def _visualize_model(self, img_dir):
print("Sampling images from model...")
batch_z = np.random.uniform(-1.0,
1.0,
size=[self.batch_size,
self.z_dim]).astype(np.float32)
correct_tag = np.zeros(
[self.batch_size, self.eyes_dim + self.hair_dim], dtype=np.float32)
correct_tag[:, 1] = 1.
correct_tag[:-1] = 1.
feed_dict = {
self.z_vec: batch_z,
self.eyes_vec: correct_tag[:, :11],
self.hair_vec: correct_tag[:, 11:],
self.train_phase: False
}
images = self.sess.run(self.gen_images, feed_dict=feed_dict)
images = ops.unprocess_image(images, 127.5, 127.5).astype(np.uint8)
shape = [4, self.batch_size // 4]
print(images.shape)
ops.save_imshow_grid(images, img_dir,
"generated_%d.png" % self.global_steps, shape)
def save_test_img(self, feature, index):
batch_z = np.random.uniform(-1.0,
1.0,
size=[self.batch_size,
self.z_dim]).astype(np.float32)
correct_tag = np.tile(feature, (self.batch_size, 1))
feed_dict = {
self.z_vec: batch_z,
self.tag_vec: correct_tag,
self.train_phase: False
}
images = self.sess.run(self.gen_images, feed_dict=feed_dict)
images = ops.unprocess_image(images, 127.5, 127.5).astype(np.uint8)
ops.save_test_image(images, index)
def _visualize_model(self, img_dir):
print("Sampling images from model...")
batch_z = np.random.uniform(-1.0,
1.0,
size=[self.batch_size,
self.z_dim]).astype(np.float32)
correct_tag = np.load('test_embedding.npy')
correct_tag = np.tile(correct_tag, (self.batch_size, 1))
feed_dict = {
self.z_vec: batch_z,
self.tag_vec: correct_tag,
self.train_phase: False
}
images = self.sess.run(self.gen_images, feed_dict=feed_dict)
images = ops.unprocess_image(images, 127.5, 127.5).astype(np.uint8)
shape = [4, self.batch_size // 4]
print(images.shape)
ops.save_imshow_grid(images, img_dir,
"generated_%d.png" % self.global_steps, shape)