265 lines
9.4 KiB
Python
265 lines
9.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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
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from mmcv.runner import BaseModule
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.utils import make_divisible
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from ..builder import BACKBONES
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from .base_backbone import BaseBackbone
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class InvertedResidual(BaseModule):
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"""InvertedResidual block for MobileNetV2.
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Args:
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in_channels (int): The input channels of the InvertedResidual block.
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out_channels (int): The output channels of the InvertedResidual block.
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stride (int): Stride of the middle (first) 3x3 convolution.
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expand_ratio (int): adjusts number of channels of the hidden layer
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in InvertedResidual by this amount.
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conv_cfg (dict, optional): 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='ReLU6').
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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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in_channels,
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out_channels,
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stride,
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expand_ratio,
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU6'),
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with_cp=False,
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init_cfg=None):
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super(InvertedResidual, self).__init__(init_cfg)
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self.stride = stride
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assert stride in [1, 2], f'stride must in [1, 2]. ' \
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f'But received {stride}.'
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self.with_cp = with_cp
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self.use_res_connect = self.stride == 1 and in_channels == out_channels
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hidden_dim = int(round(in_channels * expand_ratio))
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layers = []
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if expand_ratio != 1:
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layers.append(
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ConvModule(
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in_channels=in_channels,
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out_channels=hidden_dim,
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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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layers.extend([
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ConvModule(
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in_channels=hidden_dim,
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out_channels=hidden_dim,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=hidden_dim,
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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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in_channels=hidden_dim,
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out_channels=out_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=None)
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])
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self.conv = nn.Sequential(*layers)
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def forward(self, x):
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def _inner_forward(x):
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if self.use_res_connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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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 MobileNetV2(BaseBackbone):
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"""MobileNetV2 backbone.
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Args:
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widen_factor (float): Width multiplier, multiply number of
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channels in each layer by this amount. Default: 1.0.
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out_indices (None or Sequence[int]): Output from which stages.
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Default: (7, ).
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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, optional): 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='ReLU6').
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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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# Parameters to build layers. 4 parameters are needed to construct a
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# layer, from left to right: expand_ratio, channel, num_blocks, stride.
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arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2],
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[6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2],
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[6, 320, 1, 1]]
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def __init__(self,
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widen_factor=1.,
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out_indices=(7, ),
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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='ReLU6'),
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norm_eval=False,
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with_cp=False,
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init_cfg=[
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dict(type='Kaiming', layer=['Conv2d']),
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dict(
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type='Constant',
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val=1,
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layer=['_BatchNorm', 'GroupNorm'])
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]):
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super(MobileNetV2, self).__init__(init_cfg)
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self.widen_factor = widen_factor
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self.out_indices = out_indices
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for index in out_indices:
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if index not in range(0, 8):
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raise ValueError('the item in out_indices must in '
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f'range(0, 8). But received {index}')
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if frozen_stages not in range(-1, 8):
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raise ValueError('frozen_stages must be in range(-1, 8). '
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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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self.in_channels = make_divisible(32 * widen_factor, 8)
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self.conv1 = ConvModule(
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in_channels=3,
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out_channels=self.in_channels,
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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=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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self.layers = []
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for i, layer_cfg in enumerate(self.arch_settings):
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expand_ratio, channel, num_blocks, stride = layer_cfg
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out_channels = make_divisible(channel * widen_factor, 8)
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inverted_res_layer = self.make_layer(
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out_channels=out_channels,
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num_blocks=num_blocks,
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stride=stride,
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expand_ratio=expand_ratio)
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layer_name = f'layer{i + 1}'
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self.add_module(layer_name, inverted_res_layer)
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self.layers.append(layer_name)
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if widen_factor > 1.0:
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self.out_channel = int(1280 * widen_factor)
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else:
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self.out_channel = 1280
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layer = ConvModule(
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in_channels=self.in_channels,
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out_channels=self.out_channel,
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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=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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self.add_module('conv2', layer)
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self.layers.append('conv2')
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def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
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"""Stack InvertedResidual blocks to build a layer for MobileNetV2.
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Args:
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out_channels (int): out_channels of block.
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num_blocks (int): number of blocks.
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stride (int): stride of the first block. Default: 1
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expand_ratio (int): Expand the number of channels of the
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hidden layer in InvertedResidual by this ratio. Default: 6.
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"""
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layers = []
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for i in range(num_blocks):
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if i >= 1:
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stride = 1
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layers.append(
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InvertedResidual(
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self.in_channels,
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out_channels,
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stride,
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expand_ratio=expand_ratio,
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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.in_channels = out_channels
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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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outs = []
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for i, layer_name in enumerate(self.layers):
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layer = getattr(self, layer_name)
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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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return tuple(outs)
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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(1, self.frozen_stages + 1):
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layer = getattr(self, f'layer{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 train(self, mode=True):
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super(MobileNetV2, 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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