def visualize_samples(images, name="results/test.png", layout=[5,5], vrange=[-1., 1.]):
images = (images - vrange[0]) / (vrange[1]-vrange[0]) * 255.
images = np.rint(images).astype(np.uint8)
view = uf.tile_images(images, size=layout)
if name is None:
return view
view = Image.fromarray(view, 'RGB')
view.save(name)
vl_mgen = um.RandomRectangleMaskGenerator(img_shape[0],
img_shape[1],
max_ratio=.5)
ckpt_file = args.save_dir + '/params_' + args.data_set + '.ckpt'
print('restoring parameters from', ckpt_file)
saver.restore(sess, ckpt_file)
sample_mgen = um.CenterMaskGenerator(obs_shape[0], obs_shape[1], .875)
d = next(test_data)
from PIL import Image
img = Image.fromarray(
uf.tile_images(np.rint(d).astype(np.uint8), size=(5, 5)), 'RGB')
img.save(
os.path.join("plots", '%s_original_%s.png' % (args.data_set, "test")))
feed_dict = vl.make_feed_dict(d, vl_mgen)
ret = sess.run([vl.mxs] + vl.zs + vl.x_hats, feed_dict=feed_dict)
mxs = np.concatenate(ret[0], axis=0)
mxs *= 255.
ret = ret[1:]
zs, x_hats = ret[:len(ret) // 2], ret[len(ret) // 2:]
x_hats = np.concatenate(x_hats, axis=0)
x_hats *= 255.
img = Image.fromarray(
uf.tile_images(np.rint(mxs).astype(np.uint8), size=(5, 5)), 'RGB')
img.save(
ckpt_file = args.save_dir + '/params_' + args.data_set + '.ckpt'
print('restoring parameters from', ckpt_file)
saver.restore(sess, ckpt_file)
# generate samples from the model
sample_x = []
all_data = []
for i in range(args.num_samples):
d = next(test_data)
for k in range(d.shape[0]):
d[k] = d[2].copy()
all_data.append(d)
sample_x.append(sample_from_model(sess, data=all_data[-1])) ##
sample_x = np.concatenate(sample_x, axis=0)
all_data = np.concatenate(all_data, axis=0)
sample_x = np.rint(sample_x * 127.5 + 127.5)
all_data = np.rint(all_data)
for i in range(sample_x.shape[0]):
ms = sample_mgen.gen(1)[0]
contour = 1 - uf.find_contour(ms)[:, :, None]
contour[contour < 1] = 0.8
sample_x[i] *= contour
from PIL import Image
img = Image.fromarray(
uf.tile_images(sample_x.astype(np.uint8), size=(4, 4)), 'RGB')
img.save(
os.path.join("plots",
'%s_complete_%s.png' % (args.data_set, exp_label)))
all_data = []
for i in range(args.num_samples):
all_data.append(next(test_data))
sample_x.append(sample_from_model(sess, data=all_data[-1])) ##
sample_x = np.concatenate(sample_x, axis=0)
all_data = np.concatenate(all_data, axis=0)
sample_x = np.rint(sample_x * 127.5 + 127.5)
all_data = np.rint(all_data)
np.savez(os.path.join("plots",'%s_original_%s.npz' % (args.data_set, exp_label)), all_data)
np.savez(os.path.join("plots",'%s_complete_%s.npz' % (args.data_set, exp_label)), sample_x)
print(uf.evaluate(all_data[-100:].astype(np.float64), sample_x[-100:].astype(np.float64)))
for i in range(sample_x.shape[0]):
ms = test_mgen.gen(1)[0]
contour = 1-uf.find_contour(ms)[:, :, None]
contour[contour<1] = 0.8
sample_x[i] *= contour
from PIL import Image
img = Image.fromarray(uf.tile_images(sample_x.astype(np.uint8), size=(10,10)), 'RGB')
img.save(os.path.join("plots", '%s_complete_%s.png' % (args.data_set, exp_label)))
#
# img_tile = plotting.img_tile(sample_x[:100], aspect_ratio=1.0, border_color=1.0, stretch=True)
# img = plotting.plot_img(img_tile, title=args.data_set + ' samples')
# plotting.plt.savefig(os.path.join("plots",'%s_complete_%s.png' % (args.data_set, exp_label)))
# plotting.plt.close('all')
# np.savez(os.path.join("plots",'%s_complete_%s.npz' % (args.data_set, exp_label)), sample_x)
v.load_vae(sess, v.saver)
test_mgen = m.RandomRectangleMaskGenerator(64,
64,
min_ratio=0.25,
max_ratio=0.5)
data = next(test_data)
data = next(test_data)
# img_tile = plotting.img_tile(data[:25], aspect_ratio=1.0, border_color=1.0, stretch=True)
# img = plotting.plot_img(img_tile, title=v.FLAGS.data_set + ' samples')
# plotting.plt.savefig(os.path.join("plots",'%s_vae_original.png' % (v.FLAGS.data_set)))
feed_dict = v.make_feed_dict(data, test_mgen)
ret = sess.run([v.mxs] + v.x_hats, feed_dict=feed_dict)
mx, x_hat = ret[0], ret[1:]
mx, x_hat = np.concatenate(mx, axis=0), np.concatenate(x_hat, axis=0)
mx = np.rint(mx * 255.)
x_hat = np.rint(x_hat * 255.)
from PIL import Image
img = Image.fromarray(uf.tile_images(mx.astype(np.uint8), size=(4, 4)),
'RGB')
img.save(os.path.join("plots", '%s_vae_ori_%s.png' % ('celeba64', "test")))
img = Image.fromarray(uf.tile_images(x_hat.astype(np.uint8), size=(4, 4)),
'RGB')
img.save(
os.path.join("plots", '%s_vae_recon_%s.png' % ('celeba64', "test")))
