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update the operator.

Signed-off-by: wxywb <xy.wang@zilliz.com>
main
wxywb 2 years ago
parent
commit
88c5f939a8
  1. 71
      albef.py
  2. 33
      configs/Grounding.yaml
  3. 25
      configs/NLVR.yaml
  4. 25
      configs/NLVR_pretrain.yaml
  5. 29
      configs/Pretrain.yaml
  6. 31
      configs/Retrieval_coco.yaml
  7. 31
      configs/Retrieval_flickr.yaml
  8. 25
      configs/VE.yaml
  9. 32
      configs/VQA.yaml
  10. 21
      configs/config_bert.json
  11. BIN
      models/__pycache__/__init__.cpython-38.pyc
  12. BIN
      models/__pycache__/model_retrieval.cpython-38.pyc
  13. BIN
      models/__pycache__/tokenization_bert.cpython-38.pyc
  14. BIN
      models/__pycache__/vit.cpython-38.pyc
  15. BIN
      models/__pycache__/xbert.cpython-38.pyc

71
albef.py

@ -18,19 +18,25 @@ from PIL import Image
import torch import torch
import yaml import yaml
from torchvision import transforms from torchvision import transforms
from urllib.parse import urlparse
from timm.models.hub import download_cached_file
from towhee.types.image_utils import to_pil from towhee.types.image_utils import to_pil
from towhee.operator.base import NNOperator, OperatorFlag from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color from towhee.types.arg import arg, to_image_color
from towhee import register from towhee import register
def is_url(url_or_filename):
parsed = urlparse(url_or_filename)
return parsed.scheme in ("http", "https")
@register(output_schema=['vec']) @register(output_schema=['vec'])
class Albef(NNOperator): class Albef(NNOperator):
""" """
ALBEF multi-modal embedding operator ALBEF multi-modal embedding operator
""" """
def prepare_model(checkpoint_path, model):
checkpoint = torch.load(checkpoint_path, map_location='cpu')
def prepare_model(self, checkpoint_path, model, interpolate_pos_embed):
checkpoint = self.load_checkpoint(checkpoint_path)
state_dict = checkpoint['model'] state_dict = checkpoint['model']
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
@ -42,22 +48,43 @@ class Albef(NNOperator):
state_dict[encoder_key] = state_dict[key] state_dict[encoder_key] = state_dict[key]
del state_dict[key] del state_dict[key]
msg = model.load_state_dict(state_dict,strict=False) msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from ' + checkpoint_path)
return model return model
def load_checkpoint(self, url_or_filename):
if is_url(url_or_filename):
cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
checkpoint = torch.load(cached_file, map_location='cpu')
elif os.path.isfile(url_or_filename):
checkpoint = torch.load(url_or_filename, map_location='cpu')
else:
raise RuntimeError('checkpoint url or path is invalid')
return checkpoint
def __init__(self, model_name: str, modality: str): def __init__(self, model_name: str, modality: str):
self.modality = modality self.modality = modality
config = self._configs()[model_name] config = self._configs()[model_name]
path = str(Path(__file__).parent)
sys.path.append(path)
from models.model_retrieval import ALBEF
from models.vit import interpolate_pos_embed
from models.tokenization_bert import BertTokenizer
sys.path.pop()
