deepgeek/MiniCPM-o
2
0
Fork 0
mirror of https://github.com/OpenBMB/MiniCPM-V.git synced 2026-02-04 17:59:18 +08:00
Code Issues Packages Projects Releases Wiki Activity

Modify eval_mm for MiniCPM-o 2.6

Browse Source
This commit is contained in:
Poppy Xu
2025-01-21 15:34:54 +08:00
parent ec68cefc17
commit d8f382e157
82 changed files with 14279 additions and 843 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
eval_mm/vqaeval/eval_utils/vqa_evaluate.py
View File

@@ -370,8 +370,6 @@ def evaluate_VQA(
generate_method="interleave",
answer_path='./answers',
):
print(f"answer path:{answer_path}")
sampler = None
if torch.distributed.is_initialized():
sampler=InferenceSampler(len(dataset))
@@ -383,8 +381,6 @@ def evaluate_VQA(
collate_fn=collate_fn_vqa
)
now_rank = torch.distributed.get_rank()
answer_dir = os.path.join(answer_path, model_name, time)
os.makedirs(answer_dir, exist_ok=True)
@@ -395,21 +391,15 @@ def evaluate_VQA(
predictions = []
for batch in tqdm(dataloader, desc="Running inference"):
image_paths, questions, gt_answers, ocr_tokens_list, question_ids, question_type = batch
image_paths, questions, gt_answers, ocr_tokens_list, question_ids, question_type = batch
with torch.no_grad():
if model_name != "minicpm":
if model_name != "codellama":
outputs = model.generate(images=image_paths, questions=questions, datasetname=dataset_name)
else:
outputs = model.generate()
elif model_name == "minicpm":
if generate_method == "old":
outputs = model.generate(images=image_paths, questions=questions, datasetname=dataset_name)
elif generate_method == "interleave":
outputs = model.generate_with_interleaved(images=image_paths, questions=questions, datasetname=dataset_name)
else:
raise Exception(f"Wrong generate paradigm {generate_method}!")
if generate_method == "old":
outputs = model.generate(images=image_paths, questions=questions, datasetname=dataset_name)
elif generate_method == "interleave":
outputs = model.generate_with_interleaved(images=image_paths, questions=questions, datasetname=dataset_name)
else:
raise Exception(f"Wrong generate paradigm {generate_method}!")
for i in range(len(outputs)):
answer_dict = {