File size: 17,574 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 |
import warnings
from functools import partial
from typing import Any, Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_named_param, handle_legacy_interface
__all__ = [
"MNASNet",
"MNASNet0_5_Weights",
"MNASNet0_75_Weights",
"MNASNet1_0_Weights",
"MNASNet1_3_Weights",
"mnasnet0_5",
"mnasnet0_75",
"mnasnet1_0",
"mnasnet1_3",
]
# Paper suggests 0.9997 momentum, for TensorFlow. Equivalent PyTorch momentum is
# 1.0 - tensorflow.
_BN_MOMENTUM = 1 - 0.9997
class _InvertedResidual(nn.Module):
def __init__(
self, in_ch: int, out_ch: int, kernel_size: int, stride: int, expansion_factor: int, bn_momentum: float = 0.1
) -> None:
super().__init__()
if stride not in [1, 2]:
raise ValueError(f"stride should be 1 or 2 instead of {stride}")
if kernel_size not in [3, 5]:
raise ValueError(f"kernel_size should be 3 or 5 instead of {kernel_size}")
mid_ch = in_ch * expansion_factor
self.apply_residual = in_ch == out_ch and stride == 1
self.layers = nn.Sequential(
# Pointwise
nn.Conv2d(in_ch, mid_ch, 1, bias=False),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Depthwise
nn.Conv2d(mid_ch, mid_ch, kernel_size, padding=kernel_size // 2, stride=stride, groups=mid_ch, bias=False),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Linear pointwise. Note that there's no activation.
nn.Conv2d(mid_ch, out_ch, 1, bias=False),
nn.BatchNorm2d(out_ch, momentum=bn_momentum),
)
def forward(self, input: Tensor) -> Tensor:
if self.apply_residual:
return self.layers(input) + input
else:
return self.layers(input)
def _stack(
in_ch: int, out_ch: int, kernel_size: int, stride: int, exp_factor: int, repeats: int, bn_momentum: float
) -> nn.Sequential:
"""Creates a stack of inverted residuals."""
if repeats < 1:
raise ValueError(f"repeats should be >= 1, instead got {repeats}")
# First one has no skip, because feature map size changes.
first = _InvertedResidual(in_ch, out_ch, kernel_size, stride, exp_factor, bn_momentum=bn_momentum)
remaining = []
for _ in range(1, repeats):
remaining.append(_InvertedResidual(out_ch, out_ch, kernel_size, 1, exp_factor, bn_momentum=bn_momentum))
return nn.Sequential(first, *remaining)
def _round_to_multiple_of(val: float, divisor: int, round_up_bias: float = 0.9) -> int:
"""Asymmetric rounding to make `val` divisible by `divisor`. With default
bias, will round up, unless the number is no more than 10% greater than the
smaller divisible value, i.e. (83, 8) -> 80, but (84, 8) -> 88."""
if not 0.0 < round_up_bias < 1.0:
raise ValueError(f"round_up_bias should be greater than 0.0 and smaller than 1.0 instead of {round_up_bias}")
new_val = max(divisor, int(val + divisor / 2) // divisor * divisor)
return new_val if new_val >= round_up_bias * val else new_val + divisor
def _get_depths(alpha: float) -> List[int]:
"""Scales tensor depths as in reference MobileNet code, prefers rounding up
rather than down."""
depths = [32, 16, 24, 40, 80, 96, 192, 320]
return [_round_to_multiple_of(depth * alpha, 8) for depth in depths]
class MNASNet(torch.nn.Module):
"""MNASNet, as described in https://arxiv.org/abs/1807.11626. This
implements the B1 variant of the model.
>>> model = MNASNet(1.0, num_classes=1000)
>>> x = torch.rand(1, 3, 224, 224)
>>> y = model(x)
>>> y.dim()
2
>>> y.nelement()
1000
"""
# Version 2 adds depth scaling in the initial stages of the network.
_version = 2
def __init__(self, alpha: float, num_classes: int = 1000, dropout: float = 0.2) -> None:
super().__init__()
_log_api_usage_once(self)
if alpha <= 0.0:
raise ValueError(f"alpha should be greater than 0.0 instead of {alpha}")
self.alpha = alpha
self.num_classes = num_classes
depths = _get_depths(alpha)
layers = [
# First layer: regular conv.
nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
# Depthwise separable, no skip.
nn.Conv2d(depths[0], depths[0], 3, padding=1, stride=1, groups=depths[0], bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
nn.Conv2d(depths[0], depths[1], 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(depths[1], momentum=_BN_MOMENTUM),
# MNASNet blocks: stacks of inverted residuals.
_stack(depths[1], depths[2], 3, 2, 3, 3, _BN_MOMENTUM),
_stack(depths[2], depths[3], 5, 2, 3, 3, _BN_MOMENTUM),
_stack(depths[3], depths[4], 5, 2, 6, 3, _BN_MOMENTUM),
_stack(depths[4], depths[5], 3, 1, 6, 2, _BN_MOMENTUM),
_stack(depths[5], depths[6], 5, 2, 6, 4, _BN_MOMENTUM),
_stack(depths[6], depths[7], 3, 1, 6, 1, _BN_MOMENTUM),
# Final mapping to classifier input.
nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
self.layers = nn.Sequential(*layers)
self.classifier = nn.Sequential(nn.Dropout(p=dropout, inplace=True), nn.Linear(1280, num_classes))
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, mode="fan_out", nonlinearity="sigmoid")
nn.init.zeros_(m.bias)
def forward(self, x: Tensor) -> Tensor:
x = self.layers(x)
# Equivalent to global avgpool and removing H and W dimensions.
x = x.mean([2, 3])
return self.classifier(x)
def _load_from_state_dict(
self,
state_dict: Dict,
prefix: str,
local_metadata: Dict,
strict: bool,
missing_keys: List[str],
unexpected_keys: List[str],
error_msgs: List[str],
) -> None:
version = local_metadata.get("version", None)
if version not in [1, 2]:
raise ValueError(f"version shluld be set to 1 or 2 instead of {version}")
if version == 1 and not self.alpha == 1.0:
