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miunangel
229
Dassl.ProGrad.pytorch/dassl/modeling/backbone/shufflenetv2.py
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229
Dassl.ProGrad.pytorch/dassl/modeling/backbone/shufflenetv2.py
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"""
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Code source: https://github.com/pytorch/vision
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"""
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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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from .build import BACKBONE_REGISTRY
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from .backbone import Backbone
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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().__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(Backbone):
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def __init__(self, stages_repeats, stages_out_channels, **kwargs):
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super().__init__()
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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._out_features = output_channels
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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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return v.view(v.size(0), -1)
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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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@BACKBONE_REGISTRY.register()
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def shufflenet_v2_x0_5(pretrained=True, **kwargs):
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model = ShuffleNetV2([4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
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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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@BACKBONE_REGISTRY.register()
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def shufflenet_v2_x1_0(pretrained=True, **kwargs):
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model = ShuffleNetV2([4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
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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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@BACKBONE_REGISTRY.register()
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def shufflenet_v2_x1_5(pretrained=True, **kwargs):
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model = ShuffleNetV2([4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
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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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@BACKBONE_REGISTRY.register()
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def shufflenet_v2_x2_0(pretrained=True, **kwargs):
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model = ShuffleNetV2([4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
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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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