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| ''' | |
| * Copyright (c) 2022, salesforce.com, inc. | |
| * All rights reserved. | |
| * SPDX-License-Identifier: BSD-3-Clause | |
| * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause | |
| * By Junnan Li | |
| ''' | |
| import argparse | |
| import os | |
| import ruamel_yaml as yaml | |
| import numpy as np | |
| import random | |
| import time | |
| import datetime | |
| import json | |
| from pathlib import Path | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.backends.cudnn as cudnn | |
| import torch.distributed as dist | |
| from torch.utils.data import DataLoader | |
| from models.blip_retrieval import blip_retrieval | |
| import utils | |
| from data.video_dataset import VideoDataset | |
| def evaluation(model, data_loader, tokenizer, device, config): | |
| # test | |
| model.eval() | |
| metric_logger = utils.MetricLogger(delimiter=" ") | |
| header = 'Evaluation:' | |
| print('Computing features for evaluation...') | |
| start_time = time.time() | |
| texts = data_loader.dataset.text | |
| num_text = len(texts) | |
| text_bs = 256 | |
| text_ids = [] | |
| text_embeds = [] | |
| text_atts = [] | |
| for i in range(0, num_text, text_bs): | |
| text = texts[i: min(num_text, i+text_bs)] | |
| text_input = tokenizer(text, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(device) | |
| text_output = model.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text') | |
| text_embed = F.normalize(model.text_proj(text_output.last_hidden_state[:,0,:])) | |
| text_embeds.append(text_embed) | |
| text_ids.append(text_input.input_ids) | |
| text_atts.append(text_input.attention_mask) | |
| text_embeds = torch.cat(text_embeds,dim=0) | |
| text_ids = torch.cat(text_ids,dim=0) | |
| text_atts = torch.cat(text_atts,dim=0) | |
| text_ids[:,0] = tokenizer.additional_special_tokens_ids[0] | |
| video_feats = [] | |
| video_embeds = [] | |
| for video, video_id in data_loader: | |
| B,N,C,W,H = video.size() | |
| video = video.view(-1,C,W,H) | |
| video = video.to(device,non_blocking=True) | |
| video_feat = model.visual_encoder(video) | |
| video_embed = model.vision_proj(video_feat[:,0,:]) | |
| video_embed = video_embed.view(B,N,-1).mean(dim=1) | |
| video_embed = F.normalize(video_embed,dim=-1) | |
| video_feat = video_feat.view(B,-1,video_feat.shape[-1]) | |
| video_feats.append(video_feat.cpu()) | |
| video_embeds.append(video_embed) | |
| video_feats = torch.cat(video_feats,dim=0) | |
| video_embeds = torch.cat(video_embeds,dim=0) | |
| sims_matrix = video_embeds @ text_embeds.t() | |
| score_matrix_v2t = torch.full((len(texts),len(texts)),-100.0).to(device) | |
| num_tasks = utils.get_world_size() | |
| rank = utils.get_rank() | |
| step = sims_matrix.size(0)//num_tasks + 1 | |
| start = rank*step | |
| end = min(sims_matrix.size(0),start+step) | |
| for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): | |
| topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) | |
| encoder_output = video_feats[start+i].repeat(config['k_test'],1,1).to(device,non_blocking=True) | |
| encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) | |
| output = model.text_encoder(text_ids[topk_idx], | |
| attention_mask = text_atts[topk_idx], | |
| encoder_hidden_states = encoder_output, | |
| encoder_attention_mask = encoder_att, | |
| return_dict = True, | |
| ) | |
| score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] | |
| score_matrix_v2t[start+i,topk_idx] = score + topk_sim | |
| sims_matrix = sims_matrix.t() | |
| score_matrix_t2v = torch.full((len(texts),len(texts)),-100.0).to(device) | |
| step = sims_matrix.size(0)//num_tasks + 1 | |
| start = rank*step | |
| end = min(sims_matrix.size(0),start+step) | |
| for i,sims in enumerate(metric_logger.log_every(sims_matrix[start:end], 50, header)): | |
| topk_sim, topk_idx = sims.topk(k=config['k_test'], dim=0) | |
| encoder_output = video_feats[topk_idx].to(device,non_blocking=True) | |
| encoder_att = torch.ones(encoder_output.size()[:-1],dtype=torch.long).to(device,non_blocking=True) | |
| output = model.text_encoder(text_ids[start+i].repeat(config['k_test'],1), | |
| attention_mask = text_atts[start+i].repeat(config['k_test'],1), | |
| encoder_hidden_states = encoder_output, | |
