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import logging |
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import torch.nn as nn |
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from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init |
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from annotator.uniformer.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, make_divisible |
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@BACKBONES.register_module() |
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class MobileNetV2(nn.Module): |
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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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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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""" |
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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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super(MobileNetV2, self).__init__() |
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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, 8). 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 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, (_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') |
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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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