485 lines
18 KiB
Python
485 lines
18 KiB
Python
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from functools import partial
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from typing import Any, List, Optional, Type, Union
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import torch
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import torch.nn as nn
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from torch import Tensor
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from torchvision.models.resnet import (
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BasicBlock,
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Bottleneck,
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ResNet,
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ResNet18_Weights,
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ResNet50_Weights,
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ResNeXt101_32X8D_Weights,
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ResNeXt101_64X4D_Weights,
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)
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from ...transforms._presets import ImageClassification
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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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from .utils import _fuse_modules, _replace_relu, quantize_model
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__all__ = [
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"QuantizableResNet",
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"ResNet18_QuantizedWeights",
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"ResNet50_QuantizedWeights",
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"ResNeXt101_32X8D_QuantizedWeights",
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"ResNeXt101_64X4D_QuantizedWeights",
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"resnet18",
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"resnet50",
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"resnext101_32x8d",
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"resnext101_64x4d",
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]
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class QuantizableBasicBlock(BasicBlock):
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, **kwargs)
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self.add_relu = torch.nn.quantized.FloatFunctional()
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def forward(self, x: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out = self.add_relu.add_relu(out, identity)
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return out
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def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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_fuse_modules(self, [["conv1", "bn1", "relu"], ["conv2", "bn2"]], is_qat, inplace=True)
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if self.downsample:
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_fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)
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class QuantizableBottleneck(Bottleneck):
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, **kwargs)
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self.skip_add_relu = nn.quantized.FloatFunctional()
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self.relu1 = nn.ReLU(inplace=False)
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self.relu2 = nn.ReLU(inplace=False)
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def forward(self, x: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu2(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out = self.skip_add_relu.add_relu(out, identity)
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return out
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def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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_fuse_modules(
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self, [["conv1", "bn1", "relu1"], ["conv2", "bn2", "relu2"], ["conv3", "bn3"]], is_qat, inplace=True
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)
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if self.downsample:
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_fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)
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class QuantizableResNet(ResNet):
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, **kwargs)
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self.quant = torch.ao.quantization.QuantStub()
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self.dequant = torch.ao.quantization.DeQuantStub()
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def forward(self, x: Tensor) -> Tensor:
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x = self.quant(x)
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# Ensure scriptability
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# super(QuantizableResNet,self).forward(x)
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# is not scriptable
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x = self._forward_impl(x)
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x = self.dequant(x)
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return x
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def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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r"""Fuse conv/bn/relu modules in resnet models
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Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
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Model is modified in place. Note that this operation does not change numerics
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and the model after modification is in floating point
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"""
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_fuse_modules(self, ["conv1", "bn1", "relu"], is_qat, inplace=True)
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for m in self.modules():
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if type(m) is QuantizableBottleneck or type(m) is QuantizableBasicBlock:
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m.fuse_model(is_qat)
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def _resnet(
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block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]],
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layers: List[int],
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weights: Optional[WeightsEnum],
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progress: bool,
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quantize: bool,
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**kwargs: Any,
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) -> QuantizableResNet:
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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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if "backend" in weights.meta:
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_ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
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backend = kwargs.pop("backend", "fbgemm")
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model = QuantizableResNet(block, layers, **kwargs)
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_replace_relu(model)
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if quantize:
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quantize_model(model, backend)
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if weights is not None:
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
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return model
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_COMMON_META = {
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"min_size": (1, 1),
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"categories": _IMAGENET_CATEGORIES,
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"backend": "fbgemm",
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
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"_docs": """
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These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
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weights listed below.
