from timm.models import ByoModelCfg, ByoBlockCfg, ByobNet from ._base import EncoderMixin import torch.nn as nn class GERNetEncoder(ByobNet, 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.head def get_stages(self): return [ nn.Identity(), self.stem, self.stages[0], self.stages[1], self.stages[2], nn.Sequential(self.stages[3], self.stages[4], self.final_conv), ] 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("head.fc.weight", None) state_dict.pop("head.fc.bias", None) super().load_state_dict(state_dict, **kwargs) regnet_weights = { "timm-gernet_s": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_s-756b4751.pth", # noqa }, "timm-gernet_m": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth", # noqa }, "timm-gernet_l": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth", # noqa }, } pretrained_settings = {} for model_name, sources in regnet_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], "num_classes": 1000, } timm_gernet_encoders = { "timm-gernet_s": { "encoder": GERNetEncoder, "pretrained_settings": pretrained_settings["timm-gernet_s"], "params": { "out_channels": (3, 13, 48, 48, 384, 1920), "cfg": ByoModelCfg( blocks=( ByoBlockCfg(type="basic", d=1, c=48, s=2, gs=0, br=1.0), ByoBlockCfg(type="basic", d=3, c=48, s=2, gs=0, br=1.0), ByoBlockCfg(type="bottle", d=7, c=384, s=2, gs=0, br=1 / 4), ByoBlockCfg(type="bottle", d=2, c=560, s=2, gs=1, br=3.0), ByoBlockCfg(type="bottle", d=1, c=256, s=1, gs=1, br=3.0), ), stem_chs=13, stem_pool=None, num_features=1920, ), }, }, "timm-gernet_m": { "encoder": GERNetEncoder, "pretrained_settings": pretrained_settings["timm-gernet_m"], "params": { "out_channels": (3, 32, 128, 192, 640, 2560), "cfg": ByoModelCfg( blocks=( ByoBlockCfg(type="basic", d=1, c=128, s=2, gs=0, br=1.0), ByoBlockCfg(type="basic", d=2, c=192, s=2, gs=0, br=1.0), ByoBlockCfg(type="bottle", d=6, c=640, s=2, gs=0, br=1 / 4), ByoBlockCfg(type="bottle", d=4, c=640, s=2, gs=1, br=3.0), ByoBlockCfg(type="bottle", d=1, c=640, s=1, gs=1, br=3.0), ), stem_chs=32, stem_pool=None, num_features=2560, ), }, }, "timm-gernet_l": { "encoder": GERNetEncoder, "pretrained_settings": pretrained_settings["timm-gernet_l"], "params": { "out_channels": (3, 32, 128, 192, 640, 2560), "cfg": ByoModelCfg( blocks=( ByoBlockCfg(type="basic", d=1, c=128, s=2, gs=0, br=1.0), ByoBlockCfg(type="basic", d=2, c=192, s=2, gs=0, br=1.0), ByoBlockCfg(type="bottle", d=6, c=640, s=2, gs=0, br=1 / 4), ByoBlockCfg(type="bottle", d=5, c=640, s=2, gs=1, br=3.0), ByoBlockCfg(type="bottle", d=4, c=640, s=1, gs=1, br=3.0), ), stem_chs=32, stem_pool=None, num_features=2560, ), }, }, }