284 lines
9.4 KiB
Python
284 lines
9.4 KiB
Python
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 mmcv.cnn import ConvModule, constant_init, kaiming_init
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.utils import channel_shuffle
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from ..builder import BACKBONES
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from .base_backbone import BaseBackbone
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class InvertedResidual(nn.Module):
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"""InvertedResidual block for ShuffleNetV2 backbone.
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Args:
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inplanes (int): The input channels of the block.
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planes (int): The output channels of the block.
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stride (int): Stride of the 3x3 convolution layer. Default: 1
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conv_cfg (dict): Config dict for convolution layer.
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Default: None, which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='BN').
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='ReLU').
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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. Default: False.
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Returns:
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Tensor: The output tensor.
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"""
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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with_cp=False):
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super(InvertedResidual, self).__init__()
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self.stride = stride
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self.with_cp = with_cp
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branch_features = planes // 2
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if self.stride == 1:
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assert inplanes == branch_features * 2, (
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f'inplanes ({inplanes}) should equal to branch_features * 2 '
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f'({branch_features * 2}) when stride is 1')
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if inplanes != branch_features * 2:
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assert self.stride != 1, (
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f'stride ({self.stride}) should not equal 1 when '
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f'inplanes != branch_features * 2')
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if self.stride > 1:
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self.branch1 = nn.Sequential(
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ConvModule(
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inplanes,
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inplanes,
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kernel_size=3,
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stride=self.stride,
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padding=1,
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groups=inplanes,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=None),
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ConvModule(
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inplanes,
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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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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg),
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)
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self.branch2 = nn.Sequential(
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ConvModule(
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inplanes 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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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg),
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ConvModule(
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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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groups=branch_features,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=None),
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ConvModule(
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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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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg))
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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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out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
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else:
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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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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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@BACKBONES.register_module()
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class ShuffleNetV2(BaseBackbone):
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"""ShuffleNetV2 backbone.
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Args:
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groups (int): The number of groups to be used in grouped 1x1
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convolutions in each InvertedResidual. Default: 3.
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widen_factor (float): Width multiplier - adjusts the number of
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channels in each layer by this amount. Default: 1.0.
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out_indices (Sequence[int]): Output from which stages.
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Default: (0, 1, 2, 3).
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frozen_stages (int): Stages to be frozen (all param fixed).
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Default: -1, which means not freezing any parameters.
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conv_cfg (dict): Config dict for convolution layer.
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Default: None, which means using conv2d.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='BN').
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act_cfg (dict): Config dict for activation layer.
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Default: dict(type='ReLU').
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norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only. Default: False.
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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. Default: False.
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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),
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frozen_stages=-1,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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norm_eval=False,
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with_cp=False):
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super(ShuffleNetV2, self).__init__()
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self.stage_blocks = [4, 8, 4]
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self.groups = groups
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self.out_indices = out_indices
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assert max(out_indices) < len(self.stage_blocks)
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self.frozen_stages = frozen_stages
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assert frozen_stages < len(self.stage_blocks)
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.act_cfg = act_cfg
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self.norm_eval = norm_eval
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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('widen_factor must be in [0.5, 1.0, 1.5, 2.0]. '
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f'But received {widen_factor}')
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self.inplanes = 24
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self.conv1 = ConvModule(
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in_channels=3,
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out_channels=self.inplanes,
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kernel_size=3,
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stride=2,
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padding=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layers = nn.ModuleList()
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for i, num_blocks in enumerate(self.stage_blocks):
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layer = self._make_layer(channels[i], num_blocks)
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self.layers.append(layer)
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output_channels = channels[-1]
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self.conv2 = ConvModule(
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in_channels=self.inplanes,
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out_channels=output_channels,
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kernel_size=1,
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conv_cfg=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg)
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def _make_layer(self, planes, num_blocks):
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""" Stack blocks to make a layer.
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Args:
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planes (int): planes of the block.
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num_blocks (int): number of blocks.
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"""
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layers = []
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for i in range(num_blocks):
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stride = 2 if i == 0 else 1
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layers.append(
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InvertedResidual(
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inplanes=self.inplanes,
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planes=planes,
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stride=stride,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg,
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with_cp=self.with_cp))
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self.inplanes = planes
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return nn.Sequential(*layers)
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def _freeze_stages(self):
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if 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(self.frozen_stages):
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m = self.layers[i]
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m.eval()
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for param in m.parameters():
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param.requires_grad = False
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def init_weights(self, pretrained=None):
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if 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, (_BatchNorm, nn.GroupNorm)):
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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. But received '
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f'{type(pretrained)}')
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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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for i, layer in enumerate(self.layers):
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x = layer(x)
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if i in self.out_indices:
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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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self._freeze_stages()
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if mode and self.norm_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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