anet_clip/backup/eval_utils.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import collections
import torch
import numpy as np
import json
from collections import OrderedDict
from tqdm import tqdm
from os.path import dirname, abspath

pdvc_dir = dirname(abspath(__file__))
sys.path.insert(0, pdvc_dir)
sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3'))
sys.path.insert(0, os.path.join(pdvc_dir, 'densevid_eval3/SODA'))


from densevid_eval3.eval_soda import eval_soda
from densevid_eval3.eval_para import eval_para
from densevid_eval3.eval_dvc import eval_dvc

def calculate_avg_proposal_num(json_path):
    data = json.load(open(json_path))
    return np.array([len(v) for v in data['results'].values()]).mean()

def convert_tapjson_to_dvcjson(tap_json, dvc_json):
    data = json.load(open(tap_json, 'r'))
    data['version'] = "VERSION 1.0"
    data['external_data'] = {'used:': True, 'details': "C3D pretrained on Sports-1M"}

    all_names = list(data['results'].keys())
    for video_name in all_names:
        for p_info in data['results'][video_name]:
            p_info['timestamp'] = p_info.pop('segment')
            p_info['proposal_score'] = p_info.pop('score')
            p_info['sentence_score'] = p_info.pop('sentence_score', 0)
        data['results']["v_" + video_name] = data['results'].pop(video_name)
    json.dump(data, open(dvc_json, 'w'))


def convert_dvcjson_to_tapjson(dvc_json, tap_json):
    data = json.load(open(dvc_json, 'r'))['results']
    out = {}
    out['version'] = "VERSION 1.0"
    out['external_data'] = {'used:': True, 'details': "GT proposals"}
    out['results'] = {}

    all_names = list(data.keys())
    for video_name in all_names:
        video_info = []
        event_num = len(data[video_name])
        timestamps = [data[video_name][i]['timestamp'] for i in range(event_num)]
        sentences = [data[video_name][i]['sentence'] for i in range(event_num)]
        for i, timestamp in enumerate(timestamps):
            score = data[video_name][i].get('proposal_score', 1.0)
            video_info.append({'segment': timestamp, 'score': score, 'sentence': sentences[i], 'sentence_score': data[video_name][i].get('sentence_score', 0)})
        out['results'][video_name[2:]] = video_info
    json.dump(out, open(tap_json, 'w'))


def convert_gtjson_to_tapjson(gt_json, tap_json):
    data = json.load(open(gt_json, 'r'))
    out = {}
    out['version'] = "VERSION 1.0"
    out['external_data'] = {'used:': True, 'details': "GT proposals"}
    out['results'] = {}

    all_names = list(data.keys())
    for video_name in all_names:
        video_info = []
        timestamps = data[video_name]['timestamps']
        sentences = data[video_name]['sentences']
        for i, timestamp in enumerate(timestamps):
            video_info.append({'segment': timestamp, 'score': 1., 'sentence': sentences[i]})
        out['results'][video_name[2:]] = video_info
    with open(tap_json, 'w') as f:
        json.dump(out, f)


def get_topn_from_dvcjson(dvc_json, out_json, top_n=3, ranking_key='proposal_score', score_thres=-1e8):
    data = json.load(open(dvc_json, 'r'))['results']
    out = {}
    out['version'] = "VERSION 1.0"
    out['external_data'] = {'used:': True, 'details': "GT proposals"}
    out['results'] = {}
    all_names = list(data.keys())
    num = 0
    bad_vid = 0
    for video_name in all_names:
        info = data[video_name]
        new_info = sorted(info, key=lambda x: x[ranking_key], reverse=True)
        new_info = [p for p in new_info if p[ranking_key] > score_thres]
        new_info = new_info[:top_n]
        out['results'][video_name] = new_info
        num += len(new_info)
        if len(new_info) == 0:
            bad_vid += 1
            out['results'].pop(video_name)
    print('average proosal number: {}'.format(num / len(all_names)))
    print('bad videos number: {}'.format(bad_vid))
    print('good videos number: {}'.format(len(out['results'])))
    with open(out_json, 'w') as f:
        json.dump(out, f)


def eval_metrics(dvc_filename, gt_filenames, para_gt_filenames, alpha=0.3, ranking_key='proposal_score', rerank=False, dvc_eval_version='2018', transformer_input_type='queries'):
    score = collections.defaultdict(lambda: -1)
    # top_n = 3
    # top_n_filename = dvc_filename + '.top{}.json'.format(top_n)
    # get_topn_from_dvcjson(dvc_filename, top_n_filename, top_n=top_n, ranking_key=ranking_key)
    # dvc_score = eval_dvc(json_path=top_n_filename, reference=gt_filenames)
    # dvc_score = {k: sum(v) / len(v) for k, v in dvc_score.items()}
    # dvc_score.update(eval_soda(top_n_filename, ref_list=gt_filenames))
    # dvc_score.update(eval_para(top_n_filename, referneces=para_gt_filenames))
    # for key in dvc_score.keys():
    #     score[key] = dvc_score[key]
    if transformer_input_type == 'prior_proposals':
        dvc_score = eval_para(dvc_filename, referneces=para_gt_filenames)
        score.update(dvc_score)
        #breakpoint()
        return score
        
