"""
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch
import torch.utils.model_zoo as model_zoo
from torch import nn
__all__ = [
'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
'shufflenet_v2_x2_0'
]
model_urls = {
'shufflenetv2_x0.5':
'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
'shufflenetv2_x1.0':
'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
'shufflenetv2_x1.5': None,
'shufflenetv2_x2.0': None,
}
def channel_shuffle(x, groups):
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups, channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride):
super(InvertedResidual, self).__init__()
if not (1 <= stride <= 3):
raise ValueError('illegal stride value')
self.stride = stride
branch_features = oup // 2
assert (self.stride != 1) or (inp == branch_features << 1)
if self.stride > 1:
self.branch1 = nn.Sequential(
self.depthwise_conv(
inp, inp, kernel_size=3, stride=self.stride, padding=1
),
nn.BatchNorm2d(inp),
nn.Conv2d(
inp,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False
),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
self.branch2 = nn.Sequential(
nn.Conv2d(
inp if (self.stride > 1) else branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False
),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(
branch_features,
branch_features,
kernel_size=3,
stride=self.stride,
padding=1
),
nn.BatchNorm2d(branch_features),
nn.Conv2d(
branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False
),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(
i, o, kernel_size, stride, padding, bias=bias, groups=i
)
def forward(self, x):
if self.stride == 1:
x1, x2 = x.chunk(2, dim=1)
out = torch.cat((x1, self.branch2(x2)), dim=1)
else:
out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
out = channel_shuffle(out, 2)
return out
[docs]class ShuffleNetV2(nn.Module):
"""ShuffleNetV2.
Reference:
Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
Public keys:
- ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
- ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
- ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
- ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
"""
def __init__(
self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs
):
super(ShuffleNetV2, self).__init__()
self.loss = loss
if len(stages_repeats) != 3:
raise ValueError(
'expected stages_repeats as list of 3 positive ints'
)
if len(stages_out_channels) != 5:
raise ValueError(
'expected stages_out_channels as list of 5 positive ints'
)
self._stage_out_channels = stages_out_channels
input_channels = 3
output_channels = self._stage_out_channels[0]
self.conv1 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
input_channels = output_channels
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
for name, repeats, output_channels in zip(
stage_names, stages_repeats, self._stage_out_channels[1:]
):
seq = [InvertedResidual(input_channels, output_channels, 2)]
for i in range(repeats - 1):
seq.append(
InvertedResidual(output_channels, output_channels, 1)
)
setattr(self, name, nn.Sequential(*seq))
input_channels = output_channels
output_channels = self._stage_out_channels[-1]
self.conv5 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Linear(output_channels, num_classes)
def featuremaps(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.conv5(x)
return x
def forward(self, x):
f = self.featuremaps(x)
v = self.global_avgpool(f)
v = v.view(v.size(0), -1)
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.
"""
if model_url is None:
import warnings
warnings.warn(
'ImageNet pretrained weights are unavailable for this model'
)
return
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 shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ShuffleNetV2(
num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
return model
def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ShuffleNetV2(
num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
return model
def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ShuffleNetV2(
num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
return model
def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ShuffleNetV2(
num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs
)
if pretrained:
init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
return model