segmentation_models_pytorch/encoders/timm_sknet.py

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,
        },
    },
}