#resnet50: https://download.pytorch.org/models/resnet50-19c8e357.pth
imageNet_pretrain = 'resnet50-19c8e357.pth'
train_dataset = imgdataset(dataset_dir=src_dir,
txt_path=src_annfile,
transformer='train')
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
train_dataset_t = imgdataset_camtrans(dataset_dir=tar_dir,
txt_path=tar_annfile,
transformer='train',
num_cam=num_cam,
K=K)
train_loader_t = DataLoader(dataset=train_dataset_t,
batch_size=int(batch_size / K),
shuffle=True,
num_workers=4,
drop_last=True)
test_dataset_t = imgdataset(dataset_dir=tar_dir_test,
txt_path=tar_annfile_test,
transformer='test')
test_loader_t = DataLoader(dataset=test_dataset_t,
batch_size=4,
shuffle=False,
num_workers=0)
from utils.evaluate_joint_sim import evaluate_joint
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
dataset_path = 'data/dataset/'
ann_file_train = 'list_market/list_market_train.txt'
ann_file_test = 'list_market/list_market_test.txt'
snapshot = 'snapshot/resnet50_duke2market_epoch00100.pth'
num_cam = 6
########### DATASET ###########
img_dir = dataset_path + 'Market-1501/bounding_box_train_camstyle_merge/'
train_dataset = imgdataset_camtrans(dataset_dir=img_dir,
txt_path=ann_file_train,
transformer='test',
K=num_cam,
num_cam=num_cam)
train_loader = DataLoader(dataset=train_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
img_dir = dataset_path + 'Market-1501/'
test_dataset = imgdataset(dataset_dir=img_dir,
txt_path=ann_file_test,
transformer='test')
test_loader = DataLoader(dataset=test_dataset,
batch_size=1,
shuffle=False,
num_workers=4)