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from torch import nn
from torch import Tensor
from typing import Callable, Optional, List
from utils.learning import freeze_params
__all__ = ['MobileNetV2']
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,
padding: int = -1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
activation_layer: Optional[Callable[..., nn.Module]] = None,
dilation: int = 1,
) -> None:
if padding == -1:
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().__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,
dilation: 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
self.kernel_size = 3
self.dilation = dilation
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,
dilation=dilation,
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,
output_stride=8,
norm_layer: Optional[Callable[..., nn.Module]] = None,
width_mult: float = 1.0,
inverted_residual_setting: Optional[List[List[int]]] = None,
round_nearest: int = 8,
block: Optional[Callable[..., nn.Module]] = None,
freeze_at=0) -> 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
last_channel = 1280
input_channel = 32
current_stride = 1
rate = 1
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)
]
current_stride *= 2
# building inverted residual blocks
for t, c, n, s in inverted_residual_setting:
if current_stride == output_stride:
stride = 1
dilation = rate
rate *= s
else:
stride = s
dilation = 1
current_stride *= s
output_channel = _make_divisible(c * width_mult, round_nearest)
for i in range(n):
if i == 0:
features.append(
block(input_channel, output_channel, stride, dilation,
t, norm_layer))
else:
features.append(
block(input_channel, output_channel, 1, rate, t,
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)
self._initialize_weights()
feature_4x = self.features[0:4]
feautre_8x = self.features[4:7]
feature_16x = self.features[7:14]
feature_32x = self.features[14:]
self.stages = [feature_4x, feautre_8x, feature_16x, feature_32x]
self.freeze(freeze_at)
def forward(self, x):
xs = []
for stage in self.stages:
x = stage(x)
xs.append(x)
return xs
def _initialize_weights(self):
# 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 freeze(self, freeze_at):
if freeze_at >= 1:
for m in self.stages[0][0]:
freeze_params(m)
for idx, stage in enumerate(self.stages, start=2):
if freeze_at >= idx:
freeze_params(stage)
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