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