Step 3: run this script which: 1. resizes the images to 84x84 """ from __future__ import absolute_import, division, print_function import csv import glob import os from PIL import Image from Data_Path import get_data_path input_dir = os.path.join(get_data_path(), 'MiniImageNet') path_to_images = os.path.join(input_dir, 'images') all_images = glob.glob(path_to_images + '/*/*') n_images = len(all_images) # Resize images for i, image_file in enumerate(all_images): try: im = Image.open(image_file) if not (im.height == 84 and im.width == 84): im = im.resize((84, 84), resample=Image.LANCZOS) im.save(image_file) except OSError:
parser.add_argument('--batch-size', type=int, help='input batch size for training', default=128) parser.add_argument('--num-epochs', type=int, help='number of epochs to train', default=200) # 200 parser.add_argument('--lr', type=float, help='initial learning rate', default=1e-3) parser.add_argument('--test-batch-size',type=int, help='input batch size for testing', default=1000) prm = parser.parse_args() prm.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") prm.data_path = get_data_path() set_random_seed(prm.seed) if prm.Experiment_Name == 'Permute_Labels': prm.run_name = 'TwoTaskTransfer_permuted_labels' prm.data_transform = 'Permute_Labels' prm.model_name = 'ConvNet3' freeze_description = 'freeze lower layers' not_freeze_list = ['fc_out'] freeze_list = None elif prm.Experiment_Name == 'Shuffled_Pixels': n_pixels_shuffles = 200 prm.run_name = 'TwoTaskTransfer_shuffled_pixels' + str(n_pixels_shuffles) + '_v2' prm.data_transform = 'Shuffled_Pixels'
Step 3: run this script which: 1. resizes the images to 84x84 """ from __future__ import absolute_import, division, print_function import csv import glob import os from PIL import Image from Data_Path import get_data_path input_dir = os.path.join(get_data_path(), 'SmallImageNet') path_to_images = os.path.join(input_dir, 'images') all_images = glob.glob(path_to_images + '/*/*') n_images = len(all_images) # Resize images for i, image_file in enumerate(all_images): try: im = Image.open(image_file) if not (im.height == 84 and im.width == 84): im = im.resize((84, 84), resample=Image.LANCZOS) im.save(image_file) except OSError: