435 lines
17 KiB
Python
435 lines
17 KiB
Python
|
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)
|