449 lines
16 KiB
Python
449 lines
16 KiB
Python
import re
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from collections import OrderedDict
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from functools import partial
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from typing import Any, List, Optional, Tuple
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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 torch import Tensor
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from ..transforms._presets import ImageClassification
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from ..utils import _log_api_usage_once
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from ._api import register_model, Weights, WeightsEnum
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from ._meta import _IMAGENET_CATEGORIES
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from ._utils import _ovewrite_named_param, handle_legacy_interface
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__all__ = [
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"DenseNet",
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"DenseNet121_Weights",
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"DenseNet161_Weights",
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"DenseNet169_Weights",
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"DenseNet201_Weights",
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"densenet121",
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"densenet161",
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"densenet169",
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"densenet201",
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]
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class _DenseLayer(nn.Module):
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def __init__(
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self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False
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) -> None:
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super().__init__()
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self.norm1 = nn.BatchNorm2d(num_input_features)
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self.relu1 = nn.ReLU(inplace=True)
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self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)
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self.norm2 = nn.BatchNorm2d(bn_size * growth_rate)
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self.relu2 = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, 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, use_reentrant=False)
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@torch.jit._overload_method # noqa: F811
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def forward(self, input: List[Tensor]) -> Tensor: # noqa: F811
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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: # noqa: F811
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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, 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().__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().__init__()
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self.norm = nn.BatchNorm2d(num_input_features)
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self.relu = nn.ReLU(inplace=True)
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self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False)
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self.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().__init__()
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_log_api_usage_once(self)
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# First convolution
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self.features = nn.Sequential(
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OrderedDict(
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[
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("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, 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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)
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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, 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, weights: WeightsEnum, 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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)
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state_dict = weights.get_state_dict(progress=progress, check_hash=True)
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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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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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weights: Optional[WeightsEnum],
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progress: bool,
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**kwargs: Any,
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) -> DenseNet:
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if weights is not None:
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
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model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
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if weights is not None:
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_load_state_dict(model=model, weights=weights, progress=progress)
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return model
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_COMMON_META = {
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"min_size": (29, 29),
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"categories": _IMAGENET_CATEGORIES,
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"recipe": "https://github.com/pytorch/vision/pull/116",
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"_docs": """These weights are ported from LuaTorch.""",
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}
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class DenseNet121_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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**_COMMON_META,
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"num_params": 7978856,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 74.434,
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"acc@5": 91.972,
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}
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},
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"_ops": 2.834,
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"_file_size": 30.845,
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},
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)
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DEFAULT = IMAGENET1K_V1
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class DenseNet161_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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**_COMMON_META,
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"num_params": 28681000,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 77.138,
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"acc@5": 93.560,
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}
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},
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"_ops": 7.728,
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"_file_size": 110.369,
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},
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)
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DEFAULT = IMAGENET1K_V1
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class DenseNet169_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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**_COMMON_META,
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"num_params": 14149480,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 75.600,
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"acc@5": 92.806,
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}
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},
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"_ops": 3.36,
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"_file_size": 54.708,
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},
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)
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DEFAULT = IMAGENET1K_V1
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class DenseNet201_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/densenet201-c1103571.pth",
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transforms=partial(ImageClassification, crop_size=224),
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meta={
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**_COMMON_META,
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"num_params": 20013928,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 76.896,
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"acc@5": 93.370,
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}
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},
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"_ops": 4.291,
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"_file_size": 77.373,
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},
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)
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DEFAULT = IMAGENET1K_V1
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1))
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def densenet121(*, weights: Optional[DenseNet121_Weights] = None, 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/abs/1608.06993>`_.
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Args:
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weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.DenseNet121_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.DenseNet121_Weights
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:members:
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"""
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weights = DenseNet121_Weights.verify(weights)
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return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1))
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def densenet161(*, weights: Optional[DenseNet161_Weights] = None, 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/abs/1608.06993>`_.
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Args:
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weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.DenseNet161_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.DenseNet161_Weights
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:members:
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"""
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weights = DenseNet161_Weights.verify(weights)
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return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1))
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def densenet169(*, weights: Optional[DenseNet169_Weights] = None, 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/abs/1608.06993>`_.
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Args:
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weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.DenseNet169_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.DenseNet169_Weights
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:members:
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"""
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weights = DenseNet169_Weights.verify(weights)
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return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1))
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def densenet201(*, weights: Optional[DenseNet201_Weights] = None, 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/abs/1608.06993>`_.
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Args:
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weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.DenseNet201_Weights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.DenseNet201_Weights
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:members:
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"""
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weights = DenseNet201_Weights.verify(weights)
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return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)
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