| encoder_attention_mask = encoder_att, | |
| return_dict = True, | |
| ) | |
| score = model.itm_head(output.last_hidden_state[:,0,:])[:,1] | |
| score_matrix_t2v[start+i,topk_idx] = score + topk_sim | |
| if args.distributed: | |
| dist.barrier() | |
| torch.distributed.all_reduce(score_matrix_v2t, op=torch.distributed.ReduceOp.SUM) | |
| torch.distributed.all_reduce(score_matrix_t2v, op=torch.distributed.ReduceOp.SUM) | |
| total_time = time.time() - start_time | |
| total_time_str = str(datetime.timedelta(seconds=int(total_time))) | |
| print('Evaluation time {}'.format(total_time_str)) | |
| return score_matrix_v2t.cpu().numpy(), score_matrix_t2v.cpu().numpy() | |
| def itm_eval(scores_v2t, scores_t2v, txt2vmg, vid2txt): | |
| #Video->Text | |
| ranks = np.zeros(scores_v2t.shape[0]) | |
| for index,score in enumerate(scores_v2t): | |
| inds = np.argsort(score)[::-1] | |
| ranks[index] = np.where(inds == vid2txt[index])[0][0] | |
| # Compute metrics | |
| tr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) | |
| tr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) | |
| tr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) | |
| #Text->Video | |
| ranks = np.zeros(scores_t2v.shape[0]) | |
| for index,score in enumerate(scores_t2v): | |
| inds = np.argsort(score)[::-1] | |
| ranks[index] = np.where(inds == txt2vmg[index])[0][0] | |
| mdR = np.median(ranks+1) | |
| # Compute metrics | |
| vr1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks) | |
| vr5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks) | |
| vr10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks) | |
| tr_mean = (tr1 + tr5 + tr10) / 3 | |
| vr_mean = (vr1 + vr5 + vr10) / 3 | |
| r_mean = (tr_mean + vr_mean) / 2 | |
| eval_result = {'txt_r1': tr1, | |
| 'txt_r5': tr5, | |
| 'txt_r10': tr10, | |
| 'txt_r_mean': tr_mean, | |
| 'vid_r1': vr1, | |
| 'vid_r5': vr5, | |
| 'vid_r10': vr10, | |
| 'vid_r_mean': vr_mean, | |
| 'vid_mdR': mdR, | |
| 'r_mean': r_mean} | |
| return eval_result | |
| def main(args, config): | |
| utils.init_distributed_mode(args) | |
| device = torch.device(args.device) | |
| # fix the seed for reproducibility | |
| seed = args.seed + utils.get_rank() | |
| torch.manual_seed(seed) | |
| np.random.seed(seed) | |
| random.seed(seed) | |
| cudnn.benchmark = True | |
| #### Dataset #### | |
| print("Creating retrieval dataset") | |
| test_dataset = VideoDataset(config['video_root'],config['ann_root'],num_frm=config['num_frm_test'], | |
| max_img_size=config['image_size'], frm_sampling_strategy='uniform') | |
| test_loader = DataLoader( | |
| test_dataset, | |
| batch_size=config['batch_size'], | |
| num_workers=4, | |
| pin_memory=True, | |
| drop_last=False, | |
| shuffle=False, | |
| ) | |
| #### Model #### | |
| print("Creating model") | |
| model = blip_retrieval(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit']) | |
| model = model.to(device) | |
| model_without_ddp = model | |
| if args.distributed: | |
| model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) | |
| model_without_ddp = model.module | |
| score_v2t, score_t2v, = evaluation(model_without_ddp, test_loader, model_without_ddp.tokenizer, device, config) | |
| if utils.is_main_process(): | |
| test_result = itm_eval(score_v2t, score_t2v, test_loader.dataset.txt2video, test_loader.dataset.video2txt) | |
| print(test_result) | |
| log_stats = {**{f'{k}': v for k, v in test_result.items()},} | |
| with open(os.path.join(args.output_dir, "test_result.txt"),"a") as f: | |
| f.write(json.dumps(log_stats) + "\n") | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--config', default='./configs/retrieval_msrvtt.yaml') | |
| parser.add_argument('--output_dir', default='output/Retrieval_msrvtt') | |
| parser.add_argument('--device', default='cuda') | |
| parser.add_argument('--seed', default=42, type=int) | |
| parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes') | |
| parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') | |
| parser.add_argument('--distributed', default=True, type=bool) | |
| args = parser.parse_args() | |
| config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader) | |
| Path(args.output_dir).mkdir(parents=True, exist_ok=True) | |
| yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w')) | |
| main(args, config) |