'''
* 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)