305 lines
11 KiB
Python
305 lines
11 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, build_activation_layer, build_conv_layer,
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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, make_divisible
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from ..builder import BACKBONES
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from .base_backbone import BaseBackbone
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class ShuffleUnit(nn.Module):
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"""ShuffleUnit block.
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ShuffleNet unit with pointwise group convolution (GConv) and channel
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shuffle.
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Args:
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inplanes (int): The input channels of the ShuffleUnit.
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planes (int): The output channels of the ShuffleUnit.
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groups (int, optional): The number of groups to be used in grouped 1x1
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convolutions in each ShuffleUnit. Default: 3
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first_block (bool, optional): Whether it is the first ShuffleUnit of a
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sequential ShuffleUnits. Default: True, which means using the
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grouped 1x1 convolution.
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combine (str, optional): The ways to combine the input and output
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branches. Default: 'add'.
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conv_cfg (dict): Config dict for convolution layer. Default: None,
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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, optional): Use checkpoint or not. Using checkpoint
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will save some memory while slowing down the training speed.
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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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groups=3,
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first_block=True,
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combine='add',
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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(ShuffleUnit, self).__init__()
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self.inplanes = inplanes
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self.planes = planes
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self.first_block = first_block
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self.combine = combine
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self.groups = groups
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self.bottleneck_channels = self.planes // 4
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self.with_cp = with_cp
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if self.combine == 'add':
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self.depthwise_stride = 1
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self._combine_func = self._add
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assert inplanes == planes, (
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'inplanes must be equal to planes when combine is add')
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elif self.combine == 'concat':
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self.depthwise_stride = 2
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self._combine_func = self._concat
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self.planes -= self.inplanes
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else:
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raise ValueError(f'Cannot combine tensors with {self.combine}. '
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'Only "add" and "concat" are supported')
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self.first_1x1_groups = self.groups if first_block else 1
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self.g_conv_1x1_compress = ConvModule(
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in_channels=self.inplanes,
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out_channels=self.bottleneck_channels,
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kernel_size=1,
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groups=self.first_1x1_groups,
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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.depthwise_conv3x3_bn = ConvModule(
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in_channels=self.bottleneck_channels,
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out_channels=self.bottleneck_channels,
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kernel_size=3,
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stride=self.depthwise_stride,
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padding=1,
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groups=self.bottleneck_channels,
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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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self.g_conv_1x1_expand = ConvModule(
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in_channels=self.bottleneck_channels,
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out_channels=self.planes,
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kernel_size=1,
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groups=self.groups,
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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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self.avgpool = nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
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self.act = build_activation_layer(act_cfg)
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@staticmethod
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def _add(x, out):
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# residual connection
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return x + out
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@staticmethod
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def _concat(x, out):
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# concatenate along channel axis
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return torch.cat((x, out), 1)
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def forward(self, x):
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def _inner_forward(x):
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residual = x
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if self.combine == 'concat':
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residual = self.avgpool(residual)
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out = self.g_conv_1x1_compress(x)
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out = channel_shuffle(out, self.groups)
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out = self.depthwise_conv3x3_bn(out)
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out = self.g_conv_1x1_expand(out)
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out = self._combine_func(residual, out)
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out = self.act(out)
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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 ShuffleNetV1(BaseBackbone):
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"""ShuffleNetV1 backbone.
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Args:
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groups (int, optional): The number of groups to be used in grouped 1x1
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convolutions in each ShuffleUnit. Default: 3.
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widen_factor (float, optional): Width multiplier - adjusts the number
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of 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. Default: None,
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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, 3),
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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(ShuffleNetV1, self).__init__()
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self.stage_blocks = [3, 7, 3]
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self.groups = groups
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for indice in out_indices:
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if indice not in range(0, 4):
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raise ValueError('the item in out_indices must in '
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f'range(0, 4). But received {indice}')
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if frozen_stages not in range(-1, 4):
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raise ValueError('frozen_stages must be in range(-1, 4). '
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f'But received {frozen_stages}')
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self.out_indices = out_indices
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self.frozen_stages = frozen_stages
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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 groups == 1:
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channels = (144, 288, 576)
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elif groups == 2:
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channels = (200, 400, 800)
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elif groups == 3:
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channels = (240, 480, 960)
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elif groups == 4:
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channels = (272, 544, 1088)
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elif groups == 8:
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channels = (384, 768, 1536)
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else:
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raise ValueError(f'{groups} groups is not supported for 1x1 '
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'Grouped Convolutions')
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channels = [make_divisible(ch * widen_factor, 8) for ch in channels]
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self.inplanes = int(24 * widen_factor)
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self.conv1 = build_conv_layer(
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self.conv_cfg,
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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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bias=False)
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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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first_block = False if i == 0 else True
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layer = self.make_layer(channels[i], num_blocks, first_block)
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self.layers.append(layer)
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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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layer = self.layers[i]
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layer.eval()
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for param in layer.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 make_layer(self, planes, num_blocks, first_block=True):
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""" Stack ShuffleUnit 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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first_block (bool, optional): Whether is the first ShuffleUnit of a
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sequential ShuffleUnits. Default: True, which means using the
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grouped 1x1 convolution.
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"""
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layers = []
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for i in range(num_blocks):
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first_block = first_block if i == 0 else True
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combine_mode = 'concat' if i == 0 else 'add'
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layers.append(
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ShuffleUnit(
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self.inplanes,
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planes,
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groups=self.groups,
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first_block=first_block,
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combine=combine_mode,
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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 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(ShuffleNetV1, 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, _BatchNorm):
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m.eval()
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