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added testing output path to args
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@ -50,6 +50,6 @@ parser.add_argument('--learning_rate', dest='learning_rate', default=0.001, help
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# parser.add_argument('--graph_path', dest='graph_path', default='./graphs/', help='tensorboard graph')
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# parser.add_argument('--images_path', dest='images_path', default='./images/', help='result images path')
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parser.add_argument('--training_samples_path', dest='training_samples_path', default='./training_samples/', help='samples images generated during training path')
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parser.add_argument('--testing_output_path', dest='testing_output_path', default='./testing_output/', help='output images generated from running test.py path')
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args = parser.parse_args()
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11
test.py
11
test.py
@ -6,7 +6,9 @@ import os
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import matplotlib.pyplot as plt
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import sys
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sys.path.append('..')
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from model import Model
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from config import *
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IMAGE_SIZE = 128
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LOCAL_SIZE = 64
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@ -16,7 +18,7 @@ BATCH_SIZE = 16
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test_npy = './lfw.npy'
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def test():
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def test(args):
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x = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3])
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mask = tf.placeholder(tf.float32, [BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 1])
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local_x = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
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@ -50,7 +52,7 @@ def test():
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masked = raw * (1 - mask_batch[i]) + np.ones_like(raw) * mask_batch[i] * 255
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img = completion[i]
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img = np.array((img + 1) * 127.5, dtype=np.uint8)
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dst = './testing_output_images/{}.jpg'.format("{0:06d}".format(cnt))
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dst = args.testing_output_path + '{}.jpg'.format("{0:06d}".format(cnt))
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output_image([['Input', masked], ['Output', img], ['Ground Truth', raw]], dst)
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@ -91,5 +93,6 @@ def output_image(images, dst):
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if __name__ == '__main__':
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test()
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if not os.path.exists(args.testing_output_path):
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os.makedirs(args.testing_output_path)
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test(args)
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