187 lines
7.0 KiB
Python
187 lines
7.0 KiB
Python
import logging
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import torch.nn as nn
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from mmcv.cnn import ConvModule, constant_init, kaiming_init
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from mmcv.runner import load_checkpoint
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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
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from .base_backbone import BaseBackbone
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@BACKBONES.register_module()
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class MobileNetv3(BaseBackbone):
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"""MobileNetv3 backbone.
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Args:
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arch (str): Architechture of mobilnetv3, from {small, big}.
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Default: small.
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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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out_indices (None or Sequence[int]): Output from which stages.
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Default: (10, ), which means output tensors from final stage.
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frozen_stages (int): Stages to be frozen (all param fixed).
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Defualt: -1, which means not freezing any parameters.
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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
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some memory while slowing down the training speed.
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Defualt: False.
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"""
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# Parameters to build each block:
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# [kernel size, mid channels, out channels, with_se, act type, stride]
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arch_settings = {
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'small': [[3, 16, 16, True, 'ReLU', 2],
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[3, 72, 24, False, 'ReLU', 2],
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[3, 88, 24, False, 'ReLU', 1],
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[5, 96, 40, True, 'HSwish', 2],
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[5, 240, 40, True, 'HSwish', 1],
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[5, 240, 40, True, 'HSwish', 1],
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[5, 120, 48, True, 'HSwish', 1],
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[5, 144, 48, True, 'HSwish', 1],
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[5, 288, 96, True, 'HSwish', 2],
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[5, 576, 96, True, 'HSwish', 1],
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[5, 576, 96, True, 'HSwish', 1]],
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'big': [[3, 16, 16, False, 'ReLU', 1],
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[3, 64, 24, False, 'ReLU', 2],
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[3, 72, 24, False, 'ReLU', 1],
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[5, 72, 40, True, 'ReLU', 2],
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[5, 120, 40, True, 'ReLU', 1],
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[5, 120, 40, True, 'ReLU', 1],
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[3, 240, 80, False, 'HSwish', 2],
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[3, 200, 80, False, 'HSwish', 1],
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[3, 184, 80, False, 'HSwish', 1],
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[3, 184, 80, False, 'HSwish', 1],
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[3, 480, 112, True, 'HSwish', 1],
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[3, 672, 112, True, 'HSwish', 1],
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[5, 672, 160, True, 'HSwish', 1],
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[5, 672, 160, True, 'HSwish', 2],
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[5, 960, 160, True, 'HSwish', 1]]
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} # yapf: disable
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def __init__(self,
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arch='small',
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conv_cfg=None,
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norm_cfg=dict(type='BN'),
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out_indices=(10, ),
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frozen_stages=-1,
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norm_eval=False,
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with_cp=False):
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super(MobileNetv3, self).__init__()
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assert arch in self.arch_settings
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for index in out_indices:
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if index not in range(0, len(self.arch_settings[arch])):
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raise ValueError('the item in out_indices must in '
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f'range(0, {len(self.arch_settings[arch])}). '
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f'But received {index}')
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if frozen_stages not in range(-1, len(self.arch_settings[arch])):
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raise ValueError('frozen_stages must be in range(-1, '
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f'{len(self.arch_settings[arch])}). '
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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.arch = arch
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.out_indices = out_indices
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self.frozen_stages = frozen_stages
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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 = 16
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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=conv_cfg,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='HSwish'))
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self.layers = self._make_layer()
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self.feat_dim = self.arch_settings[arch][-1][2]
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def _make_layer(self):
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layers = []
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layer_setting = self.arch_settings[self.arch]
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for i, params in enumerate(layer_setting):
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(kernel_size, mid_channels, out_channels, with_se, act,
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stride) = params
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if with_se:
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se_cfg = dict(
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channels=mid_channels,
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ratio=4,
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act_cfg=(dict(type='ReLU'), dict(type='HSigmoid')))
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else:
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se_cfg = None
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layer = InvertedResidual(
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in_channels=self.in_channels,
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out_channels=out_channels,
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mid_channels=mid_channels,
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kernel_size=kernel_size,
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stride=stride,
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se_cfg=se_cfg,
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with_expand_conv=True,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=dict(type=act),
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with_cp=self.with_cp)
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self.in_channels = out_channels
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layer_name = 'layer{}'.format(i + 1)
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self.add_module(layer_name, layer)
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layers.append(layer_name)
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return layers
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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 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(MobileNetv3, 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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