def test_discriminator_invalid_input(self):
wrong_dim_img = tf.zeros([5, 32, 32])
with self.assertRaises(ValueError):
dcgan.discriminator(wrong_dim_img)
spatially_undefined_shape = tf.placeholder(tf.float32, [5, 32, None, 3])
with self.assertRaises(ValueError):
dcgan.discriminator(spatially_undefined_shape)
not_square = tf.zeros([5, 32, 16, 3])
with self.assertRaisesRegexp(ValueError, 'not have equal width and height'):
dcgan.discriminator(not_square)
not_power_2 = tf.zeros([5, 30, 30, 3])
with self.assertRaisesRegexp(ValueError, 'not a power of 2'):
dcgan.discriminator(not_power_2)
def test_discriminator_invalid_input(self):
wrong_dim_img = tf.zeros([5, 32, 32])
with self.assertRaises(ValueError):
dcgan.discriminator(wrong_dim_img)
spatially_undefined_shape = tf.placeholder(tf.float32, [5, 32, None, 3])
with self.assertRaises(ValueError):
dcgan.discriminator(spatially_undefined_shape)
not_square = tf.zeros([5, 32, 16, 3])
with self.assertRaisesRegexp(ValueError, 'not have equal width and height'):
dcgan.discriminator(not_square)
not_power_2 = tf.zeros([5, 30, 30, 3])
with self.assertRaisesRegexp(ValueError, 'not a power of 2'):
dcgan.discriminator(not_power_2)
def test_discriminator_graph(self):
# Check graph construction for a number of image size/depths and batch
# sizes.
for i, batch_size in zip(xrange(1, 6), xrange(3, 8)):
tf.reset_default_graph()
img_w = 2 ** i
image = tf.random_uniform([batch_size, img_w, img_w, 3], -1, 1)
output, end_points = dcgan.discriminator(
image,
depth=32)
self.assertAllEqual([batch_size, 1], output.get_shape().as_list())
expected_names = ['conv%i' % j for j in xrange(1, i+1)] + ['logits']
self.assertSetEqual(set(expected_names), set(end_points.keys()))
# Check layer depths.
for j in range(1, i+1):
layer = end_points['conv%i' % j]
self.assertEqual(32 * 2**(j-1), layer.get_shape().as_list()[-1])
def test_discriminator_graph(self):
# Check graph construction for a number of image size/depths and batch
# sizes.
for i, batch_size in zip(xrange(1, 6), xrange(3, 8)):
tf.reset_default_graph()
img_w = 2 ** i
image = tf.random_uniform([batch_size, img_w, img_w, 3], -1, 1)
output, end_points = dcgan.discriminator(
image,
depth=32)
self.assertAllEqual([batch_size, 1], output.get_shape().as_list())
expected_names = ['conv%i' % j for j in xrange(1, i+1)] + ['logits']
self.assertSetEqual(set(expected_names), set(end_points.keys()))
# Check layer depths.
for j in range(1, i+1):
layer = end_points['conv%i' % j]
self.assertEqual(32 * 2**(j-1), layer.get_shape().as_list()[-1])
def test_discriminator_run(self):
image = tf.random_uniform([5, 32, 32, 3], -1, 1)
output, _ = dcgan.discriminator(image)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output.eval()
D_model.save_weights('logs/D_Epoch%d-GLoss%.4f-DLoss%.4f.h5' %
((epoch + 1), G_total_loss /
(epoch_size + 1), D_total_loss /
(epoch_size + 1)))
print('Saving state, iter:', str(epoch + 1))
if __name__ == "__main__":
image_shape = [64, 64, 3]
# 数据集存放路径
annotation_path = "train_lines.txt"
# 生成网络和评价网络
G_model = generator(64, image_shape)
D_model = discriminator(64, image_shape)
# G_model_path = "model_data/Generator_Flower.h5"
# D_model_path = "model_data/Discriminator_Flower.h5"
# G_model.load_weights(G_model_path, by_name=True, skip_mismatch=True)
# D_model.load_weights(D_model_path, by_name=True, skip_mismatch=True)
with open(annotation_path) as f:
lines = f.readlines()
num_train = len(lines)
#------------------------------------------------------#
# Init_Epoch为起始世代
# Epoch总训练世代
#------------------------------------------------------#
if True:
batch_size = 64
lr = 0.002
#------------------------------#
# 每隔50个step保存一次图片
#------------------------------#
save_interval = 50
#------------------------------------------#
# 获得图片路径
#------------------------------------------#
annotation_path = "train_lines.txt"
#----------------------------#
# 生成网络和评价网络
#----------------------------#
G_model = generator(channel, input_shape)
D_model = discriminator(channel, input_shape)
#------------------------------------------#
# 将训练好的模型重新载入
#------------------------------------------#
if G_model_path != '':
G_model.load_weights(G_model_path, by_name=True, skip_mismatch=True)
if D_model_path != '':
D_model.load_weights(D_model_path, by_name=True, skip_mismatch=True)
with open(annotation_path) as f:
lines = f.readlines()
num_train = len(lines)
#------------------------------------------------------#
# Init_Epoch为起始世代
def test_discriminator_run(self):
image = tf.random_uniform([5, 32, 32, 3], -1, 1)
output, _ = dcgan.discriminator(image)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output.eval()
