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Running
on
Zero
Running
on
Zero
| import json | |
| import argparse | |
| from tabulate import tabulate | |
| tasks = { | |
| "Action Sequence": ("action_sequence.json", "star/Charades_v1_480/", "video", True), # has start & end | |
| "Action Prediction": ("action_prediction.json", "star/Charades_v1_480/", "video", True), # has start & end | |
| "Action Antonym": ("action_antonym.json", "ssv2_video/", "video", False), | |
| "Fine-grained Action": ("fine_grained_action.json", "pMoments_in_Time_Raw/videos/", "video", False), | |
| "Unexpected Action": ("unexpected_action.json", "FunQA_test/test/", "video", False), | |
| "Object Existence": ("object_existence.json", "clevrer/video_validation/", "video", False), | |
| "Object Interaction": ("object_interaction.json", "star/Charades_v1_480/", "video", True), # has start & end | |
| "Object Shuffle": ("object_shuffle.json", "perception/videos/", "video", False), | |
| "Moving Direction": ("moving_direction.json", "clevrer/video_validation/", "video", False), | |
| "Action Localization": ("action_localization.json", "sta/sta_video/", "video", True), # has start & end | |
| "Scene Transition": ("scene_transition.json", "scene_qa/video/", "video", False), | |
| "Action Count": ("action_count.json", "perception/videos/", "video", False), | |
| "Moving Count": ("moving_count.json", "clevrer/video_validation/", "video", False), | |
| "Moving Attribute": ("moving_attribute.json", "clevrer/video_validation/", "video", False), | |
| "State Change": ("state_change.json", "perception/videos/", "video", False), | |
| "Fine-grained Pose": ("fine_grained_pose.json", "nturgbd/", "video", False), | |
| "Character Order": ("character_order.json", "perception/videos/", "video", False), | |
| "Egocentric Navigation": ("egocentric_navigation.json", "vlnqa/", "video", False), | |
| "Episodic Reasoning": ("episodic_reasoning.json", "tvqa/frames_fps3_hq/", "frame", True), # has start & end, read frame | |
| "Counterfactual Inference": ("counterfactual_inference.json", "clevrer/video_validation/", "video", False), | |
| } | |
| def main(): | |
| args = parse_args() | |
| res = [eval(x.strip()) for x in open(args.pred_path, 'r').readlines()] | |
| task_types = tasks.keys() | |
| task_acc = {x: [] for x in task_types} | |
| acc = [] | |
| for i, x in enumerate(res): | |
| value = 1 | |
| if x['pred'] != x['gt']: | |
| value = 0 | |
| acc.append(value) | |
| task_acc[x['task_type']].append(value) | |
| acc = sum(acc) * 100 / len(acc) | |
| task_acc = {x: sum(task_acc[x]) * 100 / len(task_acc[x]) for x in task_acc} | |
| print(f"{args.pred_path}:", acc) | |
| task_names = list(tasks.keys()) | |
| table_data = [] | |
| for i in range(len(task_names) // 4): | |
| row_task_names = task_names[i * 4: (i + 1) * 4] | |
| row_task_acc = [task_acc[x] for x in row_task_names] | |
| table_data.append(row_task_names) | |
| table_data.append(row_task_acc) | |
| print(tabulate(table_data, floatfmt=".1f"), '\n') | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Evaluate video captioning.") | |
| parser.add_argument("--pred_path", default=r'', help="The path to file containing prediction.") | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == '__main__': | |
| main() | |