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()