104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
from ._base import EncoderMixin
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from timm.models.resnet import ResNet
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from timm.models.sknet import SelectiveKernelBottleneck, SelectiveKernelBasic
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import torch.nn as nn
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class SkNetEncoder(ResNet, EncoderMixin):
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def __init__(self, out_channels, depth=5, **kwargs):
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super().__init__(**kwargs)
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self._depth = depth
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self._out_channels = out_channels
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self._in_channels = 3
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del self.fc
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del self.global_pool
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def get_stages(self):
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return [
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nn.Identity(),
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nn.Sequential(self.conv1, self.bn1, self.act1),
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nn.Sequential(self.maxpool, self.layer1),
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self.layer2,
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self.layer3,
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self.layer4,
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]
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def forward(self, x):
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stages = self.get_stages()
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features = []
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for i in range(self._depth + 1):
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x = stages[i](x)
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features.append(x)
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return features
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def load_state_dict(self, state_dict, **kwargs):
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state_dict.pop("fc.bias", None)
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state_dict.pop("fc.weight", None)
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super().load_state_dict(state_dict, **kwargs)
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sknet_weights = {
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'timm-skresnet18': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth'
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},
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'timm-skresnet34': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth'
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},
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'timm-skresnext50_32x4d': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth',
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}
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}
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pretrained_settings = {}
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for model_name, sources in sknet_weights.items():
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pretrained_settings[model_name] = {}
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for source_name, source_url in sources.items():
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pretrained_settings[model_name][source_name] = {
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"url": source_url,
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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timm_sknet_encoders = {
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'timm-skresnet18': {
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'encoder': SkNetEncoder,
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"pretrained_settings": pretrained_settings["timm-skresnet18"],
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'params': {
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'out_channels': (3, 64, 64, 128, 256, 512),
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'block': SelectiveKernelBasic,
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'layers': [2, 2, 2, 2],
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'zero_init_last_bn': False,
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'block_args': {'sk_kwargs': {'rd_ratio': 1/8, 'split_input': True}}
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}
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},
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'timm-skresnet34': {
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'encoder': SkNetEncoder,
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"pretrained_settings": pretrained_settings["timm-skresnet34"],
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'params': {
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'out_channels': (3, 64, 64, 128, 256, 512),
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'block': SelectiveKernelBasic,
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'layers': [3, 4, 6, 3],
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'zero_init_last_bn': False,
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'block_args': {'sk_kwargs': {'rd_ratio': 1/8, 'split_input': True}}
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}
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},
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'timm-skresnext50_32x4d': {
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'encoder': SkNetEncoder,
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"pretrained_settings": pretrained_settings["timm-skresnext50_32x4d"],
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'params': {
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'out_channels': (3, 64, 256, 512, 1024, 2048),
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'block': SelectiveKernelBottleneck,
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'layers': [3, 4, 6, 3],
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'zero_init_last_bn': False,
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'cardinality': 32,
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'base_width': 4
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}
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}
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}
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