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:
