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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,
),
},
},
}
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