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import copy |
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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 torch.nn.modules.batchnorm import _BatchNorm |
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from ..builder import BACKBONES |
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from .base_backbone import BaseBackbone |
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from .utils import InvertedResidual, load_checkpoint |
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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): Architecture of mobilnetv3, from {small, big}. |
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Default: small. |
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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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out_indices (None or Sequence[int]): Output from which stages. |
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Default: (-1, ), 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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Default: -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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Default: False. |
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""" |
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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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} |
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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=(-1, ), |
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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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norm_cfg = copy.deepcopy(norm_cfg) |
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super().__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(-len(self.arch_settings[arch]), |
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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.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 = f'layer{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 or \ |
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i - len(self.layers) 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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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().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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