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from __future__ import annotations |
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from collections.abc import Sequence |
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import torch |
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import torch.nn as nn |
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from monai.networks.blocks.convolutions import Convolution |
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from monai.networks.layers import same_padding |
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from monai.networks.layers.factories import Conv |
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class SimpleASPP(nn.Module): |
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""" |
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A simplified version of the atrous spatial pyramid pooling (ASPP) module. |
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Chen et al., Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. |
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https://arxiv.org/abs/1802.02611 |
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Wang et al., A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions |
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from CT Images. https://ieeexplore.ieee.org/document/9109297 |
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""" |
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def __init__( |
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self, |
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spatial_dims: int, |
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in_channels: int, |
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conv_out_channels: int, |
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kernel_sizes: Sequence[int] = (1, 3, 3, 3), |
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dilations: Sequence[int] = (1, 2, 4, 6), |
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norm_type: tuple | str | None = "BATCH", |
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acti_type: tuple | str | None = "LEAKYRELU", |
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bias: bool = False, |
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) -> None: |
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""" |
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Args: |
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spatial_dims: number of spatial dimensions, could be 1, 2, or 3. |
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in_channels: number of input channels. |
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conv_out_channels: number of output channels of each atrous conv. |
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The final number of output channels is conv_out_channels * len(kernel_sizes). |
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kernel_sizes: a sequence of four convolutional kernel sizes. |
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Defaults to (1, 3, 3, 3) for four (dilated) convolutions. |
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dilations: a sequence of four convolutional dilation parameters. |
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Defaults to (1, 2, 4, 6) for four (dilated) convolutions. |
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norm_type: final kernel-size-one convolution normalization type. |
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Defaults to batch norm. |
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acti_type: final kernel-size-one convolution activation type. |
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Defaults to leaky ReLU. |
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bias: whether to have a bias term in convolution blocks. Defaults to False. |
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According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_, |
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if a conv layer is directly followed by a batch norm layer, bias should be False. |
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Raises: |
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ValueError: When ``kernel_sizes`` length differs from ``dilations``. |
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See also: |
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:py:class:`monai.networks.layers.Act` |
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:py:class:`monai.networks.layers.Conv` |
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:py:class:`monai.networks.layers.Norm` |
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""" |
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super().__init__() |
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if len(kernel_sizes) != len(dilations): |
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raise ValueError( |
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"kernel_sizes and dilations length must match, " |
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f"got kernel_sizes={len(kernel_sizes)} dilations={len(dilations)}." |
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) |
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pads = tuple(same_padding(k, d) for k, d in zip(kernel_sizes, dilations)) |
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self.convs = nn.ModuleList() |
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for k, d, p in zip(kernel_sizes, dilations, pads): |
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_conv = Conv[Conv.CONV, spatial_dims]( |
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in_channels=in_channels, out_channels=conv_out_channels, kernel_size=k, dilation=d, padding=p |
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) |
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self.convs.append(_conv) |
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out_channels = conv_out_channels * len(pads) |
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self.conv_k1 = Convolution( |
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spatial_dims=spatial_dims, |
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in_channels=out_channels, |
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out_channels=out_channels, |
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kernel_size=1, |
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act=acti_type, |
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norm=norm_type, |
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bias=bias, |
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) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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""" |
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Args: |
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x: in shape (batch, channel, spatial_1[, spatial_2, ...]). |
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""" |
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x_out = torch.cat([conv(x) for conv in self.convs], dim=1) |
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x_out = self.conv_k1(x_out) |
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return x_out |
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