def main(argv=None):
dcgan = DCGAN(
batch_size=96, f_size=6, z_dim=40,
gdepth1=512, gdepth2=256, gdepth3=128, gdepth4=64,
ddepth1=54, ddepth2=90, ddepth3=150, ddepth4=250)
dcgan.d(dcgan.g(dcgan.z))
g_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='g'))
d_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='d'))
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
g_checkpoint_path = os.path.join(os.path.dirname(__file__), '..', FLAGS.train_dir, 'g.ckpt')
d_checkpoint_path = os.path.join(os.path.dirname(__file__), '..', FLAGS.train_dir, 'd.ckpt')
if os.path.exists(g_checkpoint_path):
g_saver.restore(sess, g_checkpoint_path)
if os.path.exists(d_checkpoint_path):
d_saver.restore(sess, d_checkpoint_path)
ops = []
targets = [
{'name': 'g/conv1/Relu:0', 'row': 8, 'col': 32},
{'name': 'g/conv2/Relu:0', 'row': 8, 'col': 16},
{'name': 'g/conv3/Relu:0', 'row': 8, 'col': 8 },
{'name': 'Tanh:0', 'row': 1, 'col': 3 },
{'name': 'd/conv0/Maximum:0', 'row': 6, 'col': 9 },
{'name': 'd/conv1/Maximum:0', 'row': 6, 'col': 15},
{'name': 'd/conv2/Maximum:0', 'row': 6, 'col': 25},
]
for target in targets:
t = sess.graph.get_tensor_by_name(target['name'])
batch_outputs = tf.split(0, dcgan.batch_size, t)
for i in range(3):
maps = tf.split(3, t.get_shape()[3], batch_outputs[i])
rows = []
cols = target['col']
for row in range(target['row']):
rows.append(tf.concat(2, maps[cols * row: cols * row + cols]))
montaged = tf.concat(1, rows)
out = tf.image.convert_image_dtype(tf.squeeze(montaged, [0]), tf.uint8, saturate=True)
ops.append(tf.image.encode_png(out, name=t.op.name.replace('/', '-') + '-%02d' % i))
results = sess.run(ops)
for i in range(len(ops)):
filename = ops[i].op.name + '.png'
print('write %s' % filename)
with open(os.path.join(os.path.dirname(__file__), '..', FLAGS.images_dir, filename), 'wb') as f:
f.write(results[i])
def main(argv=None):
dcgan = DCGAN(
batch_size=96, f_size=6, z_dim=40,
gdepth1=512, gdepth2=256, gdepth3=128, gdepth4=64,
ddepth1=54, ddepth2=90, ddepth3=150, ddepth4=250)
dcgan.d(dcgan.g(dcgan.z))
g_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='g'))
d_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='d'))
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
g_checkpoint_path = os.path.join(os.path.dirname(__file__), '..', FLAGS.train_dir, 'g.ckpt')
d_checkpoint_path = os.path.join(os.path.dirname(__file__), '..', FLAGS.train_dir, 'd.ckpt')
if os.path.exists(g_checkpoint_path):
g_saver.restore(sess, g_checkpoint_path)
if os.path.exists(d_checkpoint_path):
d_saver.restore(sess, d_checkpoint_path)
ops = []
targets = [
{'name': 'g/conv1/Relu:0', 'row': 8, 'col': 32},
{'name': 'g/conv2/Relu:0', 'row': 8, 'col': 16},
{'name': 'g/conv3/Relu:0', 'row': 8, 'col': 8 },
{'name': 'Tanh:0', 'row': 1, 'col': 3 },
{'name': 'd/conv0/Maximum:0', 'row': 6, 'col': 9 },
{'name': 'd/conv1/Maximum:0', 'row': 6, 'col': 15},
{'name': 'd/conv2/Maximum:0', 'row': 6, 'col': 25},
]
for target in targets:
t = sess.graph.get_tensor_by_name(target['name'])
batch_outputs = tf.split(0, dcgan.batch_size, t)
for i in range(3):
maps = tf.split(3, t.get_shape()[3], batch_outputs[i])
rows = []
cols = target['col']
for row in range(target['row']):
rows.append(tf.concat(2, maps[cols * row: cols * row + cols]))
montaged = tf.concat(1, rows)
out = tf.image.convert_image_dtype(tf.squeeze(montaged, [0]), tf.uint8, saturate=True)
ops.append(tf.image.encode_png(out, name=t.op.name.replace('/', '-') + '-%02d' % i))
results = sess.run(ops)
for i in range(len(ops)):
filename = ops[i].op.name + '.png'
print('write %s' % filename)
with open(os.path.join(os.path.dirname(__file__), '..', FLAGS.images_dir, filename), 'wb') as f:
f.write(results[i])
inputs.append(np.expand_dims(np.random.uniform(low, high, 128) + offset, 0))
inputs = np.concatenate(inputs).T
# generate images
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import tensorflow as tf
from dcgan import DCGAN
dcgan = DCGAN(
batch_size=128, f_size=6, z_dim=16,
gdepth1=216, gdepth2=144, gdepth3=96, gdepth4=64,
ddepth1=0, ddepth2=0, ddepth3=0, ddepth4=0)
placeholder = tf.placeholder(tf.float32, shape=(128, 16))
generate = dcgan.g(placeholder)[-1]
g_saver = tf.train.Saver(dcgan.g.variables)
g_checkpoint_path = os.path.join(os.path.dirname(__file__), '..', 'train', 'g.ckpt')
with tf.Graph().as_default() as g:
with tf.Session() as sess:
tmp = DCGAN(
batch_size=128, f_size=6, z_dim=16,
gdepth1=216, gdepth2=144, gdepth3=96, gdepth4=64,
ddepth1=0, ddepth2=0, ddepth3=0, ddepth4=0)
tmp.g(tmp.z)
saver = tf.train.Saver(tmp.g.variables)
saver.restore(sess, g_checkpoint_path)
# get each means and variances
outputs = []
for op in g.get_operations():
