batch_y = batch_y.get().transpose()
total_Oaccuracy += sess.run(Oaccuracy,
feed_dict={
x_n: batch_x,
y: batch_y,
keep_prob: 1.,
is_train: False
})
print 'Iteration %i, Accuracy: %.2f' % (i_iter,
total_Oaccuracy / mb_idx)
# Store images
if i_iter % store_img_iter == 0 or i_iter == max_iter - 1:
# Store Generated
genmix_imgs = (np.transpose(gen_img, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, 10)
imsave(os.path.join(gen_dir, '%i.jpg' % i_iter), genmix_imgs)
# Store Generated 96
genmix_imgs = (np.transpose(gen_img128, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, 10)
imsave(os.path.join(gen_dir128, '%i.jpg' % i_iter), genmix_imgs)
# Store Real
real_imgs = (np.transpose(batch_x, [0, 2, 3, 1]) + 1.) * 127.5
real_imgs = np.uint8(real_imgs[:, :, :, ::-1])
real_imgs = drawblock(real_imgs, 10)
imsave(os.path.join(real_dir, '%i.jpg' % i_iter), real_imgs)
# Store model
if i_iter % save_iter == 0 or i_iter == max_iter - 1 or i_iter == max_iter:
save_path = saver.save(sess, dir_name + '/cdgan%i.ckpt' % i_iter)
coord.request_stop()
g6b = conv2d(g6, nout=3, kernel=3, name=gname + 'deconv6b')
g6b = tf.nn.tanh(g6b)
g6b_64 = pool(g6b, fsize=3, strides=2, op='avg')
return g6b_64, g6b
# Call functions
samples, samples128 = generator(z, iny)
# Initialize the variables
init = tf.global_variables_initializer()
# Config for session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Generate
with tf.Session(config=config) as sess:
sess.run(init)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess=sess, save_path='./models/CUB128GANAE/cdgan29999.ckpt')
gen_img, gen_img128 = sess.run([samples, samples128])
gen_img = (np.transpose(gen_img, [0, 2, 3, 1]) + 1.) * 127.5
gen_img = np.uint8(gen_img[:, :, :, ::-1])
gen_img128 = (np.transpose(gen_img128, [0, 2, 3, 1]) + 1.) * 127.5
gen_img128 = np.uint8(gen_img128[:, :, :, ::-1])
gg = drawblock(gen_img, 10)
imsave(os.path.join(gen_dir, 'sample.jpg'), gg)
ggL = drawblock(gen_img128, 10)
imsave(os.path.join(gen_dir128, 'sample.jpg'), ggL)
g6b_64 = pool(g6b, fsize=3, strides=2, op='avg')
return g6b_64, g6b
# Call functions
samples, samples128 = generator(z, iny)
# Initialize the variables
init = tf.global_variables_initializer()
# Config for session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Generate
with tf.Session(config=config) as sess:
sess.run(init)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess=sess, save_path='./models/STL128GANAE/cdgan49999.ckpt')
# generate
gen_img, gen_img128 = sess.run([samples, samples128])
# Store Generated
genmix_imgs = (np.transpose(gen_img, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes)
imsave(os.path.join(gen_dir, 'sample1.jpg'), genmix_imgs)
# Store Generated 128
genmix_imgs = (np.transpose(gen_img128, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes)
imsave(os.path.join(gen_dir128, 'sample1.jpg'), genmix_imgs)
# Call functions
samples, samples128 = generator(z, iny)
# Initialize the variables
init = tf.global_variables_initializer()
# Config for session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# Generate
with tf.Session(config=config) as sess:
sess.run(init)
saver = tf.train.Saver(max_to_keep=None)
saver.restore(sess=sess,
save_path='./models/Artist128GANAE/cdgan49999.ckpt')
# run generator
gen_img, gen_img128 = sess.run([samples, samples128])
# Store Generated
genmix_imgs = (np.transpose(gen_img, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes, fixed=4, flip=False)
imsave(os.path.join(gen_dir, 'sample1.jpg'), genmix_imgs)
# Store Generated 128
genmix_imgs = (np.transpose(gen_img128, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes, fixed=4, flip=False)
imsave(os.path.join(gen_dir128, 'sample1.jpg'), genmix_imgs)
batch_y = batch_y.get().transpose()
total_Oaccuracy += sess.run(Oaccuracy,
feed_dict={
x_n: batch_x,
y: batch_y,
keep_prob: 1.,
is_train: False
})
print 'Iteration %i, Accuracy: %.2f' % (i_iter,
total_Oaccuracy / mb_idx)
# Store images
if i_iter % store_img_iter == 0 or i_iter == max_iter - 1:
# Store Generated
genmix_imgs = (np.transpose(gen_img, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes, flip=True)
imsave(os.path.join(gen_dir, '%i.jpg' % i_iter), genmix_imgs)
# Store Generated 96
genmix_imgs = (np.transpose(gen_img128, [0, 2, 3, 1]) + 1.) * 127.5
genmix_imgs = np.uint8(genmix_imgs[:, :, :, ::-1])
genmix_imgs = drawblock(genmix_imgs, n_classes, flip=True)
imsave(os.path.join(gen_dir128, '%i.jpg' % i_iter), genmix_imgs)
# Store Real
real_imgs = (np.transpose(batch_x, [0, 2, 3, 1]) + 1.) * 127.5
real_imgs = np.uint8(real_imgs[:, :, :, ::-1])
real_imgs = drawblock(real_imgs, 10)
imsave(os.path.join(real_dir, '%i.jpg' % i_iter), real_imgs)
# Store model
if i_iter % save_iter == 0 or i_iter == max_iter - 1 or i_iter == max_iter:
save_path = saver.save(sess, dir_name + '/cdgan%i.ckpt' % i_iter)
coord.request_stop()
