Spaces:
Running
Running
File size: 7,415 Bytes
2908104 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import os
import numpy as np
import torch
#from timm.models import create_model
from .protonet import ProtoNet
from .deploy import ProtoNet_Finetune, ProtoNet_Auto_Finetune, ProtoNet_AdaTok, ProtoNet_AdaTok_EntMin
def get_backbone(args):
if args.arch == 'vit_base_patch16_224_in21k':
from .vit_google import VisionTransformer, CONFIGS
config = CONFIGS['ViT-B_16']
model = VisionTransformer(config, 224)
url = 'https://storage.googleapis.com/vit_models/imagenet21k/ViT-B_16.npz'
pretrained_weights = 'pretrained_ckpts/vit_base_patch16_224_in21k.npz'
if not os.path.exists(pretrained_weights):
try:
import wget
os.makedirs('pretrained_ckpts', exist_ok=True)
wget.download(url, pretrained_weights)
except:
print(f'Cannot download pretrained weights from {url}. Check if `pip install wget` works.')
model.load_from(np.load(pretrained_weights))
print('Pretrained weights found at {}'.format(pretrained_weights))
elif args.arch == 'dino_base_patch16':
from . import vision_transformer as vit
model = vit.__dict__['vit_base'](patch_size=16, num_classes=0)
url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth"
state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)
model.load_state_dict(state_dict, strict=True)
print('Pretrained weights found at {}'.format(url))
elif args.arch == 'deit_base_patch16':
from . import vision_transformer as vit
model = vit.__dict__['vit_base'](patch_size=16, num_classes=0)
url = "https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth"
state_dict = torch.hub.load_state_dict_from_url(url=url)["model"]
for k in ['head.weight', 'head.bias']:
if k in state_dict:
print(f"removing key {k} from pretrained checkpoint")
del state_dict[k]
model.load_state_dict(state_dict, strict=True)
print('Pretrained weights found at {}'.format(url))
elif args.arch == 'deit_small_patch16':
from . import vision_transformer as vit
model = vit.__dict__['vit_small'](patch_size=16, num_classes=0)
url = "https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth"
state_dict = torch.hub.load_state_dict_from_url(url=url)["model"]
for k in ['head.weight', 'head.bias']:
if k in state_dict:
print(f"removing key {k} from pretrained checkpoint")
del state_dict[k]
model.load_state_dict(state_dict, strict=True)
print('Pretrained weights found at {}'.format(url))
elif args.arch == 'dino_small_patch16':
from . import vision_transformer as vit
model = vit.__dict__['vit_small'](patch_size=16, num_classes=0)
if not args.no_pretrain:
url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth"
state_dict = torch.hub.load_state_dict_from_url(url="https://dl.fbaipublicfiles.com/dino/" + url)
model.load_state_dict(state_dict, strict=True)
print('Pretrained weights found at {}'.format(url))
elif args.arch == 'beit_base_patch16_224_pt22k':
from .beit import default_pretrained_model
model = default_pretrained_model(args)
print('Pretrained BEiT loaded')
elif args.arch == 'clip_base_patch16_224':
from . import clip
model, _ = clip.load('ViT-B/16', 'cpu')
elif args.arch == 'clip_resnet50':
from . import clip
model, _ = clip.load('RN50', 'cpu')
elif args.arch == 'dino_resnet50':
from torchvision.models.resnet import resnet50
model = resnet50(pretrained=False)
model.fc = torch.nn.Identity()
if not args.no_pretrain:
state_dict = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/dino/dino_resnet50_pretrain/dino_resnet50_pretrain.pth",
map_location="cpu",
)
model.load_state_dict(state_dict, strict=False)
elif args.arch == 'resnet50':
from torchvision.models.resnet import resnet50
pretrained = not args.no_pretrain
model = resnet50(pretrained=pretrained)
model.fc = torch.nn.Identity()
elif args.arch == 'resnet18':
from torchvision.models.resnet import resnet18
pretrained = not args.no_pretrain
model = resnet18(pretrained=pretrained)
model.fc = torch.nn.Identity()
elif args.arch == 'dino_xcit_medium_24_p16':
model = torch.hub.load('facebookresearch/xcit:main', 'xcit_medium_24_p16')
model.head = torch.nn.Identity()
state_dict = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/dino/dino_xcit_medium_24_p16_pretrain/dino_xcit_medium_24_p16_pretrain.pth",
map_location="cpu",
)
model.load_state_dict(state_dict, strict=False)
elif args.arch == 'dino_xcit_medium_24_p8':
model = torch.hub.load('facebookresearch/dino:main', 'dino_xcit_medium_24_p8')
elif args.arch == 'simclrv2_resnet50':
import sys
sys.path.insert(
0,
'cog',
)
import model_utils
model_utils.MODELS_ROOT_DIR = 'cog/models'
ckpt_file = os.path.join(args.pretrained_checkpoint_path, 'pretrained_ckpts/simclrv2_resnet50.pth')
resnet, _ = model_utils.load_pretrained_backbone(args.arch, ckpt_file)
class Wrapper(torch.nn.Module):
def __init__(self, model):
super(Wrapper, self).__init__()
self.model = model
def forward(self, x):
return self.model(x, apply_fc=False)
model = Wrapper(resnet)
elif args.arch in ['mocov2_resnet50', 'swav_resnet50', 'barlow_resnet50']:
from torchvision.models.resnet import resnet50
model = resnet50(pretrained=False)
ckpt_file = os.path.join(args.pretrained_checkpoint_path, 'pretrained_ckpts_converted/{}.pth'.format(args.arch))
ckpt = torch.load(ckpt_file)
msg = model.load_state_dict(ckpt, strict=False)
assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
# remove the fully-connected layer
model.fc = torch.nn.Identity()
else:
raise ValueError(f'{args.arch} is not conisdered in the current code.')
return model
def get_model(args):
backbone = get_backbone(args)
if args.deploy == 'vanilla':
model = ProtoNet(backbone)
elif args.deploy == 'finetune':
model = ProtoNet_Finetune(backbone, args.ada_steps, args.ada_lr, args.aug_prob, args.aug_types)
elif args.deploy == 'finetune_autolr':
model = ProtoNet_Auto_Finetune(backbone, args.ada_steps, args.aug_prob, args.aug_types)
elif args.deploy == 'ada_tokens':
model = ProtoNet_AdaTok(backbone, args.num_adapters,
args.ada_steps, args.ada_lr)
elif args.deploy == 'ada_tokens_entmin':
model = ProtoNet_AdaTok_EntMin(backbone, args.num_adapters,
args.ada_steps, args.ada_lr)
else:
raise ValueError(f'deploy method {args.deploy} is not supported.')
return model
|