53 lines
2.5 KiB
Python
53 lines
2.5 KiB
Python
from typing import Union, Tuple
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import torch
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import torch.nn as nn
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from .layers import Conv2d, LayerInfo, Layer
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class ResBlock(Layer):
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def __init__(self, in_channels: int, out_channels: int = -1, kernel_size: int = 3, padding: int = 1,
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stride: Union[int, Tuple[int, int]] = 1, activation=None, batch_norm=None, **kwargs):
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super().__init__(activation if activation is not None else 0, False)
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self.batch_norm = None
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if out_channels == -1:
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out_channels = in_channels
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self.seq = nn.Sequential(
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Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=stride, padding=padding, **kwargs),
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Conv2d(in_channels, out_channels, kernel_size=3, padding=1,
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activation=None, batch_norm=batch_norm))
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self.residual = Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, activation=None) if (
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out_channels != in_channels or stride != 1) else None
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def forward(self, input_data: torch.Tensor) -> torch.Tensor:
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if self.residual is not None:
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return super().forward(self.residual(input_data) + self.seq(input_data))
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return super().forward(input_data + self.seq(input_data))
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class ResBottleneck(Layer):
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def __init__(self, in_channels: int, out_channels: int = -1, bottleneck_channels: int = -1, kernel_size: int = 3,
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stride: Union[int, Tuple[int, int]] = 1, padding=1,
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activation=None, batch_norm=None, **kwargs):
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super().__init__(activation if activation is not None else 0, False)
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self.batch_norm = None
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if out_channels == -1:
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out_channels = in_channels
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if bottleneck_channels == -1:
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bottleneck_channels = in_channels // 4
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self.seq = nn.Sequential(
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Conv2d(in_channels, bottleneck_channels, kernel_size=1),
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Conv2d(bottleneck_channels, bottleneck_channels, kernel_size=kernel_size,
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stride=stride, padding=padding, **kwargs),
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Conv2d(bottleneck_channels, out_channels, kernel_size=1,
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activation=None, batch_norm=batch_norm))
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self.residual = Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, activation=None) if (
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out_channels != in_channels or stride != 1) else None
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def forward(self, input_data: torch.Tensor) -> torch.Tensor:
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if self.residual is not None:
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return super().forward(self.residual(input_data) + self.seq(input_data))
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return super().forward(input_data + self.seq(input_data))
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