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