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Upload encoders/timm_regnet.py
Browse files- encoders/timm_regnet.py +332 -0
encoders/timm_regnet.py
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| 1 |
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from ._base import EncoderMixin
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| 2 |
+
from timm.models.regnet import RegNet
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| 3 |
+
import torch.nn as nn
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| 4 |
+
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| 5 |
+
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| 6 |
+
class RegNetEncoder(RegNet, EncoderMixin):
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def __init__(self, out_channels, depth=5, **kwargs):
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| 8 |
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super().__init__(**kwargs)
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self._depth = depth
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| 10 |
+
self._out_channels = out_channels
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| 11 |
+
self._in_channels = 3
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| 12 |
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| 13 |
+
del self.head
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| 14 |
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| 15 |
+
def get_stages(self):
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| 16 |
+
return [
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| 17 |
+
nn.Identity(),
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| 18 |
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self.stem,
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| 19 |
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self.s1,
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| 20 |
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self.s2,
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| 21 |
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self.s3,
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| 22 |
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self.s4,
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| 23 |
+
]
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| 24 |
+
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| 25 |
+
def forward(self, x):
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| 26 |
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stages = self.get_stages()
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| 27 |
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| 28 |
+
features = []
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| 29 |
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for i in range(self._depth + 1):
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| 30 |
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x = stages[i](x)
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| 31 |
+
features.append(x)
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| 32 |
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| 33 |
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return features
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| 34 |
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| 35 |
+
def load_state_dict(self, state_dict, **kwargs):
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| 36 |
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state_dict.pop("head.fc.weight", None)
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| 37 |
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state_dict.pop("head.fc.bias", None)
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| 38 |
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super().load_state_dict(state_dict, **kwargs)
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| 39 |
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| 40 |
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| 41 |
