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| import logging | |
| import torch.nn as nn | |
| from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init | |
| from annotator.uniformer.mmcv.runner import load_checkpoint | |
| from torch.nn.modules.batchnorm import _BatchNorm | |
| from ..builder import BACKBONES | |
| from ..utils import InvertedResidual, make_divisible | |
| class MobileNetV2(nn.Module): | |
| """MobileNetV2 backbone. | |
| Args: | |
| widen_factor (float): Width multiplier, multiply number of | |
| channels in each layer by this amount. Default: 1.0. | |
| strides (Sequence[int], optional): Strides of the first block of each | |
| layer. If not specified, default config in ``arch_setting`` will | |
| be used. | |
| dilations (Sequence[int]): Dilation of each layer. | |
| out_indices (None or Sequence[int]): Output from which stages. | |
| Default: (7, ). | |
| frozen_stages (int): Stages to be frozen (all param fixed). | |
| Default: -1, which means not freezing any parameters. | |
| conv_cfg (dict): Config dict for convolution layer. | |
| Default: None, which means using conv2d. | |
| norm_cfg (dict): Config dict for normalization layer. | |
| Default: dict(type='BN'). | |
| act_cfg (dict): Config dict for activation layer. | |
| Default: dict(type='ReLU6'). | |
| norm_eval (bool): Whether to set norm layers to eval mode, namely, | |
| freeze running stats (mean and var). Note: Effect on Batch Norm | |
| and its variants only. Default: False. | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. Default: False. | |
| """ | |
| # Parameters to build layers. 3 parameters are needed to construct a | |
| # layer, from left to right: expand_ratio, channel, num_blocks. | |
| arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4], | |
| [6, 96, 3], [6, 160, 3], [6, 320, 1]] | |
| def __init__(self, | |
| widen_factor=1., | |
| strides=(1, 2, 2, 2, 1, 2, 1), | |
| dilations=(1, 1, 1, 1, 1, 1, 1), | |
| out_indices=(1, 2, 4, 6), | |
| frozen_stages=-1, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU6'), | |
| norm_eval=False, | |
| with_cp=False): | |
| super(MobileNetV2, self).__init__() | |
| self.widen_factor = widen_factor | |
| self.strides = strides | |
| self.dilations = dilations | |
| assert len(strides) == len(dilations) == len(self.arch_settings) | |
| self.out_indices = out_indices | |
| for index in out_indices: | |
| if index not in range(0, 7): | |
| raise ValueError('the item in out_indices must in ' | |
| f'range(0, 8). But received {index}') | |
| if frozen_stages not in range(-1, 7): | |
| raise ValueError('frozen_stages must be in range(-1, 7). ' | |
| f'But received {frozen_stages}') | |
| self.out_indices = out_indices | |
| self.frozen_stages = frozen_stages | |
| self.conv_cfg = conv_cfg | |
| self.norm_cfg = norm_cfg | |
| self.act_cfg = act_cfg | |
| self.norm_eval = norm_eval | |
| self.with_cp = with_cp | |
| self.in_channels = make_divisible(32 * widen_factor, 8) | |
| self.conv1 = ConvModule( | |
| in_channels=3, | |
| out_channels=self.in_channels, | |
| kernel_size=3, | |
| stride=2, | |
| padding=1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg) | |
| self.layers = [] | |
| for i, layer_cfg in enumerate(self.arch_settings): | |
| expand_ratio, channel, num_blocks = layer_cfg | |
| stride = self.strides[i] | |
| dilation = self.dilations[i] | |
| out_channels = make_divisible(channel * widen_factor, 8) | |
| inverted_res_layer = self.make_layer( | |
| out_channels=out_channels, | |
| num_blocks=num_blocks, | |
| stride=stride, | |
| dilation=dilation, | |
| expand_ratio=expand_ratio) | |
| layer_name = f'layer{i + 1}' | |
| self.add_module(layer_name, inverted_res_layer) | |
| self.layers.append(layer_name) | |
| def make_layer(self, out_channels, num_blocks, stride, dilation, | |
| expand_ratio): | |
| """Stack InvertedResidual blocks to build a layer for MobileNetV2. | |
| Args: | |
| out_channels (int): out_channels of block. | |
| num_blocks (int): Number of blocks. | |
| stride (int): Stride of the first block. | |
| dilation (int): Dilation of the first block. | |
| expand_ratio (int): Expand the number of channels of the | |
| hidden layer in InvertedResidual by this ratio. | |
| """ | |
| layers = [] | |
| for i in range(num_blocks): | |
| layers.append( | |
| InvertedResidual( | |
| self.in_channels, | |
| out_channels, | |
| stride if i == 0 else 1, | |
| expand_ratio=expand_ratio, | |
| dilation=dilation if i == 0 else 1, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| act_cfg=self.act_cfg, | |
| with_cp=self.with_cp)) | |
| self.in_channels = out_channels | |
| return nn.Sequential(*layers) | |
| def init_weights(self, pretrained=None): | |
| if isinstance(pretrained, str): | |
| logger = logging.getLogger() | |
| load_checkpoint(self, pretrained, strict=False, logger=logger) | |
| elif pretrained is None: | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| kaiming_init(m) | |
| elif isinstance(m, (_BatchNorm, nn.GroupNorm)): | |
| constant_init(m, 1) | |
| else: | |
| raise TypeError('pretrained must be a str or None') | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| outs = [] | |
| for i, layer_name in enumerate(self.layers): | |
| layer = getattr(self, layer_name) | |
| x = layer(x) | |
| if i in self.out_indices: | |
| outs.append(x) | |
| if len(outs) == 1: | |
| return outs[0] | |
| else: | |
| return tuple(outs) | |
| def _freeze_stages(self): | |
| if self.frozen_stages >= 0: | |
| for param in self.conv1.parameters(): | |
| param.requires_grad = False | |
| for i in range(1, self.frozen_stages + 1): | |
| layer = getattr(self, f'layer{i}') | |
| layer.eval() | |
| for param in layer.parameters(): | |
| param.requires_grad = False | |
| def train(self, mode=True): | |
| super(MobileNetV2, self).train(mode) | |
| self._freeze_stages() | |
| if mode and self.norm_eval: | |
| for m in self.modules(): | |
| if isinstance(m, _BatchNorm): | |
| m.eval() | |