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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", # noqa
},
"timm-skresnet34": {
"imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth", # noqa
},
"timm-skresnext50_32x4d": {
"imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth", # noqa
},
}
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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