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regnet_weights = {
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| 42 |
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'timm-regnetx_002': {
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| 43 |
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth',
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| 44 |
+
},
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| 45 |
+
'timm-regnetx_004': {
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| 46 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth',
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| 47 |
+
},
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| 48 |
+
'timm-regnetx_006': {
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| 49 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth',
|
| 50 |
+
},
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| 51 |
+
'timm-regnetx_008': {
|
| 52 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth',
|
| 53 |
+
},
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| 54 |
+
'timm-regnetx_016': {
|
| 55 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth',
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| 56 |
+
},
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| 57 |
+
'timm-regnetx_032': {
|
| 58 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth',
|
| 59 |
+
},
|
| 60 |
+
'timm-regnetx_040': {
|
| 61 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth',
|
| 62 |
+
},
|
| 63 |
+
'timm-regnetx_064': {
|
| 64 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth',
|
| 65 |
+
},
|
| 66 |
+
'timm-regnetx_080': {
|
| 67 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth',
|
| 68 |
+
},
|
| 69 |
+
'timm-regnetx_120': {
|
| 70 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth',
|
| 71 |
+
},
|
| 72 |
+
'timm-regnetx_160': {
|
| 73 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth',
|
| 74 |
+
},
|
| 75 |
+
'timm-regnetx_320': {
|
| 76 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth',
|
| 77 |
+
},
|
| 78 |
+
'timm-regnety_002': {
|
| 79 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth',
|
| 80 |
+
},
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| 81 |
+
'timm-regnety_004': {
|
| 82 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth',
|
| 83 |
+
},
|
| 84 |
+
'timm-regnety_006': {
|
| 85 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth',
|
| 86 |
+
},
|
| 87 |
+
'timm-regnety_008': {
|
| 88 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth',
|
| 89 |
+
},
|
| 90 |
+
'timm-regnety_016': {
|
| 91 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth',
|
| 92 |
+
},
|
| 93 |
+
'timm-regnety_032': {
|
| 94 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth'
|
| 95 |
+
},
|
| 96 |
+
'timm-regnety_040': {
|
| 97 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'
|
| 98 |
+
},
|
| 99 |
+
'timm-regnety_064': {
|
| 100 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'
|
| 101 |
+
},
|
| 102 |
+
'timm-regnety_080': {
|
| 103 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth',
|
| 104 |
+
},
|
| 105 |
+
'timm-regnety_120': {
|
| 106 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth',
|
| 107 |
+
},
|
| 108 |
+
'timm-regnety_160': {
|
| 109 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth',
|
| 110 |
+
},
|
| 111 |
+
'timm-regnety_320': {
|
| 112 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'
|
| 113 |
+
}
|
| 114 |
+
}
|
| 115 |
+
|
| 116 |
+
pretrained_settings = {}
|
| 117 |
+
for model_name, sources in regnet_weights.items():
|
| 118 |
+
pretrained_settings[model_name] = {}
|
| 119 |
+
for source_name, source_url in sources.items():
|
| 120 |
+
pretrained_settings[model_name][source_name] = {
|
| 121 |
+
"url": source_url,
|
| 122 |
+
'input_size': [3, 224, 224],
|
| 123 |
+
'input_range': [0, 1],
|
| 124 |
+
'mean': [0.485, 0.456, 0.406],
|
| 125 |
+
'std': [0.229, 0.224, 0.225],
|
| 126 |
+
'num_classes': 1000
|
| 127 |
+
}
|
| 128 |
+
|
| 129 |
+
# at this point I am too lazy to copy configs, so I just used the same configs from timm's repo
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
