File size: 4,392 Bytes
be2715b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
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',
},
'timm-gernet_m': {
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth',
},
'timm-gernet_l': {
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth',
},
}
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.),
ByoBlockCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
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.),
ByoBlockCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
),
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.),
ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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.),
ByoBlockCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
),
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.),
ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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.),
ByoBlockCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
),
stem_chs=32,
stem_pool=None,
num_features=2560,
)
},
},
}
|