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from ._base import EncoderMixin | |
from timm.models.regnet import RegNet | |
import torch.nn as nn | |
class RegNetEncoder(RegNet, 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.s1, | |
self.s2, | |
self.s3, | |
self.s4, | |
] | |
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-regnetx_002': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth', | |
}, | |
'timm-regnetx_004': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth', | |
}, | |
'timm-regnetx_006': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth', | |
}, | |
'timm-regnetx_008': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth', | |
}, | |
'timm-regnetx_016': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth', | |
}, | |
'timm-regnetx_032': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth', | |
}, | |
'timm-regnetx_040': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth', | |
}, | |
'timm-regnetx_064': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth', | |
}, | |
'timm-regnetx_080': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth', | |
}, | |
'timm-regnetx_120': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth', | |
}, | |
'timm-regnetx_160': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth', | |
}, | |
'timm-regnetx_320': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth', | |
}, | |
'timm-regnety_002': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth', | |
}, | |
'timm-regnety_004': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth', | |
}, | |
'timm-regnety_006': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth', | |
}, | |
'timm-regnety_008': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth', | |
}, | |
'timm-regnety_016': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth', | |
}, | |
'timm-regnety_032': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth' | |
}, | |
'timm-regnety_040': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth' | |
}, | |
'timm-regnety_064': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth' | |
}, | |
'timm-regnety_080': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth', | |
}, | |
'timm-regnety_120': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth', | |
}, | |
'timm-regnety_160': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth', | |
}, | |
'timm-regnety_320': { | |
'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.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_size': [3, 224, 224], | |
'input_range': [0, 1], | |
'mean': [0.485, 0.456, 0.406], | |
'std': [0.229, 0.224, 0.225], | |
'num_classes': 1000 | |
} | |
# at this point I am too lazy to copy configs, so I just used the same configs from timm's repo | |
def _mcfg(**kwargs): | |
cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32) | |
cfg.update(**kwargs) | |
return cfg | |
timm_regnet_encoders = { | |
'timm-regnetx_002': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_002"], | |
'params': { | |
'out_channels': (3, 32, 24, 56, 152, 368), | |
'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13) | |
}, | |
}, | |
'timm-regnetx_004': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_004"], | |
'params': { | |
'out_channels': (3, 32, 32, 64, 160, 384), | |
'cfg': _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22) | |
}, | |
}, | |
'timm-regnetx_006': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_006"], | |
'params': { | |
'out_channels': (3, 32, 48, 96, 240, 528), | |
'cfg': _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16) | |
}, | |
}, | |
'timm-regnetx_008': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_008"], | |
'params': { | |
'out_channels': (3, 32, 64, 128, 288, 672), | |
'cfg': _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16) | |
}, | |
}, | |
'timm-regnetx_016': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_016"], | |
'params': { | |
'out_channels': (3, 32, 72, 168, 408, 912), | |
'cfg': _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18) | |
}, | |
}, | |
'timm-regnetx_032': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_032"], | |
'params': { | |
'out_channels': (3, 32, 96, 192, 432, 1008), | |
'cfg': _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25) | |
}, | |
}, | |
'timm-regnetx_040': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_040"], | |
'params': { | |
'out_channels': (3, 32, 80, 240, 560, 1360), | |
'cfg': _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23) | |
}, | |
}, | |
'timm-regnetx_064': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_064"], | |
'params': { | |
'out_channels': (3, 32, 168, 392, 784, 1624), | |
'cfg': _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17) | |
}, | |
}, | |
'timm-regnetx_080': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_080"], | |
'params': { | |
'out_channels': (3, 32, 80, 240, 720, 1920), | |
'cfg': _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23) | |
}, | |
}, | |
'timm-regnetx_120': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_120"], | |
'params': { | |
'out_channels': (3, 32, 224, 448, 896, 2240), | |
'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19) | |
}, | |
}, | |
'timm-regnetx_160': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_160"], | |
'params': { | |
'out_channels': (3, 32, 256, 512, 896, 2048), | |
'cfg': _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22) | |
}, | |
}, | |
'timm-regnetx_320': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnetx_320"], | |
'params': { | |
'out_channels': (3, 32, 336, 672, 1344, 2520), | |
'cfg': _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23) | |
}, | |
}, | |
#regnety | |
'timm-regnety_002': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_002"], | |
'params': { | |
'out_channels': (3, 32, 24, 56, 152, 368), | |
'cfg': _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_004': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_004"], | |
'params': { | |
'out_channels': (3, 32, 48, 104, 208, 440), | |
'cfg': _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_006': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_006"], | |
'params': { | |
'out_channels': (3, 32, 48, 112, 256, 608), | |
'cfg': _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_008': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_008"], | |
'params': { | |
'out_channels': (3, 32, 64, 128, 320, 768), | |
'cfg': _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_016': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_016"], | |
'params': { | |
'out_channels': (3, 32, 48, 120, 336, 888), | |
'cfg': _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_032': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_032"], | |
'params': { | |
'out_channels': (3, 32, 72, 216, 576, 1512), | |
'cfg': _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_040': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_040"], | |
'params': { | |
'out_channels': (3, 32, 128, 192, 512, 1088), | |
'cfg': _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_064': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_064"], | |
'params': { | |
'out_channels': (3, 32, 144, 288, 576, 1296), | |
'cfg': _mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_080': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_080"], | |
'params': { | |
'out_channels': (3, 32, 168, 448, 896, 2016), | |
'cfg': _mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_120': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_120"], | |
'params': { | |
'out_channels': (3, 32, 224, 448, 896, 2240), | |
'cfg': _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_160': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_160"], | |
'params': { | |
'out_channels': (3, 32, 224, 448, 1232, 3024), | |
'cfg': _mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25) | |
}, | |
}, | |
'timm-regnety_320': { | |
'encoder': RegNetEncoder, | |
"pretrained_settings": pretrained_settings["timm-regnety_320"], | |
'params': { | |
'out_channels': (3, 32, 232, 696, 1392, 3712), | |
'cfg': _mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25) | |
}, | |
}, | |
} | |