Source code for torchvision.models.mobilenetv2
from functools import partial
from typing import Any, Callable, List, Optional
import torch
from torch import nn, Tensor
from ..ops.misc import Conv2dNormActivation
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__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]
# necessary for backwards compatibility
class InvertedResidual(nn.Module):
def __init__(
self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super().__init__()
self.stride = stride
if stride not in [1, 2]:
raise ValueError(f"stride should be 1 or 2 instead of {stride}")
if norm_layer is None:
norm_layer = nn.BatchNorm2d
hidden_dim = int(round(inp * expand_ratio))
self.use_res_connect = self.stride == 1 and inp == oup
layers: List[nn.Module] = []
if expand_ratio != 1:
# pw
layers.append(
Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
)
layers.extend(
[
# dw
Conv2dNormActivation(
hidden_dim,
hidden_dim,
stride=stride,
groups=hidden_dim,
norm_layer=norm_layer,
activation_layer=nn.ReLU6,
),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
norm_layer(oup),
]
)
self.conv = nn.Sequential(*layers)
self.out_channels = oup
self._is_cn = stride > 1
def forward(self, x: Tensor) -> Tensor:
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(
self,
num_classes: int = 1000,
width_mult: float = 1.0,
inverted_residual_setting: Optional[List[List[int]]] = None,
round_nearest: int = 8,
block: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None,
dropout: float = 0.2,
) -> None:
"""
MobileNet V2 main class
Args:
num_classes (int): Number of classes
width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
inverted_residual_setting: Network structure
round_nearest (int): Round the number of channels in each layer to be a multiple of this number
Set to 1 to turn off rounding
block: Module specifying inverted residual building block for mobilenet
norm_layer: Module specifying the normalization layer to use
dropout (float): The droupout probability
"""
super().__init__()
_log_api_usage_once(self)
if block is None:
block = InvertedResidual
if norm_layer is None:
norm_layer = nn.BatchNorm2d
input_channel = 32
last_channel = 1280
if inverted_residual_setting is None:
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# only check the first element, assuming user knows t,c,n,s are required
if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
raise ValueError(
f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}"
)
# building first layer
input_channel = _make_divisible(input_channel * width_mult, round_nearest)
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
features: List[nn.Module] = [
Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
]
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
stride = s if i == 0 else 1
features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
input_channel = output_channel
# building last several layers
features.append(
Conv2dNormActivation(
input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
)
)
# make it nn.Sequential
self.features = nn.Sequential(*features)
# building classifier
self.classifier = nn.Sequential(
nn.Dropout(p=dropout),
nn.Linear(self.last_channel, num_classes),
)
# weight initialization
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:
# This exists since TorchScript doesn't support inheritance, so the superclass method
# (this one) needs to have a name other than `forward` that can be accessed in a subclass
x = self.features(x)
# Cannot use "squeeze" as batch-size can be 1
x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
_COMMON_META = {
"num_params": 3504872,
"min_size": (1, 1),
"categories": _IMAGENET_CATEGORIES,
}
[docs]class MobileNet_V2_Weights(WeightsEnum):
IMAGENET1K_V1 = Weights(
url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
"_metrics": {
"ImageNet-1K": {
"acc@1": 71.878,
"acc@5": 90.286,
}
},
"_ops": 0.301,
"_file_size": 13.555,
"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
},
)
IMAGENET1K_V2 = Weights(
url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
"_metrics": {
"ImageNet-1K": {
"acc@1": 72.154,
"acc@5": 90.822,
}
},
"_ops": 0.301,
"_file_size": 13.598,
"_docs": """
These weights improve 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
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1))
def mobilenet_v2(
*, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any
) -> MobileNetV2:
"""MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear
Bottlenecks <https://arxiv.org/abs/1801.04381>`_ paper.
Args:
weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.MobileNet_V2_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.mobilenetv2.MobileNetV2``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv2.py>`_
for more details about this class.
.. autoclass:: torchvision.models.MobileNet_V2_Weights
:members:
"""
weights = MobileNet_V2_Weights.verify(weights)
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = MobileNetV2(**kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
