Spaces:
Runtime error
Runtime error
File size: 7,466 Bytes
eb8427a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
import pathlib
import tempfile
from collections import OrderedDict
from typing import Tuple, Union
import logging
import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from timm.models.layers import DropPath, trunc_normal_
from .image_encoder import build_image_encoder
from .text_encoder import build_text_encoder
from .text_encoder import build_tokenizer
from .templates import DEFAULT_TEMPLATES
logger = logging.getLogger(__name__)
class UniCLModel(nn.Module):
def __init__(self, config: dict,):
super().__init__()
self.conf_lang_encoder = config['MODEL']['TEXT_ENCODER']
self.tokenizer = build_tokenizer(self.conf_lang_encoder)
self.text_encoder = build_text_encoder(self.conf_lang_encoder, self.tokenizer, config['VERBOSE'])
dim_projection = config['MODEL']['DIM_PROJECTION']
if hasattr(self.text_encoder, 'dim_out'):
dim_out = self.text_encoder.dim_out
else:
with torch.no_grad():
dim_out = self.text_encoder(
torch.zeros(1,1).type(torch.LongTensor)
)['last_hidden_state'].size(2)
self.text_projection = nn.Parameter(torch.empty(dim_out, dim_projection))
self.conf_image_encoder = config['MODEL']['IMAGE_ENCODER']
self.image_encoder = build_image_encoder(self.conf_image_encoder)
self.image_projection = nn.Parameter(
torch.empty(self.image_encoder.dim_out, dim_projection)
)
self.logit_scale = nn.Parameter(torch.ones([]))
trunc_normal_(self.text_projection, std=.02)
trunc_normal_(self.image_projection, std=.02)
def _convert_old_weights(self, model_dict):
model_dict_updated = {}
for k, v in model_dict.items():
if k.startswith('visual.'):
model_dict_updated['image_encoder.'+k[7:]] = v
elif k.startswith('text.'):
model_dict_updated['lang_encoder.'+k[5:]] = v
elif k == 'vision_projection':
model_dict_updated['image_projection'] = v
elif k == 'text_projection':
model_dict_updated['text_projection'] = v
else:
model_dict_updated[k] = v
return model_dict_updated
def from_pretrained(self, pretrained='', pretrained_layers=[], verbose=True):
if not os.path.isfile(pretrained):
logger.warning(f'=> Pretrained model ({pretrained}) is not a file, skip init weight')
return
pretrained_dict = torch.load(pretrained, map_location='cpu')
logger.info(f'=> Loading pretrained model {pretrained}')
pretrained_dict = self._convert_old_weights(pretrained_dict)
model_dict = self.state_dict()
pretrained_dict = {
k: v for k, v in pretrained_dict.items()
if k in model_dict.keys()
}
need_init_state_dict = {}
image_encoder_state_dict = {}
for k, v in pretrained_dict.items():
need_init = (
k.split('.')[0] in pretrained_layers
or pretrained_layers[0] == '*'
)
if need_init:
if k.startswith('image_encoder.'):
image_encoder_state_dict[k] = v
else:
if verbose:
logger.info(f'=> init {k} from {pretrained}')
need_init_state_dict[k] = v
self.image_encoder.from_state_dict(image_encoder_state_dict, ['*'], verbose)
self.load_state_dict(need_init_state_dict, strict=False)
@torch.jit.ignore
def no_weight_decay(self):
no_weight_decay = {'logit_scale'}
if hasattr(self.text_encoder, 'no_weight_decay'):
for k in self.text_encoder.no_weight_decay():
no_weight_decay.add('lang_encoder.'+k)
if hasattr(self.image_encoder, 'no_weight_decay'):
for k in self.image_encoder.no_weight_decay():
no_weight_decay.add('image_encoder.'+k)
return no_weight_decay
@property
def dtype(self):
return self.logit_scale.dtype
def get_imnet_embeddings(self):
templates = IMAGENET_DEFAULT_TEMPLATES[:1]
clss_embeddings = []
for clss in IMAGENET_CLASSES:
txts = [template.format(clss) for template in templates]
tokens = self.tokenizer(
txts, padding='max_length', truncation=True, max_length=77, return_tensors='pt'
)
tokens = {key:(val.cuda() if next(self.parameters()).is_cuda else val) for key,val in tokens.items()}
clss_embedding = self.encode_text(tokens)
clss_embedding = clss_embedding.mean(dim=0)
clss_embedding /= clss_embedding.norm()
clss_embeddings.append(clss_embedding)
imnet_text_embeddings = torch.stack(clss_embeddings, dim=0)
return imnet_text_embeddings
def get_text_embeddings(self, texts):
templates = DEFAULT_TEMPLATES[:1]
clss_embeddings = []
for clss in texts:
txts = [template.format(clss) for template in templates]
tokens = self.tokenizer(
txts, padding='max_length', truncation=True, max_length=77, return_tensors='pt'
)
tokens = {key:(val.cuda() if next(self.parameters()).is_cuda else val) for key,val in tokens.items()}
clss_embedding = self.encode_text(tokens)
clss_embedding = clss_embedding.mean(dim=0)
clss_embedding /= clss_embedding.norm()
clss_embeddings.append(clss_embedding)
imnet_text_embeddings = torch.stack(clss_embeddings, dim=0)
return imnet_text_embeddings
def encode_image(self, image, norm=True):
x = self.image_encoder.forward_features(image)
x = x @ self.image_projection
if norm:
x = x / x.norm(dim=-1, keepdim=True)
return x
def encode_text(self, text, norm=True):
x = self.text_encoder(**text)
x = x['last_hidden_state']
if self.conf_lang_encoder['TOKENIZER'] == 'clip':
x = x[torch.arange(x.size(0)), text['input_ids'].argmax(dim=-1)]
else:
x = x[:, 0]
x = x @ self.text_projection
if norm:
x = x / x.norm(dim=-1, keepdim=True)
return x
def forward(self, image, text):
features_image = self.encode_image(image)
features_text = self.encode_text(text)
# cosine similarity as logits
T = self.logit_scale.exp()
return features_image, features_text, T
def build_unicl_model(config, **kwargs):
model = UniCLModel(config)
if config['MODEL']['PRETRAINED'] != '':
pretrained_path = config['MODEL']['PRETRAINED']
from ..Utils.Utils import is_valid_url, download_file
if is_valid_url(pretrained_path):
with tempfile.TemporaryDirectory() as tmp_path:
file_local_path = pathlib.Path(tmp_path) / 'base_model.pt'
download_file(pretrained_path, file_local_path)
model.from_pretrained(str(file_local_path), config['MODEL']['PRETRAINED_LAYERS'], config['VERBOSE'])
else:
model.from_pretrained(pretrained_path, config['MODEL']['PRETRAINED_LAYERS'], config['VERBOSE'])
return model
|