197 lines
7.5 KiB
Python
197 lines
7.5 KiB
Python
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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import logging
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import torch
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import torch.nn as nn
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__all__ = ['ResNet', 'resnet110']
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def apply_3x3_convolution(in_channels, out_channels, stride=1, groups=1, dilation=1):
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"""3x3 Convolution with padding."""
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return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation)
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def apply_1x1_convolution(in_channels, out_channels, stride=1):
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"""1x1 Convolution."""
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return nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_channels, out_channels, stride=1, downsample=None, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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"""Basic Block used in ResNet. Consists of two 3x3 convolutions."""
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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self.conv1 = apply_3x3_convolution(in_channels, out_channels, stride)
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self.bn1 = norm_layer(out_channels)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = apply_3x3_convolution(out_channels, out_channels)
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self.bn2 = norm_layer(out_channels)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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"""Defines the forward pass through the block."""
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, in_channels, out_channels, stride=1, downsample=None, groups=1,
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base_width=64, dilation=1, norm_layer=None):
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"""Bottleneck block used in ResNet. Has three layers: 1x1, 3x3, and 1x1 convolutions."""
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(out_channels * (base_width / 64.)) * groups
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self.conv1 = apply_1x1_convolution(in_channels, width)
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self.bn1 = norm_layer(width)
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self.conv2 = apply_3x3_convolution(width, width, stride, groups, dilation)
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self.bn2 = norm_layer(width)
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self.conv3 = apply_1x1_convolution(width, out_channels * self.expansion)
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self.bn3 = norm_layer(out_channels * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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"""Defines the forward pass through the bottleneck block."""
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, layers, num_classes=10, zero_init_residual=False, groups=1,
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width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, KD=False):
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"""Defines the ResNet architecture."""
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.inplanes = 16
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self.dilation = 1
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if replace_stride_with_dilation is None:
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None or a 3-element tuple.")
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._create_model_layer(block, 16, layers[0])
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self.layer2 = self._create_model_layer(block, 32, layers[1], stride=2)
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self.layer3 = self._create_model_layer(block, 64, layers[2], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(64 * block.expansion, num_classes)
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self.KD = KD
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0)
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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def _create_model_layer(self, block, planes, blocks, stride=1, dilate=False):
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"""Creates a layer in ResNet using the specified block type."""
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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apply_1x1_convolution(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer))
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return nn.Sequential(*layers)
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def forward(self, x):
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"""Defines the forward pass of the ResNet model."""
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x = self.layer1(x) # Output: B x 16 x 32 x 32
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x = self.layer2(x) # Output: B x 32 x 16 x 16
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x = self.layer3(x) # Output: B x 64 x 8 x 8
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x = self.avgpool(x) # Output: B x 64 x 1 x 1
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x_f = x.view(x.size(0), -1) # Flatten: B x 64
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x = self.fc(x_f) # Output: B x num_classes
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return x
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def resnet56_server(num_classes, models_pretrained=False, path=None, **kwargs):
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"""
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Constructs a ResNet-110 model.
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Args:
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num_classes (int): Number of output classes.
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models_pretrained (bool): If True, returns a model pre-trained on ImageNet.
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path (str): Path to the pre-trained model.
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"""
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logging.info("Loading model with path: " + str(path))
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model = ResNet(Bottleneck, [6, 6, 6], num_classes=num_classes, **kwargs)
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if models_pretrained:
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checkpoint = torch.load(path)
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state_dict = checkpoint['state_dict']
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new_state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
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model.load_state_dict(new_state_dict)
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return model
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