os.environ["CUDA_VISIBLE_DEVICES"] = gpus else: gpus = "" for i in range(len(args.gpus)): gpus = gpus + args.gpus[i] + "," os.environ["CUDA_VISIBLE_DEVICES"] = gpus[:-1] torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance test_folder = args.dataset_path + args.dataset_type + "/testing/frames" # Loading dataset test_dataset = DataLoader(test_folder, transforms.Compose([ transforms.ToTensor(), ]), resize_height=args.h, resize_width=args.w, time_step=args.t_length - 1) test_size = len(test_dataset) test_batch = data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False, num_workers=args.num_workers_test, drop_last=False) # Loading the trained model model = torch.load(args.model_dir) model.cuda()
# coding:utf-8 import tensorflow as tf from model.mrcnn import MRCNN from model.utils import DataLoader from model.config import Config config = Config() with tf.Graph().as_default(): session_conf = tf.ConfigProto( allow_soft_placement=config.allow_soft_placement, log_device_placement=config.log_device_placement) # prepare data set dataloader = DataLoader() num_classes = dataloader.numclass word_embeddings1 = dataloader.word_embeddings1 word_embeddings2 = dataloader.word_embeddings2 # train_data = dataloader.trainset # test_data = dataloader.testset # testset_size = dataloader.testset_size train_data_iter, train_size, train_num_batches = dataloader.batch_iter( is_train=True, batch_size=config.batch_size, num_epochs=config.num_epochs, oversample=False, shuffle=True) test_data_iter, test_size, test_num_batches = dataloader.batch_iter( is_train=False, batch_size=20,