257 lines
8.4 KiB
Python
257 lines
8.4 KiB
Python
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import logging
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from ..runner import load_checkpoint
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from .base_backbone import BaseBackbone
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from .weight_init import constant_init, kaiming_init
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def conv_bn(inp, oup, stride):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
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nn.BatchNorm2d(oup),
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nn.ReLU(inplace=True)
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)
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def conv_1x1_bn(inp, oup):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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nn.ReLU(inplace=True)
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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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assert (num_channels % groups == 0)
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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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# transpose
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# - contiguous() required if transpose() is used before view().
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# See https://github.com/pytorch/pytorch/issues/764
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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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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class InvertedResidual(nn.Module):
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def __init__(self, inp, oup, stride, with_cp=False):
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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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self.with_cp = with_cp
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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(inp, inp, kernel_size=3,
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stride=self.stride, padding=1),
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nn.BatchNorm2d(inp),
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nn.Conv2d(inp, branch_features, kernel_size=1,
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stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.ReLU(inplace=True),
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)
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else:
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self.branch1 = nn.Sequential()
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self.branch2 = nn.Sequential(
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nn.Conv2d(inp if (self.stride > 1) else branch_features,
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branch_features, kernel_size=1,
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stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.ReLU(inplace=True),
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self.depthwise_conv(branch_features, branch_features,
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kernel_size=3, stride=self.stride, padding=1),
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nn.BatchNorm2d(branch_features),
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nn.Conv2d(branch_features, branch_features,
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kernel_size=1, stride=1, padding=0, bias=False),
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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(i, o, kernel_size, stride,
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padding, bias=bias, groups=i)
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def forward(self, x):
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def _inner_forward(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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if self.with_cp and x.requires_grad:
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out = cp.checkpoint(_inner_forward, x)
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else:
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out = _inner_forward(x)
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return out
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class ShuffleNetv2(BaseBackbone):
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"""ShuffleNetv2 backbone.
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Args:
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groups (int): number of groups to be used in grouped
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1x1 convolutions in each ShuffleUnit. Default is 3 for best
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performance according to original paper.
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widen_factor (float): Config of widen_factor.
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out_indices (Sequence[int]): Output from which stages.
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frozen_stages (int): Stages to be frozen (all param fixed). -1 means
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not freezing any parameters.
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bn_eval (bool): Whether to set nn.BatchNorm2d layers as eval mode,
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namely, freeze
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running stats (mean and var).
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bn_frozen (bool): Whether to freeze weight and bias of
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nn.BatchNorm2d layers.
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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"""
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def __init__(self,
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groups=3,
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widen_factor=1.0,
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out_indices=(0, 1, 2, 3),
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frozen_stages=-1,
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bn_eval=True,
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bn_frozen=False,
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with_cp=False):
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super(ShuffleNetv2, self).__init__()
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blocks = [4, 8, 4]
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self.groups = groups
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self.out_indices = out_indices
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self.frozen_stages = frozen_stages
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self.bn_eval = bn_eval
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self.bn_frozen = bn_frozen
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self.with_cp = with_cp
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if widen_factor == 0.5:
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channels = [48, 96, 192, 1024]
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elif widen_factor == 1.0:
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channels = [116, 232, 464, 1024]
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elif widen_factor == 1.5:
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channels = [176, 352, 704, 1024]
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elif widen_factor == 2.0:
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channels = [244, 488, 976, 2048]
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else:
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raise ValueError(
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"""{} groups is not supported for
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1x1 Grouped Convolutions""".format(groups))
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channels = [_make_divisible(ch * widen_factor, 8) for ch in channels]
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self.inplanes = channels[0]
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self.conv1 = conv_bn(3, self.inplanes, 2)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer2 = self._make_layer(channels[1], blocks[0], with_cp=with_cp)
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self.layer3 = self._make_layer(channels[2], blocks[1], with_cp=with_cp)
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self.layer4 = self._make_layer(channels[3], blocks[2], with_cp=with_cp)
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self.conv_out = conv_1x1_bn(self.inplanes, channels[-1])
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def init_weights(self, pretrained=None):
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if isinstance(pretrained, str):
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logger = logging.getLogger()
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load_checkpoint(self, pretrained, strict=False, logger=logger)
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elif pretrained is None:
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m)
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elif isinstance(m, nn.BatchNorm2d):
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constant_init(m, 1)
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else:
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raise TypeError('pretrained must be a str or None')
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def _make_layer(self,
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outplanes,
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blocks,
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with_cp):
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layers = []
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for i in range(blocks):
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if i == 0:
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layers.append(
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InvertedResidual(self.inplanes, outplanes,
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stride=2, with_cp=with_cp))
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else:
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layers.append(
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InvertedResidual(self.inplanes, outplanes,
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stride=1, with_cp=with_cp)
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)
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self.inplanes = outplanes
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.maxpool(x)
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outs = []
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if 0 in self.out_indices:
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outs.append(x)
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x = self.layer2(x)
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if 1 in self.out_indices:
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outs.append(x)
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x = self.layer3(x)
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if 2 in self.out_indices:
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outs.append(x)
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x = self.layer4(x)
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if 3 in self.out_indices:
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outs.append(x)
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x = self.conv_out(x)
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outs.append(x)
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if len(outs) == 1:
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return outs[0]
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else:
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return tuple(outs)
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def train(self, mode=True):
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super(ShuffleNetv2, self).train(mode)
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if self.bn_eval:
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for m in self.modules():
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if isinstance(m, nn.BatchNorm2d):
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m.eval()
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if self.bn_frozen:
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for params in m.parameters():
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params.requires_grad = False
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if mode and self.frozen_stages >= 0:
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for param in self.conv1.parameters():
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param.requires_grad = False
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for i in range(1, self.frozen_stages + 1):
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mod = getattr(self, 'layer{}'.format(i))
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mod.eval()
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for param in mod.parameters():
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param.requires_grad = False
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