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from __future__ import division, absolute_import | |
import torch.utils.model_zoo as model_zoo | |
from torch import nn | |
from torch.nn import functional as F | |
__all__ = ['mobilenetv2_x1_0', 'mobilenetv2_x1_4'] | |
model_urls = { | |
# 1.0: top-1 71.3 | |
'mobilenetv2_x1_0': | |
'https://mega.nz/#!NKp2wAIA!1NH1pbNzY_M2hVk_hdsxNM1NUOWvvGPHhaNr-fASF6c', | |
# 1.4: top-1 73.9 | |
'mobilenetv2_x1_4': | |
'https://mega.nz/#!RGhgEIwS!xN2s2ZdyqI6vQ3EwgmRXLEW3khr9tpXg96G9SUJugGk', | |
} | |
class ConvBlock(nn.Module): | |
"""Basic convolutional block. | |
convolution (bias discarded) + batch normalization + relu6. | |
Args: | |
in_c (int): number of input channels. | |
out_c (int): number of output channels. | |
k (int or tuple): kernel size. | |
s (int or tuple): stride. | |
p (int or tuple): padding. | |
g (int): number of blocked connections from input channels | |
to output channels (default: 1). | |
""" | |
def __init__(self, in_c, out_c, k, s=1, p=0, g=1): | |
super(ConvBlock, self).__init__() | |
self.conv = nn.Conv2d( | |
in_c, out_c, k, stride=s, padding=p, bias=False, groups=g | |
) | |
self.bn = nn.BatchNorm2d(out_c) | |
def forward(self, x): | |
return F.relu6(self.bn(self.conv(x))) | |
class Bottleneck(nn.Module): | |
def __init__(self, in_channels, out_channels, expansion_factor, stride=1): | |
super(Bottleneck, self).__init__() | |
mid_channels = in_channels * expansion_factor | |
self.use_residual = stride == 1 and in_channels == out_channels | |
self.conv1 = ConvBlock(in_channels, mid_channels, 1) | |
self.dwconv2 = ConvBlock( | |
mid_channels, mid_channels, 3, stride, 1, g=mid_channels | |
) | |
self.conv3 = nn.Sequential( | |
nn.Conv2d(mid_channels, out_channels, 1, bias=False), | |
nn.BatchNorm2d(out_channels), | |
) | |
def forward(self, x): | |
m = self.conv1(x) | |
m = self.dwconv2(m) | |
m = self.conv3(m) | |
if self.use_residual: | |
return x + m | |
else: | |
return m | |
class MobileNetV2(nn.Module): | |
"""MobileNetV2. | |
Reference: | |
Sandler et al. MobileNetV2: Inverted Residuals and | |
Linear Bottlenecks. CVPR 2018. | |
Public keys: | |
- ``mobilenetv2_x1_0``: MobileNetV2 x1.0. | |
- ``mobilenetv2_x1_4``: MobileNetV2 x1.4. | |
""" | |
def __init__( | |
self, | |
num_classes, | |
width_mult=1, | |
loss='softmax', | |
fc_dims=None, | |
dropout_p=None, | |
**kwargs | |
): | |
super(MobileNetV2, self).__init__() | |
self.loss = loss | |
self.in_channels = int(32 * width_mult) | |
self.feature_dim = int(1280 * width_mult) if width_mult > 1 else 1280 | |
# construct layers | |
self.conv1 = ConvBlock(3, self.in_channels, 3, s=2, p=1) | |
self.conv2 = self._make_layer( | |
Bottleneck, 1, int(16 * width_mult), 1, 1 | |
) | |
self.conv3 = self._make_layer( | |
Bottleneck, 6, int(24 * width_mult), 2, 2 | |
) | |
self.conv4 = self._make_layer( | |
Bottleneck, 6, int(32 * width_mult), 3, 2 | |
) | |
self.conv5 = self._make_layer( | |
Bottleneck, 6, int(64 * width_mult), 4, 2 | |
) | |
self.conv6 = self._make_layer( | |
Bottleneck, 6, int(96 * width_mult), 3, 1 | |
) | |
self.conv7 = self._make_layer( | |
Bottleneck, 6, int(160 * width_mult), 3, 2 | |
) | |
self.conv8 = self._make_layer( | |
Bottleneck, 6, int(320 * width_mult), 1, 1 | |
) | |
self.conv9 = ConvBlock(self.in_channels, self.feature_dim, 1) | |
self.global_avgpool = nn.AdaptiveAvgPool2d(1) | |
self.fc = self._construct_fc_layer( | |
fc_dims, self.feature_dim, dropout_p | |
) | |
self.classifier = nn.Linear(self.feature_dim, num_classes) | |
self._init_params() | |
def _make_layer(self, block, t, c, n, s): | |
