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Update compute_dpg_bench.py #66

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27 changes: 16 additions & 11 deletions dpg_bench/compute_dpg_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,9 +70,6 @@ def prepare_dpg_data(args):
# 'item_id', 'text', 'keywords', 'proposition_id', 'dependency', 'category_broad', 'category_detailed', 'tuple', 'question_natural_language'
data = pd.read_csv(args.csv)
for i, line in data.iterrows():
if i == 0:
continue

current_id = line.item_id
qid = int(line.proposition_id)
dependency_list_str = line.dependency.split(',')
Expand Down Expand Up @@ -122,12 +119,12 @@ def compute_dpg_one_sample(args, question_dict, image_path, vqa_model, resolutio
qid2question = value['qid2question']
qid2dependency = value['qid2dependency']

qid2answer = dict()
qid2scores = dict()
qid2validity = dict()

scores = []
all_crops_qid2scores_orig = []
for crop_tuple in crop_tuples:
qid2answer = dict()
qid2scores = dict()
qid2validity = dict()
cropped_image = crop_image(generated_image, crop_tuple)
for id, question in qid2question.items():
answer = vqa_model.vqa(cropped_image, question)
Expand All @@ -136,6 +133,7 @@ def compute_dpg_one_sample(args, question_dict, image_path, vqa_model, resolutio
with open(args.res_path.replace('.txt', '_detail.txt'), 'a') as f:
f.write(image_path + ', ' + str(crop_tuple) + ', ' + question + ', ' + answer + '\n')
qid2scores_orig = qid2scores.copy()
all_crops_qid2scores_orig.append(qid2scores_orig)

for id, parent_ids in qid2dependency.items():
# zero-out scores if parent questions are answered 'no'
Expand All @@ -158,7 +156,16 @@ def compute_dpg_one_sample(args, question_dict, image_path, vqa_model, resolutio
with open(args.res_path, 'a') as f:
f.write(image_path + ', ' + ', '.join(str(i) for i in scores) + ', ' + str(average_score) + '\n')

return average_score, qid2tuple, qid2scores_orig
aggregated_qid2scores_orig = defaultdict(float)
num_crops = len(all_crops_qid2scores_orig)
if num_crops > 0:
for crop_scores in all_crops_qid2scores_orig:
for qid, s in crop_scores.items():
aggregated_qid2scores_orig[qid] += s
for qid in aggregated_qid2scores_orig:
aggregated_qid2scores_orig[qid] /= num_crops

return average_score, qid2tuple, dict(aggregated_qid2scores_orig)


def main():
Expand Down Expand Up @@ -188,9 +195,7 @@ def main():
vqa_model = getattr(vqa_model, 'module', vqa_model)

filename_list = os.listdir(args.image_root_path)
num_each_rank = len(filename_list) / accelerator.num_processes
local_rank = accelerator.process_index
local_filename_list = filename_list[round(local_rank * num_each_rank) : round((local_rank + 1) * num_each_rank)]
local_filename_list = filename_list[accelerator.process_index::accelerator.num_processes]

local_scores = []
local_category2scores = defaultdict(list)
Expand Down