198 lines
7.5 KiB
Python
198 lines
7.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import warnings
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import torch.nn as nn
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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 ..builder import BACKBONES
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from ..utils import InvertedResidual, make_divisible
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@BACKBONES.register_module()
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class MobileNetV2(BaseModule):
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"""MobileNetV2 backbone.
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This backbone is the implementation of
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`MobileNetV2: Inverted Residuals and Linear Bottlenecks
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<https://arxiv.org/abs/1801.04381>`_.
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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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strides (Sequence[int], optional): Strides of the first block of each
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layer. If not specified, default config in ``arch_setting`` will
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be used.
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dilations (Sequence[int]): Dilation of each layer.
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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): 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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pretrained (str, optional): model pretrained path. Default: None
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Default: None
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"""
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# Parameters to build layers. 3 parameters are needed to construct a
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# layer, from left to right: expand_ratio, channel, num_blocks.
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arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
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[6, 96, 3], [6, 160, 3], [6, 320, 1]]
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def __init__(self,
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widen_factor=1.,
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strides=(1, 2, 2, 2, 1, 2, 1),
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dilations=(1, 1, 1, 1, 1, 1, 1),
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out_indices=(1, 2, 4, 6),
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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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pretrained=None,
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init_cfg=None):
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super(MobileNetV2, self).__init__(init_cfg)
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self.pretrained = pretrained
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assert not (init_cfg and pretrained), \
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'init_cfg and pretrained cannot be setting at the same time'
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if isinstance(pretrained, str):
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warnings.warn('DeprecationWarning: pretrained is a deprecated, '
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'please use "init_cfg" instead')
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self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
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elif pretrained is None:
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if init_cfg is None:
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self.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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else:
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raise TypeError('pretrained must be a str or None')
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self.widen_factor = widen_factor
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self.strides = strides
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self.dilations = dilations
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assert len(strides) == len(dilations) == len(self.arch_settings)
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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, 7):
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raise ValueError('the item in out_indices must in '
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f'range(0, 7). But received {index}')
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if frozen_stages not in range(-1, 7):
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raise ValueError('frozen_stages must be in range(-1, 7). '
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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 = layer_cfg
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stride = self.strides[i]
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dilation = self.dilations[i]
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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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dilation=dilation,
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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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def make_layer(self, out_channels, num_blocks, stride, dilation,
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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.
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dilation (int): Dilation of the first block.
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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.
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"""
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layers = []
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for i in range(num_blocks):
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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 if i == 0 else 1,
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expand_ratio=expand_ratio,
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dilation=dilation if i == 0 else 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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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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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 _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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