216 lines
7.7 KiB
Python
216 lines
7.7 KiB
Python
|
from torch import nn
|
||
|
|
from torch import Tensor
|
||
|
|
from .utils import load_state_dict_from_url
|
||
|
|
from typing import Callable, Any, Optional, List
|
||
|
|
|
||
|
|
|
||
|
|
__all__ = ['MobileNetV2', 'mobilenet_v2']
|
||
|
|
|
||
|
|
|
||
|
|
model_urls = {
|
||
|
|
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) -> int:
|
||
|
|
"""
|
||
|
|
This function is taken from the original tf repo.
|
||
|
|
It ensures that all layers have a channel number that is divisible by 8
|
||
|
|
It can be seen here:
|
||
|
|
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
|
||
|
|
"""
|
||
|
|
if min_value is None:
|
||
|
|
min_value = divisor
|
||
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
||
|
|
# Make sure that round down does not go down by more than 10%.
|
||
|
|
if new_v < 0.9 * v:
|
||
|
|
new_v += divisor
|
||
|
|
return new_v
|
||
|
|
|
||
|
|
|
||
|
|
class ConvBNActivation(nn.Sequential):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
in_planes: int,
|
||
|
|
out_planes: int,
|
||
|
|
kernel_size: int = 3,
|
||
|
|
stride: int = 1,
|
||
|
|
groups: int = 1,
|
||
|
|
norm_layer: Optional[Callable[..., nn.Module]] = None,
|
||
|
|
activation_layer: Optional[Callable[..., nn.Module]] = None,
|
||
|
|
dilation: int = 1,
|
||
|
|
) -> None:
|
||
|
|
padding = (kernel_size - 1) // 2 * dilation
|
||
|
|
if norm_layer is None:
|
||
|
|
norm_layer = nn.BatchNorm2d
|
||
|
|
if activation_layer is None:
|
||
|
|
activation_layer = nn.ReLU6
|
||
|
|
super(ConvBNReLU, self).__init__(
|
||
|
|
nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation=dilation, groups=groups,
|
||
|
|
bias=False),
|
||
|
|
norm_layer(out_planes),
|
||
|
|
activation_layer(inplace=True)
|
||
|
|
)
|
||
|
|
self.out_channels = out_planes
|
||
|
|
|
||
|
|
|
||
|
|
# necessary for backwards compatibility
|
||
|
|
ConvBNReLU = ConvBNActivation
|
||
|
|
|
||
|
|
|
||
|
|
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(InvertedResidual, self).__init__()
|
||
|
|
self.stride = stride
|
||
|
|
assert stride in [1, 2]
|
||
|
|
|
||
|
|
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(ConvBNReLU(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer))
|
||
|
|
layers.extend([
|
||
|
|
# dw
|
||
|
|
ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim, norm_layer=norm_layer),
|
||
|
|
# 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
|
||
|
|
) -> 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
|
||
|
|
|
||
|
|
"""
|
||
|
|
super(MobileNetV2, self).__init__()
|
||
|
|
|
||
|
|
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("inverted_residual_setting should be non-empty "
|
||
|
|
"or a 4-element list, got {}".format(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] = [ConvBNReLU(3, input_channel, stride=2, norm_layer=norm_layer)]
|
||
|
|
# 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(ConvBNReLU(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer))
|
||
|
|
# make it nn.Sequential
|
||
|
|
self.features = nn.Sequential(*features)
|
||
|
|
|
||
|
|
# building classifier
|
||
|
|
self.classifier = nn.Sequential(
|
||
|
|
nn.Dropout(0.2),
|
||
|
|
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 => must use reshape with x.shape[0]
|
||
|
|
x = nn.functional.adaptive_avg_pool2d(x, (1, 1)).reshape(x.shape[0], -1)
|
||
|
|
x = self.classifier(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x: Tensor) -> Tensor:
|
||
|
|
return self._forward_impl(x)
|
||
|
|
|
||
|
|
|
||
|
|
def mobilenet_v2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV2:
|
||
|
|
"""
|
||
|
|
Constructs a MobileNetV2 architecture from
|
||
|
|
`"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
||
|
|
"""
|
||
|
|
model = MobileNetV2(**kwargs)
|
||
|
|
if pretrained:
|
||
|
|
state_dict = load_state_dict_from_url(model_urls['mobilenet_v2'],
|
||
|
|
progress=progress)
|
||
|
|
model.load_state_dict(state_dict)
|
||
|
|
return model
|