''' * 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 @torch.no_grad() 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() @torch.no_grad() 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)