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import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.utils import spectral_norm
class _ConvBlock(nn.Module):
def __init__(self, pattern, in_channels, out_channels, kernel_size, stride, padding, use_weight_norm, dim, activation_type, nonlinearity_type):
# the default weight norm is spectral norm
# pattern: C for conv, N for activation norm(SyncBatchNorm), A for nonlinearity(ReLU)
super().__init__()
norm_channels = out_channels if pattern.find("C") < pattern.find("N") else in_channels
weight_norm = spectral_norm if use_weight_norm else lambda x: x
base_conv = nn.Conv2d if dim == 2 else nn.Conv3d
def _get_activation():
if activation_type == "batch":
return nn.SyncBatchNorm(norm_channels)
elif activation_type == "instance":
return nn.InstanceNorm2d(norm_channels, affine=True) if dim == 2 else nn.InstanceNorm3d(norm_channels, affine=True)
elif activation_type == "none":
return nn.Identity()
def _get_nonlinearity():
if nonlinearity_type == "relu":
return nn.ReLU(inplace=True)
elif nonlinearity_type == "leakyrelu":
return nn.LeakyReLU(0.2, inplace=True)
mappings = {
"C": weight_norm(base_conv(in_channels, out_channels, kernel_size, stride, padding)),
"N": _get_activation(),
"A": _get_nonlinearity(),
}
module_list = []
for c in pattern:
module_list.append(mappings[c])
self.layers = nn.Sequential(*module_list)
def forward(self, x):
return self.layers(x)
class ConvBlock2D(_ConvBlock):
def __init__(
self, pattern, in_channels, out_channels, kernel_size, stride, padding, use_weight_norm, activation_type="batch", nonlinearity_type="relu",
):
super().__init__(pattern, in_channels, out_channels, kernel_size, stride, padding, use_weight_norm, 2, activation_type, nonlinearity_type)
class ConvBlock3D(_ConvBlock):
def __init__(
self, pattern, in_channels, out_channels, kernel_size, stride, padding, use_weight_norm, activation_type="batch", nonlinearity_type="relu",
):
super().__init__(pattern, in_channels, out_channels, kernel_size, stride, padding, use_weight_norm, 3, activation_type, nonlinearity_type)
class _DownBlock(nn.Module):
def __init__(self, in_channels, out_channels, use_weight_norm, base_conv, base_pooling, kernel_size):
super().__init__()
self.layers = nn.Sequential(base_conv("CNA", in_channels, out_channels, kernel_size=3, stride=1, padding=1, use_weight_norm=use_weight_norm), base_pooling(kernel_size))
def forward(self, x):
return self.layers(x)
class DownBlock2D(_DownBlock):
def __init__(self, in_channels, out_channels, use_weight_norm):
super().__init__(in_channels, out_channels, use_weight_norm, ConvBlock2D, nn.AvgPool2d, (2, 2))
class DownBlock3D(_DownBlock):
def __init__(self, in_channels, out_channels, use_weight_norm):
super().__init__(in_channels, out_channels, use_weight_norm, ConvBlock3D, nn.AvgPool3d, (1, 2, 2))
class _UpBlock(nn.Module):
def __init__(self, in_channels, out_channels, use_weight_norm, base_conv, scale_factor):
super().__init__()
self.layers = nn.Sequential(nn.Upsample(scale_factor=scale_factor), base_conv("CNA", in_channels, out_channels, 3, 1, 1, use_weight_norm))
def forward(self, x):
return self.layers(x)
class UpBlock2D(_UpBlock):
def __init__(self, in_channels, out_channels, use_weight_norm):
super().__init__(in_channels, out_channels, use_weight_norm, ConvBlock2D, (2, 2))
class UpBlock3D(_UpBlock):
def __init__(self, in_channels, out_channels, use_weight_norm):
super().__init__(in_channels, out_channels, use_weight_norm, ConvBlock3D, (1, 2, 2))
class _ResBlock(nn.Module):
def __init__(self, in_channels, use_weight_norm, base_block):
super().__init__()
self.layers = nn.Sequential(
base_block("NAC", in_channels, in_channels, 3, 1, 1, use_weight_norm),
base_block("NAC", in_channels, in_channels, 3, 1, 1, use_weight_norm),
)
def forward(self, x):
return x + self.layers(x)
class ResBlock2D(_ResBlock):
def __init__(self, in_channels, use_weight_norm):
super().__init__(in_channels, use_weight_norm, ConvBlock2D)
class ResBlock3D(_ResBlock):
def __init__(self, in_channels, use_weight_norm):
super().__init__(in_channels, use_weight_norm, ConvBlock3D)
class ResBottleneck(nn.Module):
def __init__(self, in_channels, out_channels, stride, use_weight_norm):
super().__init__()
self.down_sample = nn.Identity()
if stride != 1 or in_channels != out_channels:
self.down_sample = ConvBlock2D("CN", in_channels, out_channels, 1, stride, 0, use_weight_norm)
self.layers = nn.Sequential(
ConvBlock2D("CNA", in_channels, out_channels // 4, 1, 1, 0, use_weight_norm),
ConvBlock2D("CNA", out_channels // 4, out_channels // 4, 3, stride, 1, use_weight_norm),
ConvBlock2D("CN", out_channels // 4, out_channels, 1, 1, 0, use_weight_norm),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu(self.down_sample(x) + self.layers(x))
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