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eval.py
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import os
import time
import math
import json
import random
import argparse
import datetime
import numpy as np
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader, DistributedSampler
import utils.misc as utils
from models import build_model
from datasets import build_dataset
from engine import evaluate
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '7' # batch_size max=768，0、1在用
def get_args_parser():
parser = argparse.ArgumentParser('CLIP-VG Args', add_help=False)
parser.add_argument('--sup_type', default='un', type=str)
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--lr_bert', default=1e-5, type=float)
parser.add_argument('--lr_visu_cnn', default=1e-5, type=float)
parser.add_argument('--lr_visu_tra', default=1e-5, type=float)
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--weight_decay', default=1e-4, type=float)
parser.add_argument('--epochs', default=90, type=int)
parser.add_argument('--lr_power', default=0.9, type=float, help='lr poly power')
parser.add_argument('--lr_exponential', default=0.9, type=float, help='lr exponential')
parser.add_argument('--clip_max_norm', default=0., type=float, help='gradient clipping max norm')
parser.add_argument('--eval', dest='eval', default=False, action='store_true', help='if evaluation only')
parser.add_argument('--optimizer', default='adamw', type=str)
parser.add_argument('--lr_scheduler', default='step', type=str)
parser.add_argument('--lr_drop', default=60, type=int)
# Augmentation options
parser.add_argument('--aug_blur', action='store_true', help="If true, use gaussian blur augmentation")
parser.add_argument('--aug_crop', action='store_true', help="If true, use random crop augmentation")
parser.add_argument('--aug_scale', action='store_true', help="If true, use multi-scale augmentation")
parser.add_argument('--aug_translate', action='store_true', help="If true, use random translate augmentation")
# only support ViT-B/16 and ViT-L/14
parser.add_argument('--model', type=str, default='ViT-B/16', help="Name of model to be exploited.")
parser.add_argument('--dim_feedforward', default=2048, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--dropout', default=0.1, type=float, help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--num_queries', default=100, type=int, help="Number of query slots")
parser.add_argument('--pre_norm', action='store_true')
parser.add_argument('--imsize', default=224, type=int, help='image size')
""" embedding size"""
parser.add_argument('--emb_size', default=512, type=int, help='fusion module embedding dimensions')
# Vision-Language Transformer
parser.add_argument('--vl_dropout', default=0.1, type=float,
help="Dropout applied in the vision-language transformer")
parser.add_argument('--vl_nheads', default=8, type=int,
help="Number of attention heads inside the vision-language transformer's attentions")
parser.add_argument('--vl_hidden_dim', default=512, type=int,
help='Size of the embeddings (dimension of the vision-language transformer)')
parser.add_argument('--vl_dim_feedforward', default=2048, type=int,
help="Intermediate size of the feedforward layers in the vision-language transformer blocks")
parser.add_argument('--vl_enc_layers', default=6, type=int,
help='Number of encoders in the vision-language transformer')
# Dataset parameters
parser.add_argument('--data_root', type=str, default='./data/image_data/', help='path to ReferIt splits data folder')
parser.add_argument('--split_root', type=str, default='./data/pseudo_samples/', help='location of pre-parsed dataset info')
parser.add_argument('--dataset', default='referit', type=str, help='referit/unc/unc+/gref/gref_umd')
parser.add_argument('--max_query_len', default=77, type=int, help='maximum time steps (lang length) per batch')
# Prompt Engineering: "{pseudo_query}" denote without using prompt
# "{pseudo_query}" or using "find the region that corresponds to the description {pseudo_query}"
parser.add_argument('--prompt', type=str, default='{pseudo_query}', help="Prompt template")
# dataset parameters
parser.add_argument('--output_dir', default='./outputs', help='path where to save, empty for no saving')
parser.add_argument('--device', default='cuda', help='device to use for training / testing')
parser.add_argument('--seed', default=13, type=int)
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--retrain', default='', help='retrain from checkpoint')
parser.add_argument('--light', dest='light', default=False, action='store_true', help='if use smaller model')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch')
parser.add_argument('--num_workers', default=4, type=int)
# distributed training parameters
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')
# evalutaion options
parser.add_argument('--eval_set', default='test', type=str) # 'testA', 'testB', 'val'
parser.add_argument('--eval_model', default='', type=str)
return parser
def main(args):
""" distribution init """
utils.init_distributed_mode(args)
print("git:\n {}\n".format(utils.get_sha()))
if (args.model == "ViT-L/14" or args.model == "ViT-L/14@336px"):
args.vl_hidden_dim = 768
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)
print('### INFO ### torch.backends.cudnn.benchmark = {}'.format(torch.backends.cudnn.benchmark))
# build model
model = build_model(args)
model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
n_parameters_grad = sum(p.numel() for p in model.parameters() if p.requires_grad)
n_parameters = sum(p.numel() for p in model.parameters())
print('number of requires_grad params: ', n_parameters_grad)
print('number of all params: ', n_parameters)
# build dataset
dataset_test = build_dataset(args.eval_set, args)
if args.distributed:
sampler_test = DistributedSampler(dataset_test, shuffle=False)
else:
sampler_test = torch.utils.data.SequentialSampler(dataset_test)
batch_sampler_test = torch.utils.data.BatchSampler(sampler_test, args.batch_size, drop_last=False)
data_loader_test = DataLoader(dataset_test, args.batch_size, sampler=sampler_test,
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)
checkpoint = torch.load(args.eval_model, map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
print("Current model training epoch is: ", checkpoint['epoch'])
# output log
eval_model = args.eval_model
eval_model = eval_model.split('/')[-1].split('.')[0]
output_dir = Path(args.output_dir)
if args.output_dir and utils.is_main_process():
with (output_dir / "eval_{}_{}_{}_log.txt".format(args.dataset, args.eval_set, eval_model)).open("a") as f:
f.write(str(args) + "\n")
f.flush()
start_time = time.time()
# perform evaluation
accuracy = evaluate(args, model, data_loader_test, device)
if utils.is_main_process():
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Testing time {}'.format(total_time_str))
log_stats = {'test_model:': args.eval_model,
'%s_set_accuracy'%args.eval_set: accuracy,
}
print(log_stats)
if args.output_dir and utils.is_main_process():
with (output_dir / "eval_{}_{}_{}_log.txt".format(args.dataset, args.eval_set, eval_model)).open("a") as f:
f.write(json.dumps(log_stats) + "\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser('CLIP-VG evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)