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import os
import json
from tasks.eval.eval_utils import (
dump_json,
load_json,
EvalDataset,
)
def check_ans(pred, gt):
flag = False
pred_list = pred.lower().split(' ')
pred_option, pred_content = pred_list[0], ' '.join(pred_list[1:])
gt_list = gt.lower().split(' ')
gt_option, gt_content = gt_list[0], ' '.join(gt_list[1:])
if gt_content[-1] == '.':
gt_content = gt_content[:-1]
if not any([c in pred_option for c in 'abcdefgABCDEFG']):
print(f"model doesn't follow instructions: {pred}")
elif pred_option.replace('.', '') in gt_option:
flag = True
elif gt_option in pred_option:
flag = True
return flag
def save_results(result_list, save_path):
final_res, acc_dict = {}, {}
correct, total = 0, 0
for res in result_list:
task_type = res['task_type']
if task_type not in acc_dict:
acc_dict[task_type] = [0, 0] # correct, total
acc_dict[task_type][1] += 1
total += 1
pred = res['pred']
gt = res['gt']
if check_ans(pred=pred, gt=gt):
acc_dict[task_type][0] += 1
correct += 1
for k, v in acc_dict.items():
final_res[k] = v[0] / v[1] * 100
correct += v[0]
total += v[1]
final_res['Avg'] = correct / total * 100
all_results = {
"acc_dict": acc_dict,
"result_list": result_list
}
dump_json(all_results, save_path, 'all_results.json')
dump_json(final_res, save_path, 'upload_leaderboard.json')
def load_results(save_path):
all_results = load_json(save_path, 'all_results.json')
if all_results is not None:
result_list = all_results['result_list']
else:
result_list = None
# json_data = load_json(save_path, 'all_results.json')['result_list']
return result_list
class MVBenchDataset(EvalDataset):
data_list_info = {
# "task_type (sub task name)": ("json file name", "image/video prefix", "data_type", "bound")
"Action Sequence": ("action_sequence.json", "DATAS/MVBench/video/star/Charades_v1_480/", "video", True), # has start & end
"Action Prediction": ("action_prediction.json", "DATAS/MVBench/video/star/Charades_v1_480/", "video", True), # has start & end
"Action Antonym": ("action_antonym.json", "DATAS/MVBench/video/ssv2_video/", "video", False),
"Fine-grained Action": ("fine_grained_action.json", "DATAS/MVBench/video/Moments_in_Time_Raw/videos/", "video", False),
"Unexpected Action": ("unexpected_action.json", "DATAS/MVBench/video/FunQA_test/test/", "video", False),
"Object Existence": ("object_existence.json", "DATAS/MVBench/video/clevrer/video_validation/", "video", False),
"Object Interaction": ("object_interaction.json", "DATAS/MVBench/video/star/Charades_v1_480/", "video", True), # has start & end
"Object Shuffle": ("object_shuffle.json", "DATAS/MVBench/video/perception/videos/", "video", False),
"Moving Direction": ("moving_direction.json", "DATAS/MVBench/video/clevrer/video_validation/", "video", False),
"Action Localization": ("action_localization.json", "DATAS/MVBench/video/sta/sta_video/", "video", True), # has start & end
"Scene Transition": ("scene_transition.json", "DATAS/MVBench/video/scene_qa/video/", "video", False),
"Action Count": ("action_count.json", "DATAS/MVBench/video/perception/videos/", "video", False),
"Moving Count": ("moving_count.json", "DATAS/MVBench/video/clevrer/video_validation/", "video", False),
"Moving Attribute": ("moving_attribute.json", "DATAS/MVBench/video/clevrer/video_validation/", "video", False),
"State Change": ("state_change.json", "DATAS/MVBench/video/perception/videos/", "video", False),
"Fine-grained Pose": ("fine_grained_pose.json", "DATAS/MVBench/video/nturgbd/", "video", False),
"Character Order": ("character_order.json", "DATAS/MVBench/video/perception/videos/", "video", False),
"Egocentric Navigation": ("egocentric_navigation.json", "DATAS/MVBench/video/vlnqa/", "video", False),
"Episodic Reasoning": ("episodic_reasoning.json", "DATAS/MVBench/video/tvqa/frames_fps3_hq/", "frame", True), # has start & end, read frame
"Counterfactual Inference": ("counterfactual_inference.json", "DATAS/MVBench/video/clevrer/video_validation/", "video", False),
}
data_dir = "DATAS/MVBench/json"
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
data_list_info = self.data_list_info
data_dir = self.data_dir
self.data_list = []
for k, v in data_list_info.items():
with open(os.path.join(data_dir, v[0]), 'r') as f:
json_data = json.load(f)
for data in json_data:
self.data_list.append({
'task_type': k,
'prefix': v[1],
'data_type': v[2],
'bound': v[3],
'data': data
})
# self.data_list = self.data_list[:100] # for debug
self.decord_method = {
'video': self.read_video,
'gif': self.read_gif,
'frame': self.read_frame,
}
# # transform
# crop_size = resolution
# scale_size = resolution
# input_mean = [0.48145466, 0.4578275, 0.40821073]
# input_std = [0.26862954, 0.26130258, 0.27577711]
# self.transform = T.Compose([
# GroupScale(int(scale_size), interpolation=InterpolationMode.BICUBIC),
# GroupCenterCrop(crop_size),
# Stack(),
# ToTorchFormatTensor(),
# GroupNormalize(input_mean, input_std)
# ])
def __getitem__(self, idx):
question, answer = self.qa_template(self.data_list[idx]['data'])
task_type = self.data_list[idx]['task_type']
decord_method = self.decord_method[self.data_list[idx]['data_type']]
bound = None
if self.data_list[idx]['bound']:
bound = (
self.data_list[idx]['data']['start'],
self.data_list[idx]['data']['end'],
)
video_path = os.path.join(self.data_list[idx]['prefix'], self.data_list[idx]['data']['video'])
# images_group = decord_method(video_path, bound)
try: # might be problem with decord
images_group = decord_method(video_path, bound)
except Exception as e:
print(f'error decoding {video_path}')
task_type = 'error_reading_video'
images_group = None
return {
'video_path': video_path,
'video_pils': images_group, # some might use the original pils and do their own transforms
'question': question,
'answer': answer,
'task_type': task_type,
}
def qa_template(self, data):
question = f"Question: {data['question']}\n"
question += "Options:\n"
answer = data['answer']
answer_idx = -1
for idx, c in enumerate(data['candidates']):
question += f"({chr(ord('A') + idx)}) {c}\n"
if c == answer:
answer_idx = idx
question = question.rstrip()
answer = f"({chr(ord('A') + answer_idx)}) {answer}"
return question, answer
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