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eval.py
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import json
import sys
from src.arguments import ModelArguments, DataArguments, TrainingArguments
from transformers import HfArgumentParser, AutoProcessor, AutoConfig
from src.model import MMEBModel
from src.dataset import EvalDataset
from src.collator import EvalCollator
from torch.utils.data import DataLoader
import torch
from tqdm import tqdm
import numpy as np
import pickle
import os
from datasets import load_dataset
from evaluation.eval_utils import get_pred
from src.utils import print_rank
from src.model_utils import get_backbone_name
def batch_to_device(batch, device):
_batch = {}
for key, value in batch.items():
if isinstance(value, torch.Tensor):
_batch[key] = value.to(device)
else:
_batch[key] = value
return _batch
def main():
for arg in sys.argv:
if arg.startswith("--local-rank="):
rank = arg.split("=")[1]
sys.argv.remove(arg)
sys.argv.append('--local_rank')
sys.argv.append(rank)
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model_args: ModelArguments
data_args: DataArguments
training_args: TrainingArguments
os.makedirs(data_args.encode_output_path, exist_ok=True)
processor = AutoProcessor.from_pretrained(
model_args.model_name,
trust_remote_code=True,
num_crops=model_args.num_crops,
)
hf_config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
model_backbone = get_backbone_name(hf_config=hf_config)
setattr(model_args, 'model_backbone', model_backbone)
setattr(training_args, 'model_backbone', model_backbone)
print_rank(f'model_backbone: {model_backbone}')
model = MMEBModel.load(model_args)
model.eval()
model = model.to(training_args.device, dtype=torch.bfloat16)
eval_collator = EvalCollator(
data_args=data_args,
model_args=model_args,
processor=processor,
)
# ToDo: This part of code is a little bit hacky. Need to refactor later.
for idx, subset in enumerate(data_args.subset_name):
score_path = os.path.join(data_args.encode_output_path, f"{subset}_score.json")
if os.path.exists(score_path):
try:
with open(score_path, "r") as f:
score_dict = json.load(f)
print(f"Found previous eval score, skipping {subset}")
print(score_dict)
except Exception as e:
pass
print(f"\033[91m{idx+1}/{len(data_args.subset_name)}: Processing {subset} now!\033[0m")
encode_qry_path = os.path.join(data_args.encode_output_path, f"{subset}_qry")
encode_tgt_path = os.path.join(data_args.encode_output_path, f"{subset}_tgt")
if os.path.exists(encode_qry_path) and os.path.exists(encode_tgt_path):
continue
eval_qry_dataset = EvalDataset(
data_args=data_args,
model_args=model_args,
subset=subset,
text_field="qry_text",
img_path_field="qry_img_path",
)
eval_tgt_dataset = EvalDataset(
data_args=data_args,
model_args=model_args,
subset=subset,
text_field="tgt_text",
img_path_field="tgt_img_path",
)
eval_qry_loader = DataLoader(
eval_qry_dataset,
batch_size=training_args.per_device_eval_batch_size,
collate_fn=eval_collator,
shuffle=False,
drop_last=False,
num_workers=training_args.dataloader_num_workers,
)
eval_tgt_loader = DataLoader(
eval_tgt_dataset,
batch_size=training_args.per_device_eval_batch_size,
collate_fn=eval_collator,
shuffle=False,
drop_last=False,
num_workers=training_args.dataloader_num_workers,
)
encoded_tensor = []
with torch.no_grad():
for batch in tqdm(eval_qry_loader, desc="Encode query"):
batch = batch_to_device(batch, training_args.device)
with torch.autocast(enabled=True, dtype=torch.bfloat16, device_type="cuda"):
output = model(qry=batch)
encoded_tensor.append(output["qry_reps"].cpu().detach().float().numpy())
encoded_tensor = np.concatenate(encoded_tensor)
with open(encode_qry_path, 'wb') as f:
pickle.dump((encoded_tensor, eval_qry_dataset.paired_data), f)
encoded_tensor = []
with torch.no_grad():
for batch in tqdm(eval_tgt_loader, desc="Encode target"):
batch = batch_to_device(batch, training_args.device)
output = model(tgt=batch)
encoded_tensor.append(output["tgt_reps"].cpu().detach().float().numpy())
encoded_tensor = np.concatenate(encoded_tensor)
with open(encode_tgt_path, 'wb') as f:
pickle.dump((encoded_tensor, eval_tgt_dataset.paired_data), f)
for subset in tqdm(data_args.subset_name, desc="calculate score"):
encode_qry_path = os.path.join(data_args.encode_output_path, f"{subset}_qry")
encode_tgt_path = os.path.join(data_args.encode_output_path, f"{subset}_tgt")
with open(encode_qry_path, 'rb') as f:
qry_tensor, qry_index = pickle.load(f)
with open(encode_tgt_path, 'rb') as f:
tgt_tensor, tgt_index = pickle.load(f)
qry_dict, tgt_dict = {}, {}
for qry_t, tt in zip(qry_tensor, qry_index):
text, img_path = tt["text"], tt["img_path"]
qry_dict[(text, img_path)] = qry_t
for tgt_t, tt in zip(tgt_tensor, tgt_index):
text, img_path = tt["text"], tt["img_path"]
tgt_dict[(text, img_path)] = tgt_t
eval_data = load_dataset(
data_args.dataset_name,
subset,
split=data_args.dataset_split,
)
n_correct = 0
all_pred = []
for row in eval_data:
qry_t = qry_dict[(row["qry_text"], row["qry_img_path"])] # (dim,)
tgt_t, all_candidates = [], []
for tt in zip(row["tgt_text"], row["tgt_img_path"]):
tgt_t.append(tgt_dict[tt])
all_candidates.append(tt)
tgt_t = np.stack(tgt_t, axis=0) # (num_candidate, dim)
scores, pred = get_pred(qry_t, tgt_t, normalization=model_args.normalize)
if pred == 0:
n_correct += 1
all_pred.append(all_candidates[pred])
with open(os.path.join(data_args.encode_output_path, f"{subset}_pred.txt"), "w") as f:
for item in all_pred:
f.write(f"{item}\n")
score_path = os.path.join(data_args.encode_output_path, f"{subset}_score.json")
print(f"Outputting final score to: {score_path}")
with open(score_path, "w") as f:
score_dict = {"acc": n_correct/len(eval_data), "num_correct": n_correct, "num_pred": len(eval_data)}
json.dump(score_dict, f, indent=4)
print(f"\033[91m{subset} accuracy: {n_correct/len(eval_data)}\033[0m")
if __name__ == "__main__":
main()