149 lines
4.6 KiB
Python
149 lines
4.6 KiB
Python
import torch
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from L1NormLayer import L1NormLayer
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from NNMF2d import NNMF2d
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from append_parameter import append_parameter
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def append_block(
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network: torch.nn.Sequential,
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out_channels: int,
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test_image: torch.Tensor,
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parameter_cnn_top: list[torch.nn.parameter.Parameter],
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parameter_nnmf: list[torch.nn.parameter.Parameter],
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parameter_norm: list[torch.nn.parameter.Parameter],
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torch_device: torch.device,
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dilation: tuple[int, int] | int = 1,
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padding: tuple[int, int] | int = 0,
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stride: tuple[int, int] | int = 1,
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kernel_size: tuple[int, int] = (5, 5),
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epsilon: float | None = None,
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positive_function_type: int = 0,
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beta: float | None = None,
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iterations: int = 20,
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local_learning: bool = False,
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local_learning_kl: bool = False,
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momentum: float = 0.1,
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track_running_stats: bool = False,
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) -> torch.Tensor:
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kernel_size_internal: list[int] = [kernel_size[-2], kernel_size[-1]]
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if kernel_size[0] < 1:
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kernel_size_internal[0] = test_image.shape[-2]
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if kernel_size[1] < 1:
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kernel_size_internal[1] = test_image.shape[-1]
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# Main
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network.append(torch.nn.ReLU())
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test_image = network[-1](test_image)
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# I need the output size
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mock_output = (
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torch.nn.functional.conv2d(
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torch.zeros(
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1,
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1,
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test_image.shape[2],
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test_image.shape[3],
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),
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torch.zeros((1, 1, kernel_size_internal[0], kernel_size_internal[1])),
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stride=stride,
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padding=padding,
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dilation=dilation,
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)
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.squeeze(0)
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.squeeze(0)
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)
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network.append(
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torch.nn.Unfold(
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kernel_size=(kernel_size_internal[-2], kernel_size_internal[-1]),
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dilation=dilation,
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padding=padding,
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stride=stride,
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)
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)
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test_image = network[-1](test_image)
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network.append(
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torch.nn.Fold(
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output_size=mock_output.shape,
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kernel_size=(1, 1),
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dilation=1,
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padding=0,
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stride=1,
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)
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)
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test_image = network[-1](test_image)
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network.append(L1NormLayer())
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test_image = network[-1](test_image)
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network.append(
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NNMF2d(
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in_channels=test_image.shape[1],
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out_channels=out_channels,
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epsilon=epsilon,
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positive_function_type=positive_function_type,
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beta=beta,
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iterations=iterations,
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local_learning=local_learning,
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local_learning_kl=local_learning_kl,
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).to(torch_device)
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)
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test_image = network[-1](test_image)
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append_parameter(module=network[-1], parameter_list=parameter_nnmf)
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if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1):
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network.append(
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torch.nn.BatchNorm2d(
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num_features=test_image.shape[1],
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momentum=momentum,
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track_running_stats=track_running_stats,
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device=torch_device,
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)
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)
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test_image = network[-1](test_image)
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append_parameter(module=network[-1], parameter_list=parameter_norm)
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network.append(
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torch.nn.Conv2d(
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in_channels=test_image.shape[1],
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out_channels=out_channels,
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kernel_size=(1, 1),
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stride=(1, 1),
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padding=(0, 0),
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bias=True,
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device=torch_device,
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)
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)
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# Init the cnn top layers 1x1 conv2d layers
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for name, param in network[-1].named_parameters():
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with torch.no_grad():
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if name == "bias":
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param.data *= 0
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if name == "weight":
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assert param.shape[-2] == 1
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assert param.shape[-1] == 1
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param[: param.shape[0], : param.shape[0], 0, 0] = torch.eye(
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param.shape[0], dtype=param.dtype, device=param.device
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)
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param[param.shape[0] :, :, 0, 0] = 0
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param[:, param.shape[0] :, 0, 0] = 0
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test_image = network[-1](test_image)
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append_parameter(module=network[-1], parameter_list=parameter_cnn_top)
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if (test_image.shape[-1] > 1) or (test_image.shape[-2] > 1):
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network.append(
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torch.nn.BatchNorm2d(
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num_features=test_image.shape[1],
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device=torch_device,
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momentum=momentum,
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track_running_stats=track_running_stats,
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)
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)
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test_image = network[-1](test_image)
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append_parameter(module=network[-1], parameter_list=parameter_norm)
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return test_image
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