# t: expansion factor | |
# c: output channels | |
# n: number of blocks | |
# s: stride for first layer | |
layers = [] | |
layers.append(block(self.in_channels, c, t, s)) | |
self.in_channels = c | |
for i in range(1, n): | |
layers.append(block(self.in_channels, c, t)) | |
return nn.Sequential(*layers) | |
def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None): | |
"""Constructs fully connected layer. | |
Args: | |
fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed | |
input_dim (int): input dimension | |
dropout_p (float): dropout probability, if None, dropout is unused | |
""" | |
if fc_dims is None: | |
self.feature_dim = input_dim | |
return None | |
assert isinstance( | |
fc_dims, (list, tuple) | |
), 'fc_dims must be either list or tuple, but got {}'.format( | |
type(fc_dims) | |
) | |
layers = [] | |
for dim in fc_dims: | |
layers.append(nn.Linear(input_dim, dim)) | |
layers.append(nn.BatchNorm1d(dim)) | |
layers.append(nn.ReLU(inplace=True)) | |
if dropout_p is not None: | |
layers.append(nn.Dropout(p=dropout_p)) | |
input_dim = dim | |
self.feature_dim = fc_dims[-1] | |
return nn.Sequential(*layers) | |
def _init_params(self): | |
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.constant_(m.bias, 0) | |
elif isinstance(m, nn.BatchNorm2d): | |
nn.init.constant_(m.weight, 1) | |
nn.init.constant_(m.bias, 0) | |
elif isinstance(m, nn.BatchNorm1d): | |
nn.init.constant_(m.weight, 1) | |
nn.init.constant_(m.bias, 0) | |
elif isinstance(m, nn.Linear): | |
nn.init.normal_(m.weight, 0, 0.01) | |
if m.bias is not None: | |
nn.init.constant_(m.bias, 0) | |
def featuremaps(self, x): | |
x = self.conv1(x) | |
x = self.conv2(x) | |
x = self.conv3(x) | |
x = self.conv4(x) | |
x = self.conv5(x) | |
x = self.conv6(x) | |
x = self.conv7(x) | |
x = self.conv8(x) | |
x = self.conv9(x) | |
return x | |
def forward(self, x): | |
f = self.featuremaps(x) | |
v = self.global_avgpool(f) | |
v = v.view(v.size(0), -1) | |
if self.fc is not None: | |
v = self.fc(v) | |
if not self.training: | |
return v | |
y = self.classifier(v) | |
if self.loss == 'softmax': | |
return y | |
elif self.loss == 'triplet': | |
return y, v | |
else: | |
raise KeyError("Unsupported loss: {}".format(self.loss)) | |
def init_pretrained_weights(model, model_url): | |
"""Initializes model with pretrained weights. | |
Layers that don't match with pretrained layers in name or size are kept unchanged. | |
""" | |
pretrain_dict = model_zoo.load_url(model_url) | |
model_dict = model.state_dict() | |
pretrain_dict = { | |
k: v | |
for k, v in pretrain_dict.items() | |
if k in model_dict and model_dict[k].size() == v.size() | |
} | |
model_dict.update(pretrain_dict) | |
model.load_state_dict(model_dict) | |
def mobilenetv2_x1_0(num_classes, loss, pretrained=True, **kwargs): | |
model = MobileNetV2( | |
num_classes, | |
loss=loss, | |
width_mult=1, | |
fc_dims=None, | |
dropout_p=None, | |
**kwargs | |
) | |
if pretrained: | |
# init_pretrained_weights(model, model_urls['mobilenetv2_x1_0']) | |
import warnings | |
warnings.warn( | |
'The imagenet pretrained weights need to be manually downloaded from {}' | |
.format(model_urls['mobilenetv2_x1_0']) | |
) | |
return model | |
def mobilenetv2_x1_4(num_classes, loss, pretrained=True, **kwargs): | |
model = MobileNetV2( | |
num_classes, | |
loss=loss, | |
width_mult=1.4, | |
fc_dims=None, | |
dropout_p=None, | |
**kwargs | |
) | |
if pretrained: | |
# init_pretrained_weights(model, model_urls['mobilenetv2_x1_4']) | |
import warnings | |
warnings.warn( | |
'The imagenet pretrained weights need to be manually downloaded from {}' | |
.format(model_urls['mobilenetv2_x1_4']) | |
) | |
return model | |