from functools import partial
from typing import Any, Callable, List, Optional, Sequence
import torch
from torch import nn, Tensor
from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer
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 _make_divisible, _ovewrite_named_param, handle_legacy_interface
__all__ = [
"MobileNetV3",
"MobileNet_V3_Large_Weights",
"MobileNet_V3_Small_Weights",
"mobilenet_v3_large",
"mobilenet_v3_small",
]
class InvertedResidualConfig:
# Stores information listed at Tables 1 and 2 of the MobileNetV3 paper
def __init__(
self,
input_channels: int,
kernel: int,
expanded_channels: int,
out_channels: int,
use_se: bool,
activation: str,
stride: int,
dilation: int,
width_mult: float,
):
self.input_channels = self.adjust_channels(input_channels, width_mult)
self.kernel = kernel
self.expanded_channels = self.adjust_channels(expanded_channels, width_mult)
self.out_channels = self.adjust_channels(out_channels, width_mult)
self.use_se = use_se
self.use_hs = activation == "HS"
self.stride = stride
self.dilation = dilation
@staticmethod
def adjust_channels(channels: int, width_mult: float):
return _make_divisible(channels * width_mult, 8)
class InvertedResidual(nn.Module):
# Implemented as described at section 5 of MobileNetV3 paper
def __init__(
self,
cnf: InvertedResidualConfig,
norm_layer: Callable[..., nn.Module],
se_layer: Callable[..., nn.Module] = partial(SElayer, scale_activation=nn.Hardsigmoid),
):
super().__init__()
if not (1 <= cnf.stride <= 2):
raise ValueError("illegal stride value")
self.use_res_connect = cnf.stride == 1 and cnf.input_channels == cnf.out_channels
layers: List[nn.Module] = []
activation_layer = nn.Hardswish if cnf.use_hs else nn.ReLU
# expand
if cnf.expanded_channels != cnf.input_channels:
layers.append(
Conv2dNormActivation(
cnf.input_channels,
cnf.expanded_channels,
kernel_size=1,
norm_layer=norm_layer,
activation_layer=activation_layer,
)
)
# depthwise
stride = 1 if cnf.dilation > 1 else cnf.stride
layers.append(
Conv2dNormActivation(
cnf.expanded_channels,
cnf.expanded_channels,
kernel_size=cnf.kernel,
stride=stride,
dilation=cnf.dilation,
groups=cnf.expanded_channels,
norm_layer=norm_layer,
activation_layer=activation_layer,
)
)
if cnf.use_se:
squeeze_channels = _make_divisible(cnf.expanded_channels // 4, 8)
layers.append(se_layer(cnf.expanded_channels, squeeze_channels))
# project
layers.append(
Conv2dNormActivation(
cnf.expanded_channels, cnf.out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=None
)
)
self.block = nn.Sequential(*layers)
self.out_channels = cnf.out_channels
self._is_cn = cnf.stride > 1
def forward(self, input: Tensor) -> Tensor:
result = self.block(input)
if self.use_res_connect:
result += input
return result
class MobileNetV3(nn.Module):
def __init__(
self,
inverted_residual_setting: List[InvertedResidualConfig],
last_channel: int,
num_classes: int = 1000,
block: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None,
dropout: float = 0.2,
**kwargs: Any,
) -> None:
"""
MobileNet V3 main class
Args:
inverted_residual_setting (List[InvertedResidualConfig]): Network structure
last_channel (int): The number of channels on the penultimate layer
num_classes (int): Number of classes
block (Optional[Callable[..., nn.Module]]): Module specifying inverted residual building block for mobilenet
norm_layer (Optional[Callable[..., nn.Module]]): Module specifying the normalization layer to use
dropout (float): The droupout probability
"""
super().__init__()
_log_api_usage_once(self)
if not inverted_residual_setting:
raise ValueError("The inverted_residual_setting should not be empty")
elif not (
isinstance(inverted_residual_setting, Sequence)
and all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])
):
raise TypeError("The inverted_residual_setting should be List[InvertedResidualConfig]")
if block is None:
block = InvertedResidual
if norm_layer is None:
norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01)
layers: List[nn.Module] = []
# building first layer
firstconv_output_channels = inverted_residual_setting[0].input_channels
layers.append(
Conv2dNormActivation(
3,
firstconv_output_channels,
kernel_size=3,
stride=2,
norm_layer=norm_layer,
activation_layer=nn.Hardswish,
)
)
# building inverted residual blocks
for cnf in inverted_residual_setting:
layers.append(block(cnf, norm_layer))
# building last several layers
lastconv_input_channels = inverted_residual_setting[-1].out_channels
lastconv_output_channels = 6 * lastconv_input_channels
layers.append(
Conv2dNormActivation(
lastconv_input_channels,
lastconv_output_channels,
kernel_size=1,
norm_layer=norm_layer,
activation_layer=nn.Hardswish,
)
)
self.features = nn.Sequential(*layers)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Sequential(
nn.Linear(lastconv_output_channels, last_channel),
nn.Hardswish(inplace=True),
nn.Dropout(p=dropout, inplace=True),
nn.Linear(last_channel, num_classes),
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out")
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.zeros_(m.bias)
def _forward_impl(self, x: Tensor) -> Tensor:
x = self.features(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _mobilenet_v3_conf(
arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False, **kwargs: Any
):
reduce_divider = 2 if reduced_tail else 1
dilation = 2 if dilated else 1
bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult)
adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult)
if arch == "mobilenet_v3_large":
inverted_residual_setting = [
bneck_conf(16, 3, 16, 16, False, "RE", 1, 1),
bneck_conf(16, 3, 64, 24, False, "RE", 2, 1), # C1
bneck_conf(24, 3, 72, 24, False, "RE", 1, 1),
bneck_conf(24, 5, 72, 40, True, "RE", 2, 1), # C2
bneck_conf(40, 5, 120, 40, True, "RE", 1, 1),
bneck_conf(40, 5, 120, 40, True, "RE", 1, 1),
bneck_conf(40, 3, 240, 80, False, "HS", 2, 1), # C3
bneck_conf(80, 3, 200, 80, False, "HS", 1, 1),
bneck_conf(80, 3, 184, 80, False, "HS", 1, 1),
bneck_conf(80, 3, 184, 80, False, "HS", 1, 1),
bneck_conf(80, 3, 480, 112, True, "HS", 1, 1),
bneck_conf(112, 3, 672, 112, True, "HS", 1, 1),
bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation), # C4
bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation),
bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation),
]
last_channel = adjust_channels(1280 // reduce_divider) # C5
elif arch == "mobilenet_v3_small":
inverted_residual_setting = [
bneck_conf(16, 3, 16, 16, True, "RE", 2, 1), # C1
bneck_conf(16, 3, 72, 24, False, "RE", 2, 1), # C2
bneck_conf(24, 3, 88, 24, False, "RE", 1, 1),
bneck_conf(24, 5, 96, 40, True, "HS", 2, 1), # C3
bneck_conf(40, 5, 240, 40, True, "HS", 1, 1),
bneck_conf(40, 5, 240, 40, True, "HS", 1, 1),
bneck_conf(40, 5, 120, 48, True, "HS", 1, 1),
bneck_conf(48, 5, 144, 48, True, "HS", 1, 1),
bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation), # C4
bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation),
bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation),
]
last_channel = adjust_channels(1024 // reduce_divider) # C5
else:
raise ValueError(f"Unsupported model type {arch}")
return inverted_residual_setting, last_channel
def _mobilenet_v3(
inverted_residual_setting: List[InvertedResidualConfig],
last_channel: int,
weights: Optional[WeightsEnum],
progress: bool,
**kwargs: Any,
) -> MobileNetV3:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = MobileNetV3(inverted_residual_setting, last_channel, **kwargs)
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,
}
class MobileNet_V3_Large_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 5483032,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
"_metrics": {
"ImageNet-1K": {
"acc@1": 74.042,
"acc@5": 91.340,
}
},
"_ops": 0.217,
"_file_size": 21.114,
"_docs": """These weights were trained from scratch by using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"num_params": 5483032,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
"_metrics": {
"ImageNet-1K": {
"acc@1": 75.274,
"acc@5": 92.566,
}
},
"_ops": 0.217,
"_file_size": 21.107,
"_docs": """
These weights improve marginally upon the results of the original paper by using a modified version of
TorchVision's `new training recipe
<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
""",
},
)
DEFAULT = IMAGENET1K_V2
class MobileNet_V3_Small_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 2542856,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv3-large--small",
"_metrics": {
"ImageNet-1K": {
"acc@1": 67.668,
"acc@5": 87.402,
}
},
"_ops": 0.057,
"_file_size": 9.829,
"_docs": """
These weights improve upon the results of the original paper by using a simple training recipe.
""",
},
)
DEFAULT = IMAGENET1K_V1
@register_model()
@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Large_Weights.IMAGENET1K_V1))
def mobilenet_v3_large(
*, weights: Optional[MobileNet_V3_Large_Weights] = None, progress: bool = True, **kwargs: Any
) -> MobileNetV3:
"""
Constructs a large MobileNetV3 architecture from
`Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`__.
Args:
weights (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MobileNet_V3_Large_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.mobilenet.MobileNetV3``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MobileNet_V3_Large_Weights
:members:
"""
weights = MobileNet_V3_Large_Weights.verify(weights)
inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)
@register_model()
@handle_legacy_interface(weights=("pretrained", MobileNet_V3_Small_Weights.IMAGENET1K_V1))
def mobilenet_v3_small(
*, weights: Optional[MobileNet_V3_Small_Weights] = None, progress: bool = True, **kwargs: Any
) -> MobileNetV3:
"""
Constructs a small MobileNetV3 architecture from
`Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`__.
Args:
weights (:class:`~torchvision.models.MobileNet_V3_Small_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MobileNet_V3_Small_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.mobilenet.MobileNetV3``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv3.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MobileNet_V3_Small_Weights
:members:
"""
weights = MobileNet_V3_Small_Weights.verify(weights)
inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs)
return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)