125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
from timm.models import ByoModelCfg, ByoBlockCfg, ByobNet
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from ._base import EncoderMixin
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import torch.nn as nn
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class GERNetEncoder(ByobNet, EncoderMixin):
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def __init__(self, out_channels, depth=5, **kwargs):
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super().__init__(**kwargs)
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self._depth = depth
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self._out_channels = out_channels
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self._in_channels = 3
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del self.head
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def get_stages(self):
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return [
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nn.Identity(),
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self.stem,
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self.stages[0],
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self.stages[1],
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self.stages[2],
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nn.Sequential(self.stages[3], self.stages[4], self.final_conv)
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]
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def forward(self, x):
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stages = self.get_stages()
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features = []
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for i in range(self._depth + 1):
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x = stages[i](x)
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features.append(x)
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return features
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def load_state_dict(self, state_dict, **kwargs):
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state_dict.pop("head.fc.weight", None)
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state_dict.pop("head.fc.bias", None)
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super().load_state_dict(state_dict, **kwargs)
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regnet_weights = {
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'timm-gernet_s': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_s-756b4751.pth',
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},
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'timm-gernet_m': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_m-0873c53a.pth',
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},
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'timm-gernet_l': {
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'imagenet': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-ger-weights/gernet_l-f31e2e8d.pth',
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},
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}
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pretrained_settings = {}
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for model_name, sources in regnet_weights.items():
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pretrained_settings[model_name] = {}
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for source_name, source_url in sources.items():
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pretrained_settings[model_name][source_name] = {
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"url": source_url,
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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timm_gernet_encoders = {
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'timm-gernet_s': {
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'encoder': GERNetEncoder,
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"pretrained_settings": pretrained_settings["timm-gernet_s"],
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'params': {
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'out_channels': (3, 13, 48, 48, 384, 1920),
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'cfg': ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=48, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=3, c=48, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=7, c=384, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=2, c=560, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=1, c=256, s=1, gs=1, br=3.),
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),
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stem_chs=13,
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stem_pool=None,
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num_features=1920,
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)
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},
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},
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'timm-gernet_m': {
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'encoder': GERNetEncoder,
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"pretrained_settings": pretrained_settings["timm-gernet_m"],
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'params': {
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'out_channels': (3, 32, 128, 192, 640, 2560),
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'cfg': ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=4, c=640, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=1, c=640, s=1, gs=1, br=3.),
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),
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stem_chs=32,
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stem_pool=None,
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num_features=2560,
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)
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},
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},
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'timm-gernet_l': {
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'encoder': GERNetEncoder,
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"pretrained_settings": pretrained_settings["timm-gernet_l"],
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'params': {
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'out_channels': (3, 32, 128, 192, 640, 2560),
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'cfg': ByoModelCfg(
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blocks=(
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ByoBlockCfg(type='basic', d=1, c=128, s=2, gs=0, br=1.),
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ByoBlockCfg(type='basic', d=2, c=192, s=2, gs=0, br=1.),
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ByoBlockCfg(type='bottle', d=6, c=640, s=2, gs=0, br=1 / 4),
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ByoBlockCfg(type='bottle', d=5, c=640, s=2, gs=1, br=3.),
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ByoBlockCfg(type='bottle', d=4, c=640, s=1, gs=1, br=3.),
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),
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stem_chs=32,
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stem_pool=None,
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num_features=2560,
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)
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},
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},
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}
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