311 lines
12 KiB
Python
311 lines
12 KiB
Python
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import re
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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from collections import OrderedDict
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from .utils import load_state_dict_from_url
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from torch import Tensor
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from typing import Any, List, Tuple
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__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
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model_urls = {
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'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
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'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
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'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
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'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
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}
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class _DenseLayer(nn.Module):
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def __init__(
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self,
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num_input_features: int,
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growth_rate: int,
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bn_size: int,
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drop_rate: float,
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memory_efficient: bool = False
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) -> None:
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super(_DenseLayer, self).__init__()
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self.norm1: nn.BatchNorm2d
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self.add_module('norm1', nn.BatchNorm2d(num_input_features))
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self.relu1: nn.ReLU
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self.add_module('relu1', nn.ReLU(inplace=True))
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self.conv1: nn.Conv2d
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self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
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growth_rate, kernel_size=1, stride=1,
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bias=False))
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self.norm2: nn.BatchNorm2d
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self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate))
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self.relu2: nn.ReLU
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self.add_module('relu2', nn.ReLU(inplace=True))
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self.conv2: nn.Conv2d
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self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
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kernel_size=3, stride=1, padding=1,
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bias=False))
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self.drop_rate = float(drop_rate)
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self.memory_efficient = memory_efficient
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def bn_function(self, inputs: List[Tensor]) -> Tensor:
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concated_features = torch.cat(inputs, 1)
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bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484
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return bottleneck_output
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# todo: rewrite when torchscript supports any
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def any_requires_grad(self, input: List[Tensor]) -> bool:
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for tensor in input:
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if tensor.requires_grad:
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return True
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return False
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@torch.jit.unused # noqa: T484
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def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
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def closure(*inputs):
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return self.bn_function(inputs)
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return cp.checkpoint(closure, *input)
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@torch.jit._overload_method # noqa: F811
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def forward(self, input: List[Tensor]) -> Tensor:
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pass
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@torch.jit._overload_method # noqa: F811
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def forward(self, input: Tensor) -> Tensor:
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pass
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# torchscript does not yet support *args, so we overload method
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# allowing it to take either a List[Tensor] or single Tensor
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def forward(self, input: Tensor) -> Tensor: # noqa: F811
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if isinstance(input, Tensor):
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prev_features = [input]
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else:
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prev_features = input
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if self.memory_efficient and self.any_requires_grad(prev_features):
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if torch.jit.is_scripting():
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raise Exception("Memory Efficient not supported in JIT")
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bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
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else:
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bottleneck_output = self.bn_function(prev_features)
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new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
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if self.drop_rate > 0:
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new_features = F.dropout(new_features, p=self.drop_rate,
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training=self.training)
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return new_features
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class _DenseBlock(nn.ModuleDict):
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_version = 2
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def __init__(
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self,
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num_layers: int,
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num_input_features: int,
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bn_size: int,
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growth_rate: int,
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drop_rate: float,
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memory_efficient: bool = False
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) -> None:
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer(
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num_input_features + i * growth_rate,
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growth_rate=growth_rate,
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bn_size=bn_size,
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drop_rate=drop_rate,
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memory_efficient=memory_efficient,
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)
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self.add_module('denselayer%d' % (i + 1), layer)
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def forward(self, init_features: Tensor) -> Tensor:
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features = [init_features]
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for name, layer in self.items():
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new_features = layer(features)
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features.append(new_features)
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return torch.cat(features, 1)
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class _Transition(nn.Sequential):
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def __init__(self, num_input_features: int, num_output_features: int) -> None:
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super(_Transition, self).__init__()
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self.add_module('norm', nn.BatchNorm2d(num_input_features))
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self.add_module('relu', nn.ReLU(inplace=True))
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self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
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kernel_size=1, stride=1, bias=False))
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self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
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class DenseNet(nn.Module):
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r"""Densenet-BC model class, based on
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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Args:
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growth_rate (int) - how many filters to add each layer (`k` in paper)
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block_config (list of 4 ints) - how many layers in each pooling block
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num_init_features (int) - the number of filters to learn in the first convolution layer
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bn_size (int) - multiplicative factor for number of bottle neck layers
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(i.e. bn_size * k features in the bottleneck layer)
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drop_rate (float) - dropout rate after each dense layer
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num_classes (int) - number of classification classes
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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"""
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def __init__(
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self,
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growth_rate: int = 32,
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block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
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num_init_features: int = 64,
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bn_size: int = 4,
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drop_rate: float = 0,
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num_classes: int = 1000,
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memory_efficient: bool = False
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) -> None:
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super(DenseNet, self).__init__()
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# First convolution
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self.features = nn.Sequential(OrderedDict([
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('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
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padding=3, bias=False)),
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('norm0', nn.BatchNorm2d(num_init_features)),
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('relu0', nn.ReLU(inplace=True)),
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('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
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]))
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# Each denseblock
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num_features = num_init_features
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for i, num_layers in enumerate(block_config):
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block = _DenseBlock(
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num_layers=num_layers,
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num_input_features=num_features,
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bn_size=bn_size,
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growth_rate=growth_rate,
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drop_rate=drop_rate,
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memory_efficient=memory_efficient
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)
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self.features.add_module('denseblock%d' % (i + 1), block)
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num_features = num_features + num_layers * growth_rate
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if i != len(block_config) - 1:
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trans = _Transition(num_input_features=num_features,
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num_output_features=num_features // 2)
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self.features.add_module('transition%d' % (i + 1), trans)
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num_features = num_features // 2
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# Final batch norm
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self.features.add_module('norm5', nn.BatchNorm2d(num_features))
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# Linear layer
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self.classifier = nn.Linear(num_features, num_classes)
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# Official init from torch repo.
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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)
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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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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.bias, 0)
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def forward(self, x: Tensor) -> Tensor:
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features = self.features(x)
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out = F.relu(features, inplace=True)
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out = F.adaptive_avg_pool2d(out, (1, 1))
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out = torch.flatten(out, 1)
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out = self.classifier(out)
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return out
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def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None:
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# '.'s are no longer allowed in module names, but previous _DenseLayer
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# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
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# They are also in the checkpoints in model_urls. This pattern is used
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# to find such keys.
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pattern = re.compile(
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r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
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state_dict = load_state_dict_from_url(model_url, progress=progress)
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for key in list(state_dict.keys()):
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res = pattern.match(key)
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if res:
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new_key = res.group(1) + res.group(2)
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state_dict[new_key] = state_dict[key]
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del state_dict[key]
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model.load_state_dict(state_dict)
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def _densenet(
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arch: str,
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growth_rate: int,
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block_config: Tuple[int, int, int, int],
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num_init_features: int,
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pretrained: bool,
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progress: bool,
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**kwargs: Any
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) -> DenseNet:
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model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
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if pretrained:
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_load_state_dict(model, model_urls[arch], progress)
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return model
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def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
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r"""Densenet-121 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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"""
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return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
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**kwargs)
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def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
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r"""Densenet-161 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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"""
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return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
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**kwargs)
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def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
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r"""Densenet-169 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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"""
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return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
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**kwargs)
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def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
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r"""Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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"""
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return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
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**kwargs)
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