File size: 9,531 Bytes
85e9f46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 |
'''
* 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) |