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""",
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}
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class ResNet18_QuantizedWeights(WeightsEnum):
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IMAGENET1K_FBGEMM_V1 = Weights(
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url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.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": 11689512,
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"unquantized": ResNet18_Weights.IMAGENET1K_V1,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 69.494,
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"acc@5": 88.882,
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}
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},
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"_ops": 1.814,
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"_file_size": 11.238,
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},
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)
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DEFAULT = IMAGENET1K_FBGEMM_V1
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class ResNet50_QuantizedWeights(WeightsEnum):
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IMAGENET1K_FBGEMM_V1 = Weights(
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url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.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": 25557032,
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"unquantized": ResNet50_Weights.IMAGENET1K_V1,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 75.920,
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"acc@5": 92.814,
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}
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},
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"_ops": 4.089,
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"_file_size": 24.759,
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},
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)
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IMAGENET1K_FBGEMM_V2 = Weights(
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url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"num_params": 25557032,
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"unquantized": ResNet50_Weights.IMAGENET1K_V2,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 80.282,
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"acc@5": 94.976,
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}
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},
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"_ops": 4.089,
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"_file_size": 24.953,
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},
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)
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DEFAULT = IMAGENET1K_FBGEMM_V2
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class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
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IMAGENET1K_FBGEMM_V1 = Weights(
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url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.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": 88791336,
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"unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 78.986,
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"acc@5": 94.480,
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}
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},
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"_ops": 16.414,
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"_file_size": 86.034,
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},
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)
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IMAGENET1K_FBGEMM_V2 = Weights(
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url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"num_params": 88791336,
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"unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 82.574,
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"acc@5": 96.132,
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}
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},
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"_ops": 16.414,
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"_file_size": 86.645,
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},
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)
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DEFAULT = IMAGENET1K_FBGEMM_V2
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class ResNeXt101_64X4D_QuantizedWeights(WeightsEnum):
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IMAGENET1K_FBGEMM_V1 = Weights(
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url="https://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"num_params": 83455272,
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"recipe": "https://github.com/pytorch/vision/pull/5935",
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"unquantized": ResNeXt101_64X4D_Weights.IMAGENET1K_V1,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 82.898,
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"acc@5": 96.326,
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}
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},
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"_ops": 15.46,
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"_file_size": 81.556,
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},
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)
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DEFAULT = IMAGENET1K_FBGEMM_V1
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@register_model(name="quantized_resnet18")
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@handle_legacy_interface(
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weights=(
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"pretrained",
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lambda kwargs: ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1
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if kwargs.get("quantize", False)
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else ResNet18_Weights.IMAGENET1K_V1,
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)
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)
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def resnet18(
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*,
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weights: Optional[Union[ResNet18_QuantizedWeights, ResNet18_Weights]] = None,
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progress: bool = True,
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quantize: bool = False,
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**kwargs: Any,
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) -> QuantizableResNet:
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"""ResNet-18 model from
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`Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_
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.. note::
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Note that ``quantize = True`` returns a quantized model with 8 bit
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weights. Quantized models only support inference and run on CPUs.
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GPU inference is not yet supported.
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Args:
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weights (:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The
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pretrained weights for the model. See
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:class:`~torchvision.models.quantization.ResNet18_QuantizedWeights` 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
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download to stderr. Default is True.
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quantize (bool, optional): If True, return a quantized version of the model. Default is False.
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**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
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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/quantization/resnet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.quantization.ResNet18_QuantizedWeights
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:members:
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.. autoclass:: torchvision.models.ResNet18_Weights
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:members:
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:noindex:
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"""
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weights = (ResNet18_QuantizedWeights if quantize else ResNet18_Weights).verify(weights)
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return _resnet(QuantizableBasicBlock, [2, 2, 2, 2], weights, progress, quantize, **kwargs)
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@register_model(name="quantized_resnet50")
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@handle_legacy_interface(
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weights=(
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"pretrained",
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lambda kwargs: ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1
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if kwargs.get("quantize", False)
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else ResNet50_Weights.IMAGENET1K_V1,
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)
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)
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def resnet50(
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*,
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weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None,
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progress: bool = True,
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quantize: bool = False,
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**kwargs: Any,
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) -> QuantizableResNet:
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"""ResNet-50 model from
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`Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_
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.. note::
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Note that ``quantize = True`` returns a quantized model with 8 bit
|
||
|
|
weights. Quantized models only support inference and run on CPUs.
|
||
|
|
GPU inference is not yet supported.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
weights (:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` or :class:`~torchvision.models.ResNet50_Weights`, optional): The
|
||
|
|
pretrained weights for the model. See
|
||
|
|
:class:`~torchvision.models.quantization.ResNet50_QuantizedWeights` 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.
|
||
|
|
quantize (bool, optional): If True, return a quantized version of the model. Default is False.
|
||
|
|
**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
|
||
|
|
base class. Please refer to the `source code
|
||
|
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
|
||
|
|
for more details about this class.
