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* refactor: Fixed typing exception throwing issues with JIT

* style: Added back mypy typing to forward

* chore: Added back densenet module as mypy exception
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@SsnL @fmassa @FuriouslyCurious @frgfm @eellison @Amir-Arsalan @alykhantejani @orashi @quinwu @skrish13 @Separius @chsasank
310 lines (261 sloc) 12.2 KB
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from .utils import load_state_dict_from_url
from torch import Tensor
from typing import Any, List, Tuple
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
model_urls = {
'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}
class _DenseLayer(nn.Module):
def __init__(
self,
num_input_features: int,
growth_rate: int,
bn_size: int,
drop_rate: float,
memory_efficient: bool = False
) -> None:
super(_DenseLayer, self).__init__()
self.norm1: nn.BatchNorm2d
self.add_module('norm1', nn.BatchNorm2d(num_input_features))
self.relu1: nn.ReLU
self.add_module('relu1', nn.ReLU(inplace=True))
self.conv1: nn.Conv2d
self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
growth_rate, kernel_size=1, stride=1,
bias=False))
self.norm2: nn.BatchNorm2d
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate))
self.relu2: nn.ReLU
self.add_module('relu2', nn.ReLU(inplace=True))
self.conv2: nn.Conv2d
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: List[Tensor]) -> Tensor:
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: List[Tensor]) -> bool:
for tensor in input:
if tensor.requires_grad:
return True
return False
@torch.jit.unused # noqa: T484
def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
def closure(*inputs):
return self.bn_function(inputs)
return cp.checkpoint(closure, *input)
@torch.jit._overload_method # noqa: F811
def forward(self, input: List[Tensor]) -> Tensor:
pass
@torch.jit._overload_method # noqa: F811
def forward(self, input: Tensor) -> Tensor:
pass
# torchscript does not yet support *args, so we overload method
# allowing it to take either a List[Tensor] or single Tensor
def forward(self, input: Tensor) -> Tensor: # 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
def __init__(
self,
num_layers: int,
num_input_features: int,
bn_size: int,
growth_rate: int,
drop_rate: float,
memory_efficient: bool = False
) -> None:
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: 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, num_input_features: int, num_output_features: int) -> None:
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" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
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
) -> None:
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: 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 _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> 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 = load_state_dict_from_url(model_url, progress=progress)
for key in list(state_dict.keys()):
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)
def _densenet(
arch: str,
growth_rate: int,
block_config: Tuple[int, int, int, int],
num_init_features: int,
pretrained: bool,
progress: bool,
**kwargs: Any
) -> DenseNet:
model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
if pretrained:
_load_state_dict(model, model_urls[arch], progress)
return model
def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
**kwargs)
def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs)
def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
**kwargs)
def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
**kwargs)