import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from collections import OrderedDict from torch import Tensor __all__ = [ 'DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161' ] class _DenseLayer(nn.Module): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False): super(_DenseLayer, self).__init__() self.add_module('norm1', nn.BatchNorm2d(num_input_features)), self.add_module('relu1', nn.ReLU(inplace=True)), self.add_module( 'conv1', nn.Conv2d(num_input_features, 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 = float(drop_rate) self.memory_efficient = memory_efficient def bn_function(self, inputs): concated_features = torch.cat(inputs, 1) bottleneck_output = self.conv1( self.relu1(self.norm1(concated_features))) # noqa: T484 return bottleneck_output # todo: rewrite when torchscript supports any def any_requires_grad(self, input): for tensor in input: if tensor.requires_grad: return True return False def call_checkpoint_bottleneck(self, input): def closure(*inputs): return self.bn_function(*inputs) return cp.checkpoint(closure, input) # allowing it to take either a List[Tensor] or single Tensor def forward(self, input): # noqa: F811 if isinstance(input, Tensor): prev_features = [input] else: prev_features = input 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 __constants__ = ['layers'] def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False): super(_DenseBlock, self).__init__() for i in range(num_layers): layer = _DenseLayer( num_input_features + 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): 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, num_input_features, num_output_features): super(_Transition, self).__init__() self.add_module('norm', nn.BatchNorm2d(num_input_features)) self.add_module('relu', nn.ReLU(inplace=True)) self.add_module( 'conv', nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False)) self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) class DenseNet(nn.Module): r"""Densenet-BC model class, based on `"Densely Connected Convolutional Networks" `_ 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, but slower. Default: *False*. See `"paper" `_ """ __constants__ = ['features'] def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False): super(DenseNet, self).__init__() # First convolution 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 denseblock num_features = num_init_features for i, num_layers in enumerate(block_config): block = _DenseBlock(num_layers=num_layers, num_input_features=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(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module('transition%d' % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module('norm5', nn.BatchNorm2d(num_features)) # Linear layer self.classifier = nn.Linear(num_features, num_classes) # Official init from torch repo. 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): 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 _densenet(arch, growth_rate, block_config, num_init_features, **kwargs): model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) return model def densenet121(**kwargs): r"""Densenet-121 model from `"Densely Connected Convolutional Networks" `_ Args: memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_ """ return _densenet('densenet121', 32, (6, 12, 24, 16), 64, **kwargs) def densenet161(**kwargs): r"""Densenet-161 model from `"Densely Connected Convolutional Networks" `_ Args: memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_ """ return _densenet('densenet161', 48, (6, 12, 36, 24), 96, **kwargs) def densenet169(**kwargs): r"""Densenet-169 model from `"Densely Connected Convolutional Networks" `_ Args: memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_ """ return _densenet('densenet169', 32, (6, 12, 32, 32), 64, **kwargs) def densenet201(**kwargs): r"""Densenet-201 model from `"Densely Connected Convolutional Networks" `_ Args: memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_ """ return _densenet('densenet201', 32, (6, 12, 48, 32), 64, **kwargs)