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|
| import copy |
|
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| from mmcv.cnn import ConvModule |
| from torch.nn.modules.batchnorm import _BatchNorm |
|
|
| from mmpose.registry import MODELS |
| from .base_backbone import BaseBackbone |
| from .utils import InvertedResidual |
|
|
|
|
| @MODELS.register_module() |
| class ViPNAS_MobileNetV3(BaseBackbone): |
| """ViPNAS_MobileNetV3 backbone. |
| |
| "ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search" |
| More details can be found in the `paper |
| <https://arxiv.org/abs/2105.10154>`__ . |
| |
| Args: |
| wid (list(int)): Searched width config for each stage. |
| expan (list(int)): Searched expansion ratio config for each stage. |
| dep (list(int)): Searched depth config for each stage. |
| ks (list(int)): Searched kernel size config for each stage. |
| group (list(int)): Searched group number config for each stage. |
| att (list(bool)): Searched attention config for each stage. |
| stride (list(int)): Stride config for each stage. |
| act (list(dict)): Activation config for each stage. |
| 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'). |
| frozen_stages (int): Stages to be frozen (all param fixed). |
| Default: -1, which means not freezing any parameters. |
| 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. |
| init_cfg (dict or list[dict], optional): Initialization config dict. |
| Default: |
| ``[ |
| dict(type='Normal', std=0.001, layer=['Conv2d']), |
| dict( |
| type='Constant', |
| val=1, |
| layer=['_BatchNorm', 'GroupNorm']) |
| ]`` |
| """ |
|
|
| def __init__( |
| self, |
| wid=[16, 16, 24, 40, 80, 112, 160], |
| expan=[None, 1, 5, 4, 5, 5, 6], |
| dep=[None, 1, 4, 4, 4, 4, 4], |
| ks=[3, 3, 7, 7, 5, 7, 5], |
| group=[None, 8, 120, 20, 100, 280, 240], |
| att=[None, True, True, False, True, True, True], |
| stride=[2, 1, 2, 2, 2, 1, 2], |
| act=['HSwish', 'ReLU', 'ReLU', 'ReLU', 'HSwish', 'HSwish', 'HSwish'], |
| conv_cfg=None, |
| norm_cfg=dict(type='BN'), |
| frozen_stages=-1, |
| norm_eval=False, |
| with_cp=False, |
| init_cfg=[ |
| dict(type='Normal', std=0.001, layer=['Conv2d']), |
| dict(type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) |
| ], |
| ): |
| |
| norm_cfg = copy.deepcopy(norm_cfg) |
| super().__init__(init_cfg=init_cfg) |
| self.wid = wid |
| self.expan = expan |
| self.dep = dep |
| self.ks = ks |
| self.group = group |
| self.att = att |
| self.stride = stride |
| self.act = act |
| self.conv_cfg = conv_cfg |
| self.norm_cfg = norm_cfg |
| self.frozen_stages = frozen_stages |
| self.norm_eval = norm_eval |
| self.with_cp = with_cp |
|
|
| self.conv1 = ConvModule( |
| in_channels=3, |
| out_channels=self.wid[0], |
| kernel_size=self.ks[0], |
| stride=self.stride[0], |
| padding=self.ks[0] // 2, |
| conv_cfg=conv_cfg, |
| norm_cfg=norm_cfg, |
| act_cfg=dict(type=self.act[0])) |
|
|
| self.layers = self._make_layer() |
|
|
| def _make_layer(self): |
| layers = [] |
| layer_index = 0 |
| for i, dep in enumerate(self.dep[1:]): |
| mid_channels = self.wid[i + 1] * self.expan[i + 1] |
|
|
| if self.att[i + 1]: |
| se_cfg = dict( |
| channels=mid_channels, |
| ratio=4, |
| act_cfg=(dict(type='ReLU'), |
| dict(type='HSigmoid', bias=1.0, divisor=2.0))) |
| else: |
| se_cfg = None |
|
|
| if self.expan[i + 1] == 1: |
| with_expand_conv = False |
| else: |
| with_expand_conv = True |
|
|
| for j in range(dep): |
| if j == 0: |
| stride = self.stride[i + 1] |
| in_channels = self.wid[i] |
| else: |
| stride = 1 |
| in_channels = self.wid[i + 1] |
|
|
| layer = InvertedResidual( |
| in_channels=in_channels, |
| out_channels=self.wid[i + 1], |
| mid_channels=mid_channels, |
| kernel_size=self.ks[i + 1], |
| groups=self.group[i + 1], |
| stride=stride, |
| se_cfg=se_cfg, |
| with_expand_conv=with_expand_conv, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=dict(type=self.act[i + 1]), |
| with_cp=self.with_cp) |
| layer_index += 1 |
| layer_name = f'layer{layer_index}' |
| self.add_module(layer_name, layer) |
| layers.append(layer_name) |
| return layers |
|
|
| def forward(self, x): |
| x = self.conv1(x) |
|
|
| for i, layer_name in enumerate(self.layers): |
| layer = getattr(self, layer_name) |
| x = layer(x) |
|
|
| return (x, ) |
|
|
| 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().train(mode) |
| self._freeze_stages() |
| if mode and self.norm_eval: |
| for m in self.modules(): |
| if isinstance(m, _BatchNorm): |
| m.eval() |
|
|
