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add timm-MobileNetV3 as an Encoder (#355)
* add timm-mobilenetv3 as encoder * fix import bug Co-authored-by: Pavel Yakubovskiy <[email protected]>
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segmentation_models_pytorch/encoders/timm_mobilenetv3.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,164 @@ | ||
| from timm import create_model | ||
| import torch.nn as nn | ||
| from ._base import EncoderMixin | ||
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| def make_divisible(x, divisible_by=8): | ||
| import numpy as np | ||
| return int(np.ceil(x * 1. / divisible_by) * divisible_by) | ||
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| class MobileNetV3Encoder(nn.Module, EncoderMixin): | ||
| def __init__(self, model, width_mult, depth=5, **kwargs): | ||
| super().__init__() | ||
| self._depth = depth | ||
| if 'small' in str(model): | ||
| self.mode = 'small' | ||
| self._out_channels = (16*width_mult, 16*width_mult, 24*width_mult, 48*width_mult, 576*width_mult) | ||
| self._out_channels = tuple(map(make_divisible, self._out_channels)) | ||
| elif 'large' in str(model): | ||
| self.mode = 'large' | ||
| self._out_channels = (16*width_mult, 24*width_mult, 40*width_mult, 112*width_mult, 960*width_mult) | ||
| self._out_channels = tuple(map(make_divisible, self._out_channels)) | ||
| else: | ||
| self.mode = 'None' | ||
| raise ValueError( | ||
| 'MobileNetV3 mode should be small or large, got {}'.format(self.mode)) | ||
| self._out_channels = (3,) + self._out_channels | ||
| self._in_channels = 3 | ||
| # minimal models replace hardswish with relu | ||
| model = create_model(model_name=model, | ||
| scriptable=True, # torch.jit scriptable | ||
| exportable=True, # onnx export | ||
| features_only=True) | ||
| self.conv_stem = model.conv_stem | ||
| self.bn1 = model.bn1 | ||
| self.act1 = model.act1 | ||
| self.blocks = model.blocks | ||
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| def get_stages(self): | ||
| if self.mode == 'small': | ||
| return [ | ||
| nn.Identity(), | ||
| nn.Sequential(self.conv_stem, self.bn1, self.act1), | ||
| self.blocks[0], | ||
| self.blocks[1], | ||
| self.blocks[2:4], | ||
| self.blocks[4:], | ||
| ] | ||
| elif self.mode == 'large': | ||
| return [ | ||
| nn.Identity(), | ||
| nn.Sequential(self.conv_stem, self.bn1, self.act1, self.blocks[0]), | ||
| self.blocks[1], | ||
| self.blocks[2], | ||
| self.blocks[3:5], | ||
| self.blocks[5:], | ||
| ] | ||
| else: | ||
| ValueError('MobileNetV3 mode should be small or large, got {}'.format(self.mode)) | ||
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| def forward(self, x): | ||
| stages = self.get_stages() | ||
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| features = [] | ||
| for i in range(self._depth + 1): | ||
| x = stages[i](x) | ||
| features.append(x) | ||
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| return features | ||
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| def load_state_dict(self, state_dict, **kwargs): | ||
| state_dict.pop('conv_head.weight') | ||
| state_dict.pop('conv_head.bias') | ||
| state_dict.pop('classifier.weight') | ||
| state_dict.pop('classifier.bias') | ||
| super().load_state_dict(state_dict, **kwargs) | ||
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| mobilenetv3_weights = { | ||
| 'tf_mobilenetv3_large_075': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth' | ||
| }, | ||
| 'tf_mobilenetv3_large_100': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth' | ||
| }, | ||
| 'tf_mobilenetv3_large_minimal_100': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth' | ||
| }, | ||
| 'tf_mobilenetv3_small_075': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth' | ||
| }, | ||
| 'tf_mobilenetv3_small_100': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth' | ||
| }, | ||
| 'tf_mobilenetv3_small_minimal_100': { | ||
| 'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth' | ||
| }, | ||
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| } | ||
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| pretrained_settings = {} | ||
| for model_name, sources in mobilenetv3_weights.items(): | ||
| pretrained_settings[model_name] = {} | ||
| for source_name, source_url in sources.items(): | ||
| pretrained_settings[model_name][source_name] = { | ||
| "url": source_url, | ||
| 'input_range': [0, 1], | ||
| 'mean': [0.485, 0.456, 0.406], | ||
| 'std': [0.229, 0.224, 0.225], | ||
| 'input_space': 'RGB', | ||
| } | ||
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| timm_mobilenetv3_encoders = { | ||
| 'timm-mobilenetv3_large_075': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_075'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_large_075', | ||
| 'width_mult': 0.75 | ||
| } | ||
| }, | ||
| 'timm-mobilenetv3_large_100': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_100'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_large_100', | ||
| 'width_mult': 1.0 | ||
| } | ||
| }, | ||
| 'timm-mobilenetv3_large_minimal_100': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_large_minimal_100'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_large_minimal_100', | ||
| 'width_mult': 1.0 | ||
| } | ||
| }, | ||
| 'timm-mobilenetv3_small_075': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_075'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_small_075', | ||
| 'width_mult': 0.75 | ||
| } | ||
| }, | ||
| 'timm-mobilenetv3_small_100': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_100'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_small_100', | ||
| 'width_mult': 1.0 | ||
| } | ||
| }, | ||
| 'timm-mobilenetv3_small_minimal_100': { | ||
| 'encoder': MobileNetV3Encoder, | ||
| 'pretrained_settings': pretrained_settings['tf_mobilenetv3_small_minimal_100'], | ||
| 'params': { | ||
| 'model': 'tf_mobilenetv3_small_minimal_100', | ||
| 'width_mult': 1.0 | ||
| } | ||
| }, | ||
| } |