# In the initial version of the model (v1), stem was fixed-size.
# All other layer configurations were the same. This will patch
# the model so that it's identical to v1. Model with alpha 1.0 is
# unaffected.
depths = _get_depths(self.alpha)
v1_stem = [
nn.Conv2d(3, 32, 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(32, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
nn.Conv2d(32, 32, 3, padding=1, stride=1, groups=32, bias=False),
nn.BatchNorm2d(32, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
nn.Conv2d(32, 16, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(16, momentum=_BN_MOMENTUM),
_stack(16, depths[2], 3, 2, 3, 3, _BN_MOMENTUM),
]
for idx, layer in enumerate(v1_stem):
self.layers[idx] = layer
# The model is now identical to v1, and must be saved as such.
self._version = 1
warnings.warn(
"A new version of MNASNet model has been implemented. "
"Your checkpoint was saved using the previous version. "
"This checkpoint will load and work as before, but "
"you may want to upgrade by training a newer model or "
"transfer learning from an updated ImageNet checkpoint.",
UserWarning,
)
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
_COMMON_META = {
"min_size": (1, 1),
"categories": _IMAGENET_CATEGORIES,
"recipe": "https://github.com/1e100/mnasnet_trainer",
}
class MNASNet0_5_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 2218512,
"_metrics": {
"ImageNet-1K": {
"acc@1": 67.734,
"acc@5": 87.490,
}
},
"_ops": 0.104,
"_file_size": 8.591,
"_docs": """These weights reproduce closely the results of the paper.""",
},
)
DEFAULT = IMAGENET1K_V1
class MNASNet0_75_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mnasnet0_75-7090bc5f.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/6019",
"num_params": 3170208,
"_metrics": {
"ImageNet-1K": {
"acc@1": 71.180,
"acc@5": 90.496,
}
},
"_ops": 0.215,
"_file_size": 12.303,
"_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 MNASNet1_0_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 4383312,
"_metrics": {
"ImageNet-1K": {
"acc@1": 73.456,
"acc@5": 91.510,
}
},
"_ops": 0.314,
"_file_size": 16.915,
"_docs": """These weights reproduce closely the results of the paper.""",
},
)
DEFAULT = IMAGENET1K_V1
class MNASNet1_3_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mnasnet1_3-a4c69d6f.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/6019",
"num_params": 6282256,
"_metrics": {
"ImageNet-1K": {
"acc@1": 76.506,
"acc@5": 93.522,
}
},
"_ops": 0.526,
"_file_size": 24.246,
"_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
def _mnasnet(alpha: float, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> MNASNet:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = MNASNet(alpha, **kwargs)
if weights:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
@register_model()
@handle_legacy_interface(weights=("pretrained", MNASNet0_5_Weights.IMAGENET1K_V1))
def mnasnet0_5(*, weights: Optional[MNASNet0_5_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 0.5 from
`MnasNet: Platform-Aware Neural Architecture Search for Mobile
<https://arxiv.org/abs/1807.11626>`_ paper.
Args:
weights (:class:`~torchvision.models.MNASNet0_5_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MNASNet0_5_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.mnasnet.MNASNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MNASNet0_5_Weights
:members:
"""
weights = MNASNet0_5_Weights.verify(weights)
return _mnasnet(0.5, weights, progress, **kwargs)
@register_model()
@handle_legacy_interface(weights=("pretrained", MNASNet0_75_Weights.IMAGENET1K_V1))
def mnasnet0_75(*, weights: Optional[MNASNet0_75_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 0.75 from
`MnasNet: Platform-Aware Neural Architecture Search for Mobile
<https://arxiv.org/abs/1807.11626>`_ paper.
Args:
weights (:class:`~torchvision.models.MNASNet0_75_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MNASNet0_75_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.mnasnet.MNASNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MNASNet0_75_Weights
:members:
"""
weights = MNASNet0_75_Weights.verify(weights)
return _mnasnet(0.75, weights, progress, **kwargs)
@register_model()
@handle_legacy_interface(weights=("pretrained", MNASNet1_0_Weights.IMAGENET1K_V1))
def mnasnet1_0(*, weights: Optional[MNASNet1_0_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 1.0 from
`MnasNet: Platform-Aware Neural Architecture Search for Mobile
<https://arxiv.org/abs/1807.11626>`_ paper.
Args:
weights (:class:`~torchvision.models.MNASNet1_0_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MNASNet1_0_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.mnasnet.MNASNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MNASNet1_0_Weights
:members:
"""
weights = MNASNet1_0_Weights.verify(weights)
return _mnasnet(1.0, weights, progress, **kwargs)
@register_model()
@handle_legacy_interface(weights=("pretrained", MNASNet1_3_Weights.IMAGENET1K_V1))
def mnasnet1_3(*, weights: Optional[MNASNet1_3_Weights] = None, progress: bool = True, **kwargs: Any) -> MNASNet:
"""MNASNet with depth multiplier of 1.3 from
`MnasNet: Platform-Aware Neural Architecture Search for Mobile
<https://arxiv.org/abs/1807.11626>`_ paper.
Args:
weights (:class:`~torchvision.models.MNASNet1_3_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MNASNet1_3_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.mnasnet.MNASNet``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mnasnet.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MNASNet1_3_Weights
:members:
"""
weights = MNASNet1_3_Weights.verify(weights)
return _mnasnet(1.3, weights, progress, **kwargs)
|