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resnet.py
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228 lines (154 loc) · 6.08 KB
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import torch
import torch.nn as nn
class ConvBlock(nn.Module):
def __init__(self, in_filters, out_filters, kernel_size, stride, padding):
super().__init__()
self.expansion = 4
self.conv = nn.Conv2d(in_filters, out_filters, kernel_size=kernel_size, stride=stride, padding=padding)
self.bn = nn.BatchNorm2d(out_filters)
self.relu = nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class IdentityBlock(nn.Module):
def __init__(self, in_filters, out_filters, deep=True):
super().__init__()
self.deep = deep
if self.deep is True:
self.conv1 = ConvBlock(in_filters, out_filters, kernel_size=1, stride=1, padding=0)
self.conv2 = ConvBlock(out_filters, out_filters, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(out_filters, out_filters*4, kernel_size=1, stride=1, padding=0)
self.bn = nn.BatchNorm2d(out_filters*4)
else:
self.conv4 = ConvBlock(in_filters, out_filters, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.Conv2d(out_filters, out_filters, kernel_size=3, stride=1, padding=1)
self.bn_shallow = nn.BatchNorm2d(out_filters)
self.relu = nn.ReLU()
def forward(self, x):
identity = x
if self.deep is False:
x = self.conv4(x)
x = self.conv5(x)
x = self.bn_shallow(x)
x += identity
x = self.relu(x)
return x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.bn(x)
x += identity
x = self.relu(x)
return x
class IdentityConvBlock(nn.Module):
def __init__(self, in_filters, out_filters, stride, deep=True):
super().__init__()
self.deep = deep
if self.deep is True:
self.expansion = 4
self.conv1 = ConvBlock(in_filters, out_filters, kernel_size=1, stride=1, padding=0)
self.conv2 = ConvBlock(out_filters, out_filters, kernel_size=3, stride=stride, padding=1)
self.conv3 = nn.Conv2d(out_filters, out_filters*self.expansion, kernel_size=1, stride=1, padding=0)
self.bn = nn.BatchNorm2d(out_filters*self.expansion)
self.identityConv = nn.Sequential(
nn.Conv2d(in_filters, out_filters*4, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_filters*4)
)
else:
self.conv4 = ConvBlock(in_filters, out_filters, kernel_size=3, stride=stride, padding=1)
self.conv5 = nn.Conv2d(out_filters, out_filters, kernel_size=3, stride=1, padding=1)
self.bn_shallow = nn.BatchNorm2d(out_filters)
self.identityConv_shallow = nn.Sequential(
nn.Conv2d(in_filters, out_filters, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_filters)
)
self.relu = nn.ReLU()
def forward(self, x):
identity = x
if self.deep is False:
x = self.conv4(x)
x = self.conv5(x)
x = self.bn_shallow(x)
identity = self.identityConv_shallow(identity)
x += identity
x = self.relu(x)
return x
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.bn(x)
identity = self.identityConv(identity)
x += identity
x = self.relu(x)
return x
class ResNet(nn.Module):
def __init__(self, num_blocks, image_channels=3, num_classes=10, deep=True):
super().__init__()
self.conv1 = ConvBlock(image_channels, 64, kernel_size=7, stride=2, padding=3)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
if deep is False:
self.block1 = self._construct_residual_block_shallow(64, 64, num_blocks[0], stride=1)
self.block2 = self._construct_residual_block_shallow(64, 128, num_blocks[1], stride=2)
self.block3 = self._construct_residual_block_shallow(128, 256, num_blocks[2], stride=2)
self.block4 = self._construct_residual_block_shallow(256, 512, num_blocks[3], stride=2)
self.fc = nn.Linear(512, num_classes)
else:
self.block1 = self._construct_residual_block(64, 64, num_blocks[0], stride=1)
self.block2 = self._construct_residual_block(256, 128, num_blocks[1], stride=2)
self.block3 = self._construct_residual_block(512, 256, num_blocks[2], stride=2)
self.block4 = self._construct_residual_block(1024, 512, num_blocks[3], stride=2)
self.fc = nn.Linear(512 * 4, num_classes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
def _construct_residual_block(self, input_filter_size, output_filter_size, num_layers, stride):
layers = [IdentityConvBlock(in_filters=input_filter_size, out_filters=output_filter_size, stride=stride)]
for _ in range(num_layers-1):
layers.append(IdentityBlock(in_filters=output_filter_size*4, out_filters=output_filter_size))
return nn.Sequential(*layers)
def _construct_residual_block_shallow(self, input_filter_size, output_filter_size, num_layers, stride):
layers = [IdentityConvBlock(in_filters=input_filter_size, out_filters=output_filter_size, stride=stride, deep=False)]
for _ in range(num_layers-1):
layers.append(IdentityBlock(in_filters=output_filter_size, out_filters=output_filter_size, deep=False))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.avgpool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc(x)
return x
def resnet18(num_blocks=[2, 2, 2, 2], num_classes=10):
return ResNet(num_blocks, num_classes=num_classes, deep=False)
def resnet34(num_blocks=[3, 4, 6, 3], num_classes=10):
return ResNet(num_blocks, num_classes=num_classes, deep=False)
def resnet50(num_blocks=[3, 4, 6, 3], num_classes=10):
return ResNet(num_blocks, num_classes=num_classes)
def resnet101(num_blocks=[3, 4, 23, 3], num_classes=10):
return ResNet(num_blocks, num_classes=num_classes)
def resnet152(num_blocks=[3, 8, 36, 3], num_classes=10):
return ResNet(num_blocks, num_classes=num_classes)
def test():
device = 'cuda' # or 'cpu'
model_18 = resnet18().to(device)
model_34 = resnet34().to(device)
model_50 = resnet50().to(device)
model_101 = resnet101().to(device)
model_152 = resnet152().to(device)
x = torch.randn(2, 3, 224, 224).to(device)
out_18 = model_18(x)
out_34 = model_34(x)
out_50 = model_50(x)
out_101 = model_101(x)
out_152 = model_152(x)
print(out_18.shape)
print(out_34.shape)
print(out_50.shape)
print(out_101.shape)
print(out_152.shape)
if __name__ == '__main__':
test()