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Upload encoders/timm_regnet.py
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encoders/timm_regnet.py
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1 |
+
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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7 |
+
def __init__(self, out_channels, depth=5, **kwargs):
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8 |
+
super().__init__(**kwargs)
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9 |
+
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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+
def get_stages(self):
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16 |
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return [
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17 |
+
nn.Identity(),
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18 |
+
self.stem,
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19 |
+
self.s1,
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20 |
+
self.s2,
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21 |
+
self.s3,
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22 |
+
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 |
+
stages = self.get_stages()
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27 |
+
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28 |
+
features = []
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29 |
+
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 |
+
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 |
+
state_dict.pop("head.fc.weight", None)
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37 |
+
state_dict.pop("head.fc.bias", None)
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38 |
+
super().load_state_dict(state_dict, **kwargs)
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39 |
+
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40 |
+
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41 |
+
regnet_weights = {
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42 |
+
'timm-regnetx_002': {
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43 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth',
|
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',
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50 |
+
},
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51 |
+
'timm-regnetx_008': {
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52 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth',
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53 |
+
},
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54 |
+
'timm-regnetx_016': {
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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': {
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58 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth',
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59 |
+
},
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60 |
+
'timm-regnetx_040': {
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61 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth',
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62 |
+
},
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63 |
+
'timm-regnetx_064': {
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64 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth',
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65 |
+
},
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66 |
+
'timm-regnetx_080': {
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67 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth',
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68 |
+
},
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69 |
+
'timm-regnetx_120': {
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70 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth',
|
71 |
+
},
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72 |
+
'timm-regnetx_160': {
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73 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth',
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74 |
+
},
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75 |
+
'timm-regnetx_320': {
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76 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth',
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77 |
+
},
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78 |
+
'timm-regnety_002': {
|
79 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth',
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80 |
+
},
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81 |
+
'timm-regnety_004': {
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82 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth',
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83 |
+
},
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84 |
+
'timm-regnety_006': {
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85 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth',
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86 |
+
},
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87 |
+
'timm-regnety_008': {
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88 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth',
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89 |
+
},
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90 |
+
'timm-regnety_016': {
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91 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth',
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92 |
+
},
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93 |
+
'timm-regnety_032': {
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94 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth'
|
95 |
+
},
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96 |
+
'timm-regnety_040': {
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97 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'
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98 |
+
},
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99 |
+
'timm-regnety_064': {
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100 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'
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101 |
+
},
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102 |
+
'timm-regnety_080': {
|
103 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth',
|
104 |
+
},
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105 |
+
'timm-regnety_120': {
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106 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth',
|
107 |
+
},
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108 |
+
'timm-regnety_160': {
|
109 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth',
|
110 |
+
},
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111 |
+
'timm-regnety_320': {
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112 |
+
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'
|
113 |
+
}
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114 |
+
}
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115 |
+
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116 |
+
pretrained_settings = {}
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117 |
+
for model_name, sources in regnet_weights.items():
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118 |
+
pretrained_settings[model_name] = {}
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119 |
+
for source_name, source_url in sources.items():
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120 |
+
pretrained_settings[model_name][source_name] = {
|
121 |
+
"url": source_url,
|
122 |
+
'input_size': [3, 224, 224],
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123 |
+
'input_range': [0, 1],
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124 |
+
'mean': [0.485, 0.456, 0.406],
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125 |
+
'std': [0.229, 0.224, 0.225],
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126 |
+
'num_classes': 1000
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127 |
+
}
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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)
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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,
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181 |
+
"pretrained_settings": pretrained_settings["timm-regnetx_032"],
|
182 |
+
'params': {
|
183 |
+
'out_channels': (3, 32, 96, 192, 432, 1008),
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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,
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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': {
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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 |
+
},
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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 |
+
}
|