self.device = "cuda" if torch.cuda.is_available() else "cpu" self.device = "cuda" if torch.cuda.is_available() else "cpu"
normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
tokenizer = BertTokenizer.from_pretrained(config)
model = ALBEF(config=config, text_encoder=config['text_encoder'], tokenizer=tokenizer)
cfg = yaml.load(open(config['cfg'], 'r'), Loader=yaml.Loader)
checkpoint_path = cfg['ckpt_path']
tokenizer = BertTokenizer.from_pretrained(config['text_encoder'])
self.tokenizer = tokenizer
self.model = self.prepare_model(checkpoint_path, model)
cfg = yaml.load(open(path + '/' + config['cfg_path'], 'r'), Loader=yaml.Loader)
cfg['bert_config'] = path + '/' + cfg['bert_config']
model = ALBEF(config=cfg, text_encoder=config['text_encoder'], tokenizer=tokenizer)
checkpoint_path = config['weights']
self.model = self.prepare_model(checkpoint_path, model, interpolate_pos_embed)
self.test_transform = transforms.Compose([ self.test_transform = transforms.Compose([
transforms.Resize((cfg['image_res'],cfg['image_res']),interpolation=Image.BICUBIC), transforms.Resize((cfg['image_res'],cfg['image_res']),interpolation=Image.BICUBIC),
@ -90,16 +117,20 @@ class Albef(NNOperator):
return results return results
def _inference_from_text(self, text): def _inference_from_text(self, text):
tokens = self.text_tokenizer(text, return_tensors='pt', padding=True)['input_ids'].to(self.device)
text_features = self.text_encoder(tokens).logits
return text_features
text_input = self.tokenizer(text, padding='max_length', truncation=True, max_length=30, return_tensors="pt").to(self.device)
text_output = self.model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
text_feat = text_output.last_hidden_state
text_embed = self.model.text_proj(text_feat[:,0,:])
return text_embed
@arg(1, to_image_color('RGB')) @arg(1, to_image_color('RGB'))
def _inference_from_image(self, img): def _inference_from_image(self, img):
image = to_pil(img) image = to_pil(img)
image = self.processor(images=image, return_tensors="pt").to(self.device)
image_features = self.clip_model.get_image_features(**image)
return image_features
image = self.test_transform(image).unsqueeze(0)
image_feat = self.model.visual_encoder(image)
image_embed = self.model.vision_proj(image_feat[:,0,:])
return image_embed
def _configs(self): def _configs(self):
config = {} config = {}
@ -107,7 +138,11 @@ class Albef(NNOperator):
config['albef_4m']['tokenizer'] = 'bert-base-uncased' config['albef_4m']['tokenizer'] = 'bert-base-uncased'
config['albef_4m']['text_encoder'] = 'bert-base-uncased' config['albef_4m']['text_encoder'] = 'bert-base-uncased'
config['albef_4m']['cfg_path'] = './configs/Retrieval_flickr.yaml' config['albef_4m']['cfg_path'] = './configs/Retrieval_flickr.yaml'
config['albef_4m']['ckpt_path'] = ''
config['albef_4m']['weights'] = 'https://storage.googleapis.com/sfr-pcl-data-research/ALBEF/ALBEF_4M.pth'
config['albef_14m'] = {}
config['albef_14m']['tokenizer'] = 'bert-base-uncased'
config['albef_14m']['text_encoder'] = 'bert-base-uncased'
config['albef_14m']['cfg_path'] = './configs/Retrieval_flickr.yaml'
config['albef_14m']['weights'] = 'https://storage.googleapis.com/sfr-pcl-data-research/ALBEF/ALBEF.pth'
return config

