def test(val_loader, num_query, query_name, gallery_name, num_classes):
parser = argparse.ArgumentParser(description="ReID Baseline Training")
parser.add_argument("--config_file",
default="",
help="path to config file",
type=str)
parser.add_argument("opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=False)
logger.info(args)
if args.config_file != "":
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
train_model_path = os.path.join(
cfg.OUTPUT_DIR, cfg.MODEL.NAME + '_{}.pth'.format(cfg.TEST.WEIGHT))
model = make_model(cfg, num_class=num_classes)
if torch.cuda.device_count() > 1:
model.load_param(train_model_path)
else:
model.load_param(
os.path.join(cfg.OUTPUT_DIR,
cfg.MODEL.NAME + '_{}.pth'.format(cfg.TEST.WEIGHT)))
do_inference(cfg, model, val_loader, num_query, query_name, gallery_name)
if args.config_file != "":
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
if cfg.TEST.FLIP_FEATS != 'on':
train_loader, val_loader, num_query_normal, num_classes,val_loader_center,val_loader_lb,val_loader_rb,val_loader_rt,val_loader_lt = make_dataloader(cfg)
val_loader_normal = [val_loader,val_loader_center,val_loader_lt,val_loader_rt,val_loader_lb,val_loader_rb]
model = make_model(cfg, num_class=num_classes)
model.load_param(cfg.TEST.WEIGHT)
do_inference_multi(cfg,
model,
val_loader_normal,
num_query_normal)
else:
train_loader, val_loader_normal, num_query_normal, num_classes = make_dataloader(cfg)
model = make_model(cfg, num_class=num_classes)
model.load_param(cfg.TEST.WEIGHT)
do_inference(cfg,
model,
val_loader_normal,
num_query_normal)
import os
import sys
from config.cfg import Cfg
import torch
from torch.backends import cudnn
sys.path.append('.')
from datasets import make_dataloader
from processor import do_inference
from model import make_model
from utils.logger import setup_logger
if __name__ == "__main__":
Cfg.freeze()
log_dir = Cfg.DATALOADER.LOG_DIR
logger = setup_logger('pose-transfer-avs.test', log_dir)
logger.info("Running with config:\n{}".format(Cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = Cfg.MODEL.DEVICE_ID
cudnn.benchmark = True
train_loader, val_loader = make_dataloader(Cfg)
model_G, _, _ = make_model(Cfg)
model_G.load_state_dict(torch.load(Cfg.TEST.WEIGHT))
do_inference(Cfg, model_G, val_loader)
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
#train_loader, val_loader_green, val_loader_normal, num_query_green,num_query_normal, num_classes = make_dataloader(cfg)
model = make_model(cfg, num_class=3094)
model.load_param(cfg.TEST.WEIGHT)
transform_test = transforms.Compose([
transforms.Resize(cfg.INPUT.SIZE_TEST),
# 将图像中央的高和宽均为224的正方形区域裁剪出来
# transforms.CenterCrop(224),
transforms.ToTensor(),
# transforms.Normalize((0.524, 0.4943, 0.473), (0.03477, 0.03015, 0.02478))
transforms.Normalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD)
])
batch_size = 128
testset = torchvision.datasets.ImageFolder(
root='../test_data_final/test_data_A', transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset,
batch_size=batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
num_query = len(os.listdir("../test_data_final/test_data_A\\query"))
query_name = testset.samples[-num_query:]
#print(query_name[0])
gallery_name = testset.samples[:-num_query]
do_inference(cfg, model, test_loader, num_query, query_name, gallery_name)
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=False)
logger.info(args)
if args.config_file != "":
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
train_loader, val_loader, num_query, num_classes = make_dataloader(cfg)
model = make_model(cfg, num_class=num_classes)
model.load_param(cfg.TEST.WEIGHT)
do_inference(cfg, model, val_loader, num_query)
import os
import sys
from config.config import Configuration
import torch
from torch.backends import cudnn
sys.path.append('.')
from datasets import make_dataloader
from processor import do_inference
from model import make_model
from utils.logger import setup_logger
if __name__ == "__main__":
Cfg = Configuration()
log_dir = Cfg.LOG_DIR
logger = setup_logger('{}.test'.format(Cfg.PROJECT_NAME), log_dir)
os.environ['CUDA_VISIBLE_DEVICES'] = Cfg.DEVICE_ID
cudnn.benchmark = True
# This flag allows you to enable the inbuilt cudnn auto-tuner to find the best algorithm to use for your hardware.
train_loader, test_loader = make_dataloader(Cfg)
model = make_model(Cfg)
model.load_state_dict(torch.load(Cfg.TEST_WEIGHT))
do_inference(Cfg, model, test_loader)