def _mcfg(**kwargs):
|
| 133 |
+
cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32)
|
| 134 |
+
cfg.update(**kwargs)
|
| 135 |
+
return cfg
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
timm_regnet_encoders = {
|
| 139 |
+
'timm-regnetx_002': {
|
| 140 |
+
'encoder': RegNetEncoder,
|
| 141 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_002"],
|
| 142 |
+
'params': {
|
| 143 |
+
'out_channels': (3, 32, 24, 56, 152, 368),
|
| 144 |
+
'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13)
|
| 145 |
+
},
|
| 146 |
+
},
|
| 147 |
+
'timm-regnetx_004': {
|
| 148 |
+
'encoder': RegNetEncoder,
|
| 149 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_004"],
|
| 150 |
+
'params': {
|
| 151 |
+
'out_channels': (3, 32, 32, 64, 160, 384),
|
| 152 |
+
'cfg': _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22)
|
| 153 |
+
},
|
| 154 |
+
},
|
| 155 |
+
'timm-regnetx_006': {
|
| 156 |
+
'encoder': RegNetEncoder,
|
| 157 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_006"],
|
| 158 |
+
'params': {
|
| 159 |
+
'out_channels': (3, 32, 48, 96, 240, 528),
|
| 160 |
+
'cfg': _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16)
|
| 161 |
+
},
|
| 162 |
+
},
|
| 163 |
+
'timm-regnetx_008': {
|
| 164 |
+
'encoder': RegNetEncoder,
|
| 165 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_008"],
|
| 166 |
+
'params': {
|
| 167 |
+
'out_channels': (3, 32, 64, 128, 288, 672),
|
| 168 |
+
'cfg': _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16)
|
| 169 |
+
},
|
| 170 |
+
},
|
| 171 |
+
'timm-regnetx_016': {
|
| 172 |
+
'encoder': RegNetEncoder,
|
| 173 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_016"],
|
| 174 |
+
'params': {
|
| 175 |
+
'out_channels': (3, 32, 72, 168, 408, 912),
|
| 176 |
+
'cfg': _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18)
|
| 177 |
+
},
|
| 178 |
+
},
|
| 179 |
+
'timm-regnetx_032': {
|
| 180 |
+
'encoder': RegNetEncoder,
|
| 181 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_032"],
|
| 182 |
+
'params': {
|
| 183 |
+
'out_channels': (3, 32, 96, 192, 432, 1008),
|
| 184 |
+
'cfg': _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25)
|
| 185 |
+
},
|
| 186 |
+
},
|
| 187 |
+
'timm-regnetx_040': {
|
| 188 |
+
'encoder': RegNetEncoder,
|
| 189 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_040"],
|
| 190 |
+
'params': {
|
| 191 |
+
'out_channels': (3, 32, 80, 240, 560, 1360),
|
| 192 |
+
'cfg': _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23)
|
| 193 |
+
},
|
| 194 |
+
},
|
| 195 |
+
'timm-regnetx_064': {
|
| 196 |
+
'encoder': RegNetEncoder,
|
| 197 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_064"],
|
| 198 |
+
'params': {
|
| 199 |
+
'out_channels': (3, 32, 168, 392, 784, 1624),
|
| 200 |
+
'cfg': _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17)
|
| 201 |
+
},
|
| 202 |
+
},
|
| 203 |
+
'timm-regnetx_080': {
|
| 204 |
+
'encoder': RegNetEncoder,
|
| 205 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_080"],
|
| 206 |
+
'params': {
|
| 207 |
+
'out_channels': (3, 32, 80, 240, 720, 1920),
|
| 208 |
+
'cfg': _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23)
|
| 209 |
+
},
|
| 210 |
+
},
|
| 211 |
+
'timm-regnetx_120': {
|
| 212 |
+
'encoder': RegNetEncoder,
|
| 213 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_120"],
|
| 214 |
+
'params': {
|
| 215 |
+
'out_channels': (3, 32, 224, 448, 896, 2240),
|
| 216 |
+
'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19)
|
| 217 |
+
},
|
| 218 |
+
},
|
| 219 |
+
'timm-regnetx_160': {
|
| 220 |
+
'encoder': RegNetEncoder,
|
| 221 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_160"],
|
| 222 |
+
'params': {
|
| 223 |
+
'out_channels': (3, 32, 256, 512, 896, 2048),
|
| 224 |
+
'cfg': _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22)
|
| 225 |
+
},
|
| 226 |
+
},
|
| 227 |
+
'timm-regnetx_320': {
|
| 228 |
+
'encoder': RegNetEncoder,
|
| 229 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_320"],
|
| 230 |
+
'params': {
|
| 231 |
+
'out_channels': (3, 32, 336, 672, 1344, 2520),
|
| 232 |
+
