import re from collections import OrderedDict from typing import Any, List, Tuple 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.")