    else:
        if rerank:
            dvc_filename = reranking(dvc_filename, alpha=alpha, temperature=2.0)
        dvc_score = eval_dvc(json_path=dvc_filename, reference=gt_filenames, version=dvc_eval_version)
        dvc_score = {k: sum(v) / len(v) for k, v in dvc_score.items()}
        dvc_score.update(eval_soda(dvc_filename, ref_list=gt_filenames))
        dvc_score.update(eval_para(dvc_filename, referneces=para_gt_filenames))
        score.update(dvc_score)
        return score


def save_dvc_json(out_json, path):
    with open(path, 'w') as f:
        out_json['valid_video_num'] = len(out_json['results'])
        out_json['avg_proposal_num'] = np.array([len(v) for v in out_json['results'].values()]).mean().item()
        json.dump(out_json, f)

def reranking(p_src, alpha, temperature):
    print('alpha: {}, temp: {}'.format(alpha, temperature))
    d = json.load(open(p_src))
    d_items = list(d['results'].items())
    for k,v in d_items:
        if True:
            sent_scores = [p['sentence_score'] / (float(len(p['sentence'].split()))**(temperature) + 1e-5) for p in v]
            prop_score = [p['proposal_score'] for p in v]
            joint_score = alpha * (np.array(sent_scores)) + (np.array(prop_score))
        for i,p in enumerate(v):
            p['joint_score'] = joint_score[i]
        v = sorted(v, key=lambda x: x['joint_score'], reverse=True)
        topN = v[0]['pred_event_count']
        v = v[:topN]
        v = sorted(v, key=lambda x: x['timestamp'])
        d['results'][k] = v
    save_path = p_src+'_rerank_alpha{}_temp{}.json'.format(alpha, temperature)
    save_dvc_json(d, save_path)
    return save_path


def evaluate(model, criterion, postprocessors, loader, dvc_json_path, logger=None, args=None, score_threshold=0,
             alpha=0.3, dvc_eval_version='2018', device='cuda', debug=False, skip_lang_eval=False):
    out_json = {'results': {},
                'version': "VERSION 1.0",
                'external_data': {'used:': True, 'details': None}}
    opt = loader.dataset.opt

    loss_sum = OrderedDict()
    with torch.set_grad_enabled(False):
        for dt in tqdm(loader, disable=opt.disable_tqdm):
            # valid_keys = ["video_tensor", "video_length", "video_mask", "video_key"]
            # dt = {key: value for key, value in dt.items() if key in valid_keys}
            dt = {key: _.to(device) if isinstance(_, torch.Tensor) else _ for key, _ in dt.items()}
            dt = collections.defaultdict(lambda: None, dt)

            dt['video_target'] = [
                    {key: _.to(device) if isinstance(_, torch.Tensor) else _ for key, _ in vid_info.items()} for vid_info in
                    dt['video_target']]

            # output, loss = model(dt, criterion, contrastive_criterion=None, eval_mode=True)
            output, _ = model(dt, criterion, contrastive_criterion=None, eval_mode=True)
            orig_target_sizes = dt['video_length'][:, 1]

            weight_dict = criterion.weight_dict
            # Huabin comment this line (anything about 'loss') to avoid reporting losses during evaluation
            # final_loss = sum(loss[k] * weight_dict[k] for k in loss.keys() if k in weight_dict)
            
            # Huabin comment this line to avoid reporting losses during evaluation
            # for loss_k, loss_v in loss.items():
            #     loss_sum[loss_k] = loss_sum.get(loss_k, 0) + loss_v.item()
            # loss_sum['total_loss'] = loss_sum.get('total_loss', 0) + final_loss.item()

            results = postprocessors['bbox'](output, orig_target_sizes, loader)

            batch_json = {}
            for idx, video_name in enumerate(dt['video_key']):
                segment = results[idx]['boxes'].cpu().numpy()
                raw_boxes = results[idx]['raw_boxes'].cpu().numpy()
                # pdb.set_trace()
                #breakpoint()
                batch_json[video_name] = [
                    {
                        "timestamp": segment[pid].tolist(),
                        "raw_box": raw_boxes[pid].tolist(),
                        "proposal_score": results[idx]['scores'][pid].item(),
                        "sentence": results[idx]['captions'][pid],
                        "sentence_score": results[idx]['caption_scores'][pid],
                        'query_id': results[idx]['query_id'][pid].item(),
                        'vid_duration': results[idx]['vid_duration'].item(),
                        'pred_event_count': results[idx]['pred_seq_len'].item(),
                    }
                    for pid in range(len(segment)) if results[idx]['scores'][pid].item() > score_threshold]
            out_json['results'].update(batch_json)
            if debug and len(out_json['results']) > 5:
                break

    save_dvc_json(out_json, dvc_json_path)

    if skip_lang_eval:
        return None, None
    
    # Huabin comment this line to avoid reporting losses during evaluation
    # for k in loss_sum.keys():
    #     loss_sum[k] = np.round(loss_sum[k] / (len(loader) + 1e-5), 3).item()
    # logger.info('loss: {}'.format(loss_sum))
    scores = eval_metrics(dvc_json_path,
                          gt_filenames=opt.gt_file_for_eval,
                          para_gt_filenames=opt.gt_file_for_para_eval,
                          alpha=alpha,
                          rerank=(opt.count_loss_coef > 0),
                          dvc_eval_version=dvc_eval_version,
                          transformer_input_type=opt.transformer_input_type
                          )

    out_json.update(scores)
    save_dvc_json(out_json, dvc_json_path)
    # return scores, loss_sum
    return scores, []