'cfg': _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23)
|
| 233 |
+
},
|
| 234 |
+
},
|
| 235 |
+
#regnety
|
| 236 |
+
'timm-regnety_002': {
|
| 237 |
+
'encoder': RegNetEncoder,
|
| 238 |
+
"pretrained_settings": pretrained_settings["timm-regnety_002"],
|
| 239 |
+
'params': {
|
| 240 |
+
'out_channels': (3, 32, 24, 56, 152, 368),
|
| 241 |
+
'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25)
|
| 242 |
+
},
|
| 243 |
+
},
|
| 244 |
+
'timm-regnety_004': {
|
| 245 |
+
'encoder': RegNetEncoder,
|
| 246 |
+
"pretrained_settings": pretrained_settings["timm-regnety_004"],
|
| 247 |
+
'params': {
|
| 248 |
+
'out_channels': (3, 32, 48, 104, 208, 440),
|
| 249 |
+
'cfg': _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25)
|
| 250 |
+
},
|
| 251 |
+
},
|
| 252 |
+
'timm-regnety_006': {
|
| 253 |
+
'encoder': RegNetEncoder,
|
| 254 |
+
"pretrained_settings": pretrained_settings["timm-regnety_006"],
|
| 255 |
+
'params': {
|
| 256 |
+
'out_channels': (3, 32, 48, 112, 256, 608),
|
| 257 |
+
'cfg': _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25)
|
| 258 |
+
},
|
| 259 |
+
},
|
| 260 |
+
'timm-regnety_008': {
|
| 261 |
+
'encoder': RegNetEncoder,
|
| 262 |
+
"pretrained_settings": pretrained_settings["timm-regnety_008"],
|
| 263 |
+
'params': {
|
| 264 |
+
'out_channels': (3, 32, 64, 128, 320, 768),
|
| 265 |
+
'cfg': _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25)
|
| 266 |
+
},
|
| 267 |
+
},
|
| 268 |
+
'timm-regnety_016': {
|
| 269 |
+
'encoder': RegNetEncoder,
|
| 270 |
+
"pretrained_settings": pretrained_settings["timm-regnety_016"],
|
| 271 |
+
'params': {
|
| 272 |
+
'out_channels': (3, 32, 48, 120, 336, 888),
|
| 273 |
+
'cfg': _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25)
|
| 274 |
+
},
|
| 275 |
+
},
|
| 276 |
+
'timm-regnety_032': {
|
| 277 |
+
'encoder': RegNetEncoder,
|
| 278 |
+
"pretrained_settings": pretrained_settings["timm-regnety_032"],
|
| 279 |
+
'params': {
|
| 280 |
+
'out_channels': (3, 32, 72, 216, 576, 1512),
|
| 281 |
+
'cfg': _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25)
|
| 282 |
+
},
|
| 283 |
+
},
|
| 284 |
+
'timm-regnety_040': {
|
| 285 |
+
'encoder': RegNetEncoder,
|
| 286 |
+
"pretrained_settings": pretrained_settings["timm-regnety_040"],
|
| 287 |
+
'params': {
|
| 288 |
+
'out_channels': (3, 32, 128, 192, 512, 1088),
|
| 289 |
+
'cfg': _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25)
|
| 290 |
+
},
|
| 291 |
+
},
|
| 292 |
+
'timm-regnety_064': {
|
| 293 |
+
'encoder': RegNetEncoder,
|
| 294 |
+
"pretrained_settings": pretrained_settings["timm-regnety_064"],
|
| 295 |
+
'params': {
|
| 296 |
+
'out_channels': (3, 32, 144, 288, 576, 1296),
|
| 297 |
+
'cfg': _mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25)
|
| 298 |
+
},
|
| 299 |
+
},
|
| 300 |
+
'timm-regnety_080': {
|
| 301 |
+
'encoder': RegNetEncoder,
|
| 302 |
+
"pretrained_settings": pretrained_settings["timm-regnety_080"],
|
| 303 |
+
'params': {
|
| 304 |
+
'out_channels': (3, 32, 168, 448, 896, 2016),
|
| 305 |
+
'cfg': _mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25)
|
| 306 |
+
},
|
| 307 |
+
},
|
| 308 |
+
'timm-regnety_120': {
|
| 309 |
+
'encoder': RegNetEncoder,
|
| 310 |
+
"pretrained_settings": pretrained_settings["timm-regnety_120"],
|
| 311 |
+
'params': {
|
| 312 |
+
'out_channels': (3, 32, 224, 448, 896, 2240),
|
| 313 |
+
'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25)
|
| 314 |
+
},
|
| 315 |
+
},
|
| 316 |
+
'timm-regnety_160': {
|
| 317 |
+
'encoder': RegNetEncoder,
|
| 318 |
+
"pretrained_settings": pretrained_settings["timm-regnety_160"],
|
| 319 |
+
'params': {
|
| 320 |
+
'out_channels': (3, 32, 224, 448, 1232, 3024),
|
| 321 |
+
'cfg': _mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25)
|
| 322 |
+
},
|
| 323 |
+
},
|
| 324 |
+
'timm-regnety_320': {
|
| 325 |
+
'encoder': RegNetEncoder,
|
| 326 |
+
"pretrained_settings": pretrained_settings["timm-regnety_320"],
|
| 327 |
+
'params': {
|
| 328 |
+
'out_channels': (3, 32, 232, 696, 1392, 3712),
|
| 329 |
+
'cfg': _mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25)
|
| 330 |
+
},
|
| 331 |
+
},
|
| 332 |
+
}
|