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.quantization.ResNet50_QuantizedWeights
|
||
|
|
:members:
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.ResNet50_Weights
|
||
|
|
:members:
|
||
|
|
:noindex:
|
||
|
|
"""
|
||
|
|
weights = (ResNet50_QuantizedWeights if quantize else ResNet50_Weights).verify(weights)
|
||
|
|
|
||
|
|
return _resnet(QuantizableBottleneck, [3, 4, 6, 3], weights, progress, quantize, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model(name="quantized_resnext101_32x8d")
|
||
|
|
@handle_legacy_interface(
|
||
|
|
weights=(
|
||
|
|
"pretrained",
|
||
|
|
lambda kwargs: ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1
|
||
|
|
if kwargs.get("quantize", False)
|
||
|
|
else ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
def resnext101_32x8d(
|
||
|
|
*,
|
||
|
|
weights: Optional[Union[ResNeXt101_32X8D_QuantizedWeights, ResNeXt101_32X8D_Weights]] = None,
|
||
|
|
progress: bool = True,
|
||
|
|
quantize: bool = False,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> QuantizableResNet:
|
||
|
|
"""ResNeXt-101 32x8d model from
|
||
|
|
`Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
Note that ``quantize = True`` returns a quantized model with 8 bit
|
||
|
|
weights. Quantized models only support inference and run on CPUs.
|
||
|
|
GPU inference is not yet supported.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
weights (:class:`~torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The
|
||
|
|
pretrained weights for the model. See
|
||
|
|
:class:`~torchvision.models.quantization.ResNet101_32X8D_QuantizedWeights` 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.
|
||
|
|
quantize (bool, optional): If True, return a quantized version of the model. Default is False.
|
||
|
|
**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
|
||
|
|
base class. Please refer to the `source code
|
||
|
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
|
||
|
|
for more details about this class.
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.quantization.ResNeXt101_32X8D_QuantizedWeights
|
||
|
|
:members:
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights
|
||
|
|
:members:
|
||
|
|
:noindex:
|
||
|
|
"""
|
||
|
|
weights = (ResNeXt101_32X8D_QuantizedWeights if quantize else ResNeXt101_32X8D_Weights).verify(weights)
|
||
|
|
|
||
|
|
_ovewrite_named_param(kwargs, "groups", 32)
|
||
|
|
_ovewrite_named_param(kwargs, "width_per_group", 8)
|
||
|
|
return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model(name="quantized_resnext101_64x4d")
|
||
|
|
@handle_legacy_interface(
|
||
|
|
weights=(
|
||
|
|
"pretrained",
|
||
|
|
lambda kwargs: ResNeXt101_64X4D_QuantizedWeights.IMAGENET1K_FBGEMM_V1
|
||
|
|
if kwargs.get("quantize", False)
|
||
|
|
else ResNeXt101_64X4D_Weights.IMAGENET1K_V1,
|
||
|
|
)
|
||
|
|
)
|
||
|
|
def resnext101_64x4d(
|
||
|
|
*,
|
||
|
|
weights: Optional[Union[ResNeXt101_64X4D_QuantizedWeights, ResNeXt101_64X4D_Weights]] = None,
|
||
|
|
progress: bool = True,
|
||
|
|
quantize: bool = False,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> QuantizableResNet:
|
||
|
|
"""ResNeXt-101 64x4d model from
|
||
|
|
`Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
Note that ``quantize = True`` returns a quantized model with 8 bit
|
||
|
|
weights. Quantized models only support inference and run on CPUs.
|
||
|
|
GPU inference is not yet supported.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
weights (:class:`~torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights` or :class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The
|
||
|
|
pretrained weights for the model. See
|
||
|
|
:class:`~torchvision.models.quantization.ResNet101_64X4D_QuantizedWeights` 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.
|
||
|
|
quantize (bool, optional): If True, return a quantized version of the model. Default is False.
|
||
|
|
**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableResNet``
|
||
|
|
base class. Please refer to the `source code
|
||
|
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/resnet.py>`_
|
||
|
|
for more details about this class.
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.quantization.ResNeXt101_64X4D_QuantizedWeights
|
||
|
|
:members:
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights
|
||
|
|
:members:
|
||
|
|
:noindex:
|
||
|
|
"""
|
||
|
|
weights = (ResNeXt101_64X4D_QuantizedWeights if quantize else ResNeXt101_64X4D_Weights).verify(weights)
|
||
|
|
|
||
|
|
_ovewrite_named_param(kwargs, "groups", 64)
|
||
|
|
_ovewrite_named_param(kwargs, "width_per_group", 4)
|
||
|
|
return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)
|