DenseNet实现乳腺癌识别

    import re
    from typing import Any, List, Tuple
    from collections import OrderedDict
    
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.checkpoint as cp
    from torch import Tensor
    
    
    class _DenseLayer(nn.Module):
    def __init__(self,
                 input_c: int,
                 growth_rate: int,
                 bn_size: int,
                 drop_rate: float,
                 memory_efficient: bool = False):
        super(_DenseLayer, self).__init__()
    
        self.add_module("norm1", nn.BatchNorm2d(input_c))
        self.add_module("relu1", nn.ReLU(inplace=True))
        self.add_module("conv1", nn.Conv2d(in_channels=input_c,
                                           out_channels=bn_size * growth_rate,
                                           kernel_size=1,
                                           stride=1,
                                           bias=False))
        self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate))
        self.add_module("relu2", nn.ReLU(inplace=True))
        self.add_module("conv2", nn.Conv2d(bn_size * growth_rate,
                                           growth_rate,
                                           kernel_size=3,
                                           stride=1,
                                           padding=1,
                                           bias=False))
        self.drop_rate = drop_rate
        self.memory_efficient = memory_efficient
    
    def bn_function(self, inputs: List[Tensor]) -> Tensor:
        concat_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(self.relu1(self.norm1(concat_features)))
        return bottleneck_output
    
    @staticmethod
    def any_requires_grad(inputs: List[Tensor]) -> bool:
        for tensor in inputs:
            if tensor.requires_grad:
                return True
    
        return False
    
    @torch.jit.unused
    def call_checkpoint_bottleneck(self, inputs: List[Tensor]) -> Tensor:
        def closure(*inp):
            return self.bn_function(inp)
    
        return cp.checkpoint(closure, *inputs)
    
    def forward(self, inputs: Tensor) -> Tensor:
        if isinstance(inputs, Tensor):
            prev_features = [inputs]
        else:
            prev_features = inputs
    
        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("memory efficient not supported in JIT")
    
            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)
    
        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features,
                                     p=self.drop_rate,
                                     training=self.training)
    
        return new_features
    
    
    class _DenseBlock(nn.ModuleDict):
    _version = 2
    
    def __init__(self,
                 num_layers: int,
                 input_c: int,
                 bn_size: int,
                 growth_rate: int,
                 drop_rate: float,
                 memory_efficient: bool = False):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(input_c + i * growth_rate,
                                growth_rate=growth_rate,
                                bn_size=bn_size,
                                drop_rate=drop_rate,
                                memory_efficient=memory_efficient)
            self.add_module("denselayer%d" % (i + 1), layer)
    
    def forward(self, init_features: Tensor) -> Tensor:
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)
    
    
    class _Transition(nn.Sequential):
    def __init__(self,
                 input_c: int,
                 output_c: int):
        super(_Transition, self).__init__()
        self.add_module("norm", nn.BatchNorm2d(input_c))
        self.add_module("relu", nn.ReLU(inplace=True))
        self.add_module("conv", nn.Conv2d(input_c,
                                          output_c,
                                          kernel_size=1,
                                          stride=1,
                                          bias=False))
        self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))
    
    
    class DenseNet(nn.Module):
    """
    Densenet-BC model class for imagenet
    
    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient
    """
    
    def __init__(self,
                 growth_rate: int = 32,
                 block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
                 num_init_features: int = 64,
                 bn_size: int = 4,
                 drop_rate: float = 0,
                 num_classes: int = 1000,
                 memory_efficient: bool = False):
        super(DenseNet, self).__init__()
    
        # first conv+bn+relu+pool
        self.features = nn.Sequential(OrderedDict([
            ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ("norm0", nn.BatchNorm2d(num_init_features)),
            ("relu0", nn.ReLU(inplace=True)),
            ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))
    
        # each dense block
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(num_layers=num_layers,
                                input_c=num_features,
                                bn_size=bn_size,
                                growth_rate=growth_rate,
                                drop_rate=drop_rate,
                                memory_efficient=memory_efficient)
            self.features.add_module("denseblock%d" % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
    
            if i != len(block_config) - 1:
                trans = _Transition(input_c=num_features,
                                    output_c=num_features // 2)
                self.features.add_module("transition%d" % (i + 1), trans)
                num_features = num_features // 2
    
        # finnal batch norm
        self.features.add_module("norm5", nn.BatchNorm2d(num_features))
    
        # fc layer
        self.classifier = nn.Linear(num_features, num_classes)
    
        # init weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)
    
    def forward(self, x: Tensor) -> Tensor:
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
        out = self.classifier(out)
        return out
    
    
    def densenet121(**kwargs: Any) -> DenseNet:
    # Top-1 error: 25.35%
    # 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth'
    return DenseNet(growth_rate=32,
                    block_config=(6, 12, 24, 16),
                    num_init_features=64,
                    **kwargs)
    
    
    def densenet169(**kwargs: Any) -> DenseNet:
    # Top-1 error: 24.00%
    # 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth'
    return DenseNet(growth_rate=32,
                    block_config=(6, 12, 32, 32),
                    num_init_features=64,
                    **kwargs)
    
    
    def densenet201(**kwargs: Any) -> DenseNet:
    # Top-1 error: 22.80%
    # 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth'
    return DenseNet(growth_rate=32,
                    block_config=(6, 12, 48, 32),
                    num_init_features=64,
                    **kwargs)
    
    
    def densenet161(**kwargs: Any) -> DenseNet:
    # Top-1 error: 22.35%
    # 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth'
    return DenseNet(growth_rate=48,
                    block_config=(6, 12, 36, 24),
                    num_init_features=96,
                    **kwargs)
    
    
    def load_state_dict(model: nn.Module, weights_path: str) -> None:
    # '.'s are no longer allowed in module names, but previous _DenseLayer
    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    # They are also in the checkpoints in model_urls. This pattern is used
    # to find such keys.
    pattern = re.compile(
        r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
    
    state_dict = torch.load(weights_path)
    
    num_classes = model.classifier.out_features
    load_fc = num_classes == 1000
    
    for key in list(state_dict.keys()):
        if load_fc is False:
            if "classifier" in key:
                del state_dict[key]
    
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            state_dict[new_key] = state_dict[key]
            del state_dict[key]
    model.load_state_dict(state_dict, strict=load_fc)
    print("successfully load pretrain-weights.")