232 lines
9.6 KiB
Python
232 lines
9.6 KiB
Python
# -*- coding: utf-8 -*-
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# @Author: Weisen Pan
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import torch
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import torch.nn as nn
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__all__ = ['ResNet']
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# Function to define a 3x3 convolution layer with padding
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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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# Function to define a 1x1 convolution layer
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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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# BasicBlock class for ResNet architecture
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class BasicBlock(nn.Module):
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expansion = 1 # Expansion factor
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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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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d # Default normalization layer is 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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# First convolution and batch normalization layer
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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) # ReLU activation
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# Second convolution and batch normalization layer
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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 # If downsample is provided, use it
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def forward(self, x):
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identity = x # Keep original input as identity for residual connection
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# Forward pass through first convolution, batch norm, and ReLU
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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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# Forward pass through second convolution and batch norm
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out = self.conv2(out)
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out = self.bn2(out)
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# Downsample the identity if downsample is provided
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if self.downsample is not None:
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identity = self.downsample(x)
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# Add residual connection (identity)
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out += identity
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out = self.relu(out) # Apply ReLU activation after addition
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return out
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# Bottleneck class for deeper ResNet architectures
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class Bottleneck(nn.Module):
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expansion = 4 # Expansion factor
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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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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d # Default normalization layer is BatchNorm2d
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width = int(out_channels * (base_width / 64.)) * groups # Calculate width based on group size
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# First 1x1 convolution
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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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# Second 3x3 convolution
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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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# Third 1x1 convolution to match output channels
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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) # ReLU activation
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self.downsample = downsample # Downsample if provided
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def forward(self, x):
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identity = x # Keep original input as identity for residual connection
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# First 1x1 convolution and ReLU
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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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# Second 3x3 convolution and ReLU
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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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# Third 1x1 convolution
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out = self.conv3(out)
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out = self.bn3(out)
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# Add downsampled identity if necessary
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if self.downsample is not None:
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identity = self.downsample(x)
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# Add residual connection (identity)
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out += identity
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out = self.relu(out) # Apply ReLU activation after addition
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return out
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# ResNet class to build the entire ResNet model
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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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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d # Default normalization layer
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self._norm_layer = norm_layer
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self.inplanes = 16 # Initial number of channels
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self.dilation = 1 # Dilation factor
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if replace_stride_with_dilation is None:
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replace_stride_with_dilation = [False, False, False] # Default stride behavior
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups # Number of groups for convolutions
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self.base_width = width_per_group # Base width for groups
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# Initial convolutional layer with 3 input channels (RGB image)
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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) # Batch normalization
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self.relu = nn.ReLU(inplace=True) # ReLU activation
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # Max pooling layer
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self.layer1 = self._create_model_layer(block, 16, layers[0]) # First block layer
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # Adaptive average pooling
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self.fc = nn.Linear(16 * block.expansion, num_classes) # Fully connected layer
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self.KD = KD # Knowledge Distillation flag
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for m in self.modules():
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# Initialize convolutional weights using He initialization
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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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# Initialize batch normalization weights
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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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# Zero-initialize the last batch norm layer if zero_init_residual is True
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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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# Helper function to create layers of blocks
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def _create_model_layer(self, block, planes, blocks, stride=1, dilate=False):
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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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# Forward pass of the ResNet model
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def forward(self, x):
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x = self.conv1(x) # Initial convolution
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x = self.bn1(x) # Batch normalization
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x = self.relu(x) # ReLU activation
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extracted_features = x # Feature extraction point
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x = self.layer1(x) # Pass through the first layer
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x = self.avgpool(x) # Adaptive average pooling
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x_f = x.view(x.size(0), -1) # Flatten the features
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logits = self.fc(x_f) # Fully connected layer for classification
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return logits, extracted_features # Return logits and extracted features
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# Function to create ResNet-5 model
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def resnet5_56(num_classes, models_pretrained=False, path=None, **kwargs):
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"""Constructs a ResNet-5 model."""
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model = ResNet(BasicBlock, [1, 2, 2], 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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from collections import OrderedDict
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = k.replace("module.", "")
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new_state_dict[name] = v
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model.load_state_dict(new_state_dict)
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return model
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# Function to create ResNet-8 model
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def resnet8_56(num_classes, models_pretrained=False, path=None, **kwargs):
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"""Constructs a ResNet-8 model."""
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model = ResNet(Bottleneck, [2, 2, 2], 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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from collections import OrderedDict
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = k.replace("module.", "")
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new_state_dict[name] = v
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model.load_state_dict(new_state_dict)
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return model
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