33
configs/Grounding.yaml

@ -0,0 +1,33 @@
train_file: ['data/refcoco+_train.json']
test_file: ['data/refcoco+_val.json','data/refcoco+_test.json']
refcoco_data: 'data'
det_file: 'data/refcoco+/dets.json'
coco_file: 'data/refcoco+/cocos.json'
image_root: '/export/share/datasets/vision/coco/images/'
bert_config: 'configs/config_bert.json'
image_res: 384
batch_size: 32
queue_size: 65536
momentum: 0.995
vision_width: 768
embed_dim: 256
temp: 0.07
alpha: 0.4
distill: True
warm_up: True
optimizer: {opt: adamW, lr: 1e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 1e-5, epochs: 5, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

25
configs/NLVR.yaml

@ -0,0 +1,25 @@
train_file: ['data/nlvr_train.json']
val_file: ['data/nlvr_dev.json']
test_file: ['data/nlvr_test.json']
image_root: '/export/share/datasets/vision/NLVR2/'
image_res: 384
batch_size: 16
bert_config: 'configs/config_bert.json'
alpha: 0.4
distill: True
warm_up: True
eval_ema: False
optimizer: {opt: adamW, lr: 2e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 2e-5, epochs: 10, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

25
configs/NLVR_pretrain.yaml

@ -0,0 +1,25 @@
train_file: ['data/coco.json',
'data/vg.json',
'data/cc3m_train.json',
'data/cc3m_val.json',
'data/sbu.json'
]
# each train_file (json) contains a python list where each item is {'image': img_path, 'caption': text or list_of_text }
bert_config: 'configs/config_bert.json'
image_res: 256
vision_width: 768
embed_dim: 256
batch_size: 64
optimizer: {opt: adamW, lr: 2e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 2e-5, epochs: 1, min_lr: 1e-5, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

29
configs/Pretrain.yaml

@ -0,0 +1,29 @@
train_file: ['data/coco.json',
'data/vg.json',
'data/cc12m.json',
'data/cc3m_train.json',
'data/cc3m_val.json',
'data/sbu.json'
]
# each train_file (json) contains a python list where each item is {'image': img_path, 'caption': text or list_of_text }
bert_config: 'configs/config_bert.json'
image_res: 256
vision_width: 768
embed_dim: 256
batch_size: 64
temp: 0.07
mlm_probability: 0.15
queue_size: 65536
momentum: 0.995
alpha: 0.4
optimizer: {opt: adamW, lr: 1e-4, weight_decay: 0.02}
schedular: {sched: cosine, lr: 1e-4, epochs: 30, min_lr: 1e-5, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 20, cooldown_epochs: 0}

31
configs/Retrieval_coco.yaml

@ -0,0 +1,31 @@
train_file: ['data/coco_train.json']
val_file: 'data/coco_val.json'
test_file: 'data/coco_test.json'
image_root: '/export/share/datasets/vision/coco/images/'
bert_config: 'configs/config_bert.json'
image_res: 384
batch_size_train: 32
batch_size_test: 64
queue_size: 65536
momentum: 0.995
vision_width: 768
embed_dim: 256
temp: 0.07
k_test: 256
alpha: 0.4
distill: True
warm_up: True
optimizer: {opt: adamW, lr: 1e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 1e-5, epochs: 5, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

31
configs/Retrieval_flickr.yaml

@ -0,0 +1,31 @@
train_file: ['data/flickr30k_train.json']
val_file: 'data/flickr30k_val.json'
test_file: 'data/flickr30k_test.json'
image_root: '/export/share/datasets/vision/flickr30k/' #flickr30k-images/
bert_config: 'configs/config_bert.json'
image_res: 384
batch_size_train: 32
batch_size_test: 64
queue_size: 65536
momentum: 0.995
vision_width: 768
embed_dim: 256
temp: 0.07
k_test: 128
alpha: 0.4
distill: True
warm_up: True
optimizer: {opt: adamW, lr: 1e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 1e-5, epochs: 10, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

25
configs/VE.yaml

@ -0,0 +1,25 @@
train_file: 'data/ve_train.json'
val_file: 'data/ve_dev.json'
test_file: 'data/ve_test.json'
image_root: '/export/home/project/SNLI-VE/data/images'
image_res: 384
batch_size_train: 32
batch_size_test: 64
alpha: 0.4
distill: True
warm_up: False
bert_config: 'configs/config_bert.json'
optimizer: {opt: adamW, lr: 2e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 2e-5, epochs: 5, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 1, cooldown_epochs: 0}

32
configs/VQA.yaml

@ -0,0 +1,32 @@
train_file: ['data/vqa_train.json',
'data/vqa_val.json',
'data/vg_qa.json']
test_file: ['data/vqa_test.json']
answer_list: 'data/answer_list.json'
vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #train2014/
vg_root: '/export/share/datasets/vision/visual-genome/' #image/
image_res: 384
batch_size_train: 32
batch_size_test: 16
k_test: 128
alpha: 0.4
distill: True
warm_up: True
eos: '[SEP]'
bert_config: 'configs/config_bert.json'
optimizer: {opt: adamW, lr: 2e-5, weight_decay: 0.02}
schedular: {sched: cosine, lr: 2e-5, epochs: 8, min_lr: 1e-6, decay_rate: 1, warmup_lr: 1e-5, warmup_epochs: 4, cooldown_epochs: 0}

21
configs/config_bert.json

@ -0,0 +1,21 @@
{
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"type_vocab_size": 2,
"vocab_size": 30522,
"fusion_layer": 6,
"encoder_width": 768
}

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