last_input_data,
|
|
loss_mode=cfg.learning_parameters.loss_mode,
|
|
loss_coeffs_mse=float(cfg.learning_parameters.loss_coeffs_mse),
|
|
loss_coeffs_kldiv=float(cfg.learning_parameters.loss_coeffs_kldiv),
|
|
)
|
|
|
|
assert my_loss is not None
|
|
test_count += h_x_labels.shape[0]
|
|
test_loss += my_loss.item()
|
|
|
|
performance = test_loss / test_count
|
|
time_1 = time.perf_counter()
|
|
time_measure_a = time_1 - time_0
|
|
|
|
logging.info(
|
|
(
|
|
f"\t\t{test_count} of {test_complete}"
|
|
f" with {performance:^6.2e} \t Time used: {time_measure_a:^6.2f}sec"
|
|
)
|
|
)
|
|
|
|
logging.info("")
|
|
|
|
if tb is not None:
|
|
tb.add_scalar("Test Error", performance, epoch_id)
|
|
tb.flush()
|
|
|
|
return performance
|