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"""
This file is a modification of the original ResNet implementation from:
https://github.com/pytorch/vision/blob/bf01bab6125c5f1152e4f336b470399e52a8559d/torchvision/models/resnet.py
"""
from functools import partial
from typing import Any, Callable, List, Optional, Type, Union
import torch
import torch.nn as nn
from torch import Tensor
from torchvision.models._api import Weights, WeightsEnum, register_model
from torchvision.models._meta import _IMAGENET_CATEGORIES
from torchvision.models._utils import _ovewrite_named_param, handle_legacy_interface
from torchvision.transforms._presets import ImageClassification
from torchvision.utils import _log_api_usage_once
__all__ = [
"ResNet",
"ResNet18_Weights",
"ResNet34_Weights",
"ResNet50_Weights",
"ResNet101_Weights",
"ResNet152_Weights",
"ResNeXt50_32X4D_Weights",
"ResNeXt101_32X8D_Weights",
"ResNeXt101_64X4D_Weights",
"Wide_ResNet50_2_Weights",
"Wide_ResNet101_2_Weights",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"resnext50_32x4d",
"resnext101_32x8d",
"resnext101_64x4d",
"wide_resnet50_2",
"wide_resnet101_2",
]
def conv3x3(
in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1
) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition" https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.0)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
_log_api_usage_once(self)
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
"replace_stride_with_dilation should be None "
f"or a 3-element tuple, got {replace_stride_with_dilation}"
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
)
self.layer3 = self._make_layer(
block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
)
self.layer4 = self._make_layer(
block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
)
# self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # FIXME
self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck) and m.bn3.weight is not None:
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock) and m.bn2.weight is not None:
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
def _make_layer(
self,
block: Type[Union[BasicBlock, Bottleneck]],
planes: int,
blocks: int,
stride: int = 1,
dilate: bool = False,
) -> nn.Sequential:
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
downsample,
self.groups,
self.base_width,
previous_dilation,
norm_layer,
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer,
)
)
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _resnet(
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
weights: Optional[WeightsEnum],
progress: bool,
**kwargs: Any,
) -> ResNet:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = ResNet(block, layers, **kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress))
return model
_COMMON_META = {
"min_size": (1, 1),
"categories": _IMAGENET_CATEGORIES,
}
class ResNet18_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnet18-f37072fd.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 11689512,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
"_metrics": {
"ImageNet-1K": {
"acc@1": 69.758,
"acc@5": 89.078,
}
},
"_ops": 1.814,
"_file_size": 44.661,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
class ResNet34_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnet34-b627a593.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 21797672,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
"_metrics": {
"ImageNet-1K": {
"acc@1": 73.314,
"acc@5": 91.420,
}
},
"_ops": 3.664,
"_file_size": 83.275,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
DEFAULT = IMAGENET1K_V1
class ResNet50_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnet50-0676ba61.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 25557032,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
"_metrics": {
"ImageNet-1K": {
"acc@1": 76.130,
"acc@5": 92.862,
}
},
"_ops": 4.089,
"_file_size": 97.781,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 25557032,
"recipe": "https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621",
"_metrics": {
"ImageNet-1K": {
"acc@1": 80.858,
"acc@5": 95.434,
}
},
"_ops": 4.089,
"_file_size": 97.79,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class ResNet101_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnet101-63fe2227.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 44549160,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.374,
"acc@5": 93.546,
}
},
"_ops": 7.801,
"_file_size": 170.511,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/resnet101-cd907fc2.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 44549160,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
"_metrics": {
"ImageNet-1K": {
"acc@1": 81.886,
"acc@5": 95.780,
}
},
"_ops": 7.801,
"_file_size": 170.53,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class ResNet152_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnet152-394f9c45.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 60192808,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
"_metrics": {
"ImageNet-1K": {
"acc@1": 78.312,
"acc@5": 94.046,
}
},
"_ops": 11.514,
"_file_size": 230.434,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/resnet152-f82ba261.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 60192808,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.284,
"acc@5": 96.002,
}
},
"_ops": 11.514,
"_file_size": 230.474,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class ResNeXt50_32X4D_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 25028904,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.618,
"acc@5": 93.698,
}
},
"_ops": 4.23,
"_file_size": 95.789,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 25028904,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
"_metrics": {
"ImageNet-1K": {
"acc@1": 81.198,
"acc@5": 95.340,
}
},
"_ops": 4.23,
"_file_size": 95.833,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class ResNeXt101_32X8D_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 88791336,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
"_metrics": {
"ImageNet-1K": {
"acc@1": 79.312,
"acc@5": 94.526,
}
},
"_ops": 16.414,
"_file_size": 339.586,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 88791336,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres",
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.834,
"acc@5": 96.228,
}
},
"_ops": 16.414,
"_file_size": 339.673,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class ResNeXt101_64X4D_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 83455272,
"recipe": "https://github.com/pytorch/vision/pull/5935",
"_metrics": {
"ImageNet-1K": {
"acc@1": 83.246,
"acc@5": 96.454,
}
},
"_ops": 15.46,
"_file_size": 319.318,
"_docs": """
These weights were trained from scratch by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V1
class Wide_ResNet50_2_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 68883240,
"recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
"_metrics": {
"ImageNet-1K": {
"acc@1": 78.468,
"acc@5": 94.086,
}
},
"_ops": 11.398,
"_file_size": 131.82,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 68883240,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres",
"_metrics": {
"ImageNet-1K": {
"acc@1": 81.602,
"acc@5": 95.758,
}
},
"_ops": 11.398,
"_file_size": 263.124,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class Wide_ResNet101_2_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 126886696,
"recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
"_metrics": {
"ImageNet-1K": {
"acc@1": 78.848,
"acc@5": 94.284,
}
},
"_ops": 22.753,
"_file_size": 242.896,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 126886696,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.510,
"acc@5": 96.020,
}
},
"_ops": 22.753,
"_file_size": 484.747,
"_docs": """
These weights improve upon the results of the original paper by using TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
@register_model()
@handle_legacy_interface(weights=("pretrained", ResNet18_Weights.IMAGENET1K_V1))
def resnet18_custom(
*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any
) -> ResNet:
"""ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__.
Args:
weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The
pre-trained weights to use. See
:class:`~torchvision.models.ResNet18_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.resnet.ResNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.ResNet18_Weights
:members:
"""
weights = ResNet18_Weights.verify(weights)
return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs) |