File size: 17,939 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 |
from functools import partial
from typing import Any, List, Optional, Type, Union
import torch
import torch.nn as nn
from torch import Tensor
from torchvision.models.resnet import (
BasicBlock,
Bottleneck,
ResNet,
ResNet18_Weights,
ResNet50_Weights,
ResNeXt101_32X8D_Weights,
ResNeXt101_64X4D_Weights,
)
from ...transforms._presets import ImageClassification
from .._api import register_model, Weights, WeightsEnum
from .._meta import _IMAGENET_CATEGORIES
from .._utils import _ovewrite_named_param, handle_legacy_interface
from .utils import _fuse_modules, _replace_relu, quantize_model
__all__ = [
"QuantizableResNet",
"ResNet18_QuantizedWeights",
"ResNet50_QuantizedWeights",
"ResNeXt101_32X8D_QuantizedWeights",
"ResNeXt101_64X4D_QuantizedWeights",
"resnet18",
"resnet50",
"resnext101_32x8d",
"resnext101_64x4d",
]
class QuantizableBasicBlock(BasicBlock):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.add_relu = torch.nn.quantized.FloatFunctional()
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 = self.add_relu.add_relu(out, identity)
return out
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
_fuse_modules(self, [["conv1", "bn1", "relu"], ["conv2", "bn2"]], is_qat, inplace=True)
if self.downsample:
_fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)
class QuantizableBottleneck(Bottleneck):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.skip_add_relu = nn.quantized.FloatFunctional()
self.relu1 = nn.ReLU(inplace=False)
self.relu2 = nn.ReLU(inplace=False)
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu2(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out = self.skip_add_relu.add_relu(out, identity)
return out
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
_fuse_modules(
self, [["conv1", "bn1", "relu1"], ["conv2", "bn2", "relu2"], ["conv3", "bn3"]], is_qat, inplace=True
)
if self.downsample:
_fuse_modules(self.downsample, ["0", "1"], is_qat, inplace=True)
class QuantizableResNet(ResNet):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x: Tensor) -> Tensor:
x = self.quant(x)
# Ensure scriptability
# super(QuantizableResNet,self).forward(x)
# is not scriptable
x = self._forward_impl(x)
x = self.dequant(x)
return x
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
r"""Fuse conv/bn/relu modules in resnet models
Fuse conv+bn+relu/ Conv+relu/conv+Bn modules to prepare for quantization.
Model is modified in place. Note that this operation does not change numerics
and the model after modification is in floating point
"""
_fuse_modules(self, ["conv1", "bn1", "relu"], is_qat, inplace=True)
for m in self.modules():
if type(m) is QuantizableBottleneck or type(m) is QuantizableBasicBlock:
m.fuse_model(is_qat)
def _resnet(
block: Type[Union[QuantizableBasicBlock, QuantizableBottleneck]],
layers: List[int],
weights: Optional[WeightsEnum],
progress: bool,
quantize: bool,
**kwargs: Any,
) -> QuantizableResNet:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
if "backend" in weights.meta:
_ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
backend = kwargs.pop("backend", "fbgemm")
model = QuantizableResNet(block, layers, **kwargs)
_replace_relu(model)
if quantize:
quantize_model(model, backend)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
_COMMON_META = {
"min_size": (1, 1),
"categories": _IMAGENET_CATEGORIES,
"backend": "fbgemm",
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
"_docs": """
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
weights listed below.
""",
}
class ResNet18_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 11689512,
"unquantized": ResNet18_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 69.494,
"acc@5": 88.882,
}
},
"_ops": 1.814,
"_file_size": 11.238,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
class ResNet50_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 25557032,
"unquantized": ResNet50_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 75.920,
"acc@5": 92.814,
}
},
"_ops": 4.089,
"_file_size": 24.759,
},
)
IMAGENET1K_FBGEMM_V2 = Weights(
url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 25557032,
"unquantized": ResNet50_Weights.IMAGENET1K_V2,
"_metrics": {
"ImageNet-1K": {
"acc@1": 80.282,
"acc@5": 94.976,
}
},
"_ops": 4.089,
"_file_size": 24.953,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V2
class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 88791336,
"unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 78.986,
"acc@5": 94.480,
}
},
"_ops": 16.414,
"_file_size": 86.034,
},
)
IMAGENET1K_FBGEMM_V2 = Weights(
url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 88791336,
"unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2,
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.574,
"acc@5": 96.132,
}
},
"_ops": 16.414,
"_file_size": 86.645,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V2
class ResNeXt101_64X4D_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/resnext101_64x4d_fbgemm-605a1cb3.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 83455272,
"recipe": "https://github.com/pytorch/vision/pull/5935",
"unquantized": ResNeXt101_64X4D_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 82.898,
"acc@5": 96.326,
}
},
"_ops": 15.46,
"_file_size": 81.556,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
@register_model(name="quantized_resnet18")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ResNet18_Weights.IMAGENET1K_V1,
)
)
def resnet18(
*,
weights: Optional[Union[ResNet18_QuantizedWeights, ResNet18_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> QuantizableResNet:
"""ResNet-18 model from
`Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_
.. 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.ResNet18_QuantizedWeights` or :class:`~torchvision.models.ResNet18_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.ResNet18_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.ResNet18_QuantizedWeights
:members:
.. autoclass:: torchvision.models.ResNet18_Weights
:members:
:noindex:
"""
weights = (ResNet18_QuantizedWeights if quantize else ResNet18_Weights).verify(weights)
return _resnet(QuantizableBasicBlock, [2, 2, 2, 2], weights, progress, quantize, **kwargs)
@register_model(name="quantized_resnet50")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ResNet50_Weights.IMAGENET1K_V1,
)
)
def resnet50(
*,
weights: Optional[Union[ResNet50_QuantizedWeights, ResNet50_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> QuantizableResNet:
"""ResNet-50 model from
`Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`_
.. 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.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)
|