Source code for torchvision.models.mnasnet
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 WeightsEnum, Weights
from ._meta import _IMAGENET_CATEGORIES
from ._utils import handle_legacy_interface, _ovewrite_named_param
__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 rouding 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/pdf/1807.11626.pdf. 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,
}
},
"_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,
}
},
"_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
[docs]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,
}
},
"_docs": """These weights reproduce closely the results of the paper.""",
},
)
DEFAULT = IMAGENET1K_V1
[docs]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,
}
},
"_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))
return 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/pdf/1807.11626.pdf>`_ 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)
@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/pdf/1807.11626.pdf>`_ 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)
[docs]@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/pdf/1807.11626.pdf>`_ 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)
[docs]@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/pdf/1807.11626.pdf>`_ 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)
