def main(_):
# create out_dir
tf.io.gfile.mkdir(flags.FLAGS.out_dir)
# disable eager execution
tf.compat.v1.reset_default_graph()
tf.compat.v1.disable_eager_execution()
# restore cli parameters
category2indx = {v: k for k, v in indx2category.items()}
name = flags.FLAGS.category
category = category2indx[name]
# create target category
target = tf.fill([1], category)
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
# run, for variables creation
latent = tf.random.truncated_normal(shape=(1, 128))
feed(latent, target, training=True)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(
allow_soft_placement=True)) as sess:
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
# run build
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
# run restore
saver = tf.compat.v1.train.Saver()
saver.restore(sess, flags.FLAGS.pretrained_path)
latent_1 = truncnorm.rvs(-1, 1, size=(1, 128)).astype(np.float32)
latent_2 = truncnorm.rvs(-1, 1, size=(1, 128)).astype(np.float32)
latent_vars = interpolate_points(latent_1, latent_2)
img, _ = feed(latent_1, target)
plot_img = change_range(img[0].eval(), -1, 1, 0,
255).astype(np.uint8)
save_img_cv2(plot_img,
os.path.join(flags.FLAGS.out_dir, 'latent_1.png'))
img, _ = feed(latent_2, target)
plot_img = change_range(img[0].eval(), -1, 1, 0,
255).astype(np.uint8)
save_img_cv2(plot_img,
os.path.join(flags.FLAGS.out_dir, 'latent_2.png'))
for i, latent in enumerate(latent_vars):
img, _ = feed(latent, target)
plot_img = change_range(img[0].eval(), -1, 1, 0,
255).astype(np.uint8)
save_img_cv2(plot_img,
os.path.join(flags.FLAGS.out_dir, f'{i}.png'))
def main(_):
# create out_dir
tf.io.gfile.mkdir(flags.FLAGS.out_dir)
# disable eager execution
tf.compat.v1.reset_default_graph()
tf.compat.v1.disable_eager_execution()
# restore cli parameters
category2indx = {v: k for k, v in indx2category.items()}
bs = flags.FLAGS.bs
num_batches = flags.FLAGS.num_bs
name = flags.FLAGS.category
category = category2indx[name]
# create target category
target = tf.fill([bs], category)
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
# run, for variables creation
latent = tf.random.truncated_normal(shape=(bs, 128))
feed(latent, target, training=True)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(
allow_soft_placement=True)) as sess:
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
# run build
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
# run restore
saver = tf.compat.v1.train.Saver()
saver.restore(sess, flags.FLAGS.pretrained_path)
with tf.device('/GPU:0'):
latent = tf.random.truncated_normal(shape=(bs, 128))
img, _ = feed(latent, target)
indx = 0
for i in range(num_batches):
sess.run(img)
for j in range(bs):
indx += 1
plot_img = change_range(img[j].eval(), -1, 1, 0,
255).astype(np.uint8)
save_img_cv2(
plot_img,
os.path.join(flags.FLAGS.out_dir,
f'{name}_{indx}.png'))
parser.add_argument('--bs', type=int, default=6)
if __name__ == '__main__':
args = parser.parse_args()
# parameterize config
config['experiment_name'] = args.experiment_name
config['ema'] = args.ema
config['weights_root'] = args.weights_root
config['smyrf'] = False
config['return_attn_map'] = True
biggan = Biggan(config)
biggan.load_pretrained()
# Random sampling
category2indx = {val: key for key, val in indx2category.items()}
if args.imagenet_category is not None:
category = category2indx[args.imagenet_category]
else:
category = None
generator_inputs = biggan.get_random_inputs(bs=args.bs,
target=category,
seed=args.seed)
out, attn_map, _ = biggan.sample(generator_inputs, return_attn_map=True)
print('Computing singular values...')
fig = plt.figure()
fig.suptitle(
f'Singular values of BigGAN\'s attention map (after softmax) for {args.bs} randomly generated images.'
def main(_):
# create output dir if it does not exist
tf.io.gfile.mkdir(flags.FLAGS.out_dir)
''' Main function '''
tf.compat.v1.reset_default_graph()
tf.compat.v1.disable_eager_execution()
category2indx = {v: k for k, v in indx2category.items()}
name = flags.FLAGS.category
indx = category2indx[name]
np.random.seed(flags.FLAGS.seed)
tf.compat.v2.random.set_seed(flags.FLAGS.seed)
latent = np.random.normal(size=(flags.FLAGS.bs, 128)).astype(np.float32)
target = tf.fill([flags.FLAGS.bs], indx)
with tf.device('/GPU:0'):
opt = tf.compat.v1.train.AdamOptimizer(flags.FLAGS.lr)
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
img, _, = feed(latent, target, training=True)
with tf.compat.v1.variable_scope('Discriminator',
reuse=tf.compat.v1.AUTO_REUSE):
feed_disc(img, target)
with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(
allow_soft_placement=True)) as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
saver = tf.compat.v1.train.Saver()
saver.restore(sess, flags.FLAGS.pretrained_path)
if not flags.FLAGS.image_path:
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
img, g_attn_map = feed(latent, target)
if flags.FLAGS.inject_noise:
img += tf.random.normal([flags.FLAGS.bs, 128, 128, 3],
stddev=flags.FLAGS.std,
mean=0)
else:
img = change_range(plt.imread(flags.FLAGS.image_path), 0, 255, -1,
1).astype(np.float32)
img = tf.image.resize(img, (128, 128)).eval()
img = np.expand_dims(img, axis=0)
if flags.FLAGS.inject_noise:
img += tf.random.normal([flags.FLAGS.bs, 128, 128, 3],
stddev=flags.FLAGS.std,
mean=0)
img = img.eval()
img = np.repeat(img, flags.FLAGS.bs, axis=0)
with tf.compat.v1.variable_scope('Discriminator',
reuse=tf.compat.v1.AUTO_REUSE):
_, d_attn, d_attn_map = feed_disc(img, target)
step = tf.Variable(0, trainable=False)
latent_ = tf.Variable(tf.random.truncated_normal(shape=(flags.FLAGS.bs,
128)),
trainable=True,
name='latent_')
sess.run(tf.compat.v1.variables_initializer([latent_, step]))
lookahead = BaseLookAhead([latent_], k=5, alpha=0.5)
sess.run(lookahead.get_ops())
loss_vars = [latent_, img, d_attn, d_attn_map, target, step]
loss_value, img_loss = discriminator_loss(loss_vars)
update_op = opt.minimize(loss_value, var_list=[latent_])
sess.run(tf.compat.v1.variables_initializer(opt.variables()))
for i in range(flags.FLAGS.steps):
print('{0}/{1}:\t Loss: {2:.4f}\t Image loss: {3:.4f}'.format(
i, flags.FLAGS.steps, loss_value.eval(), img_loss.eval()))
sess.run(update_op)
with tf.compat.v1.variable_scope('Generator',
reuse=tf.compat.v1.AUTO_REUSE):
inverse, g_attn_map_ = feed(latent_, target)
inverse = inverse.eval()
g_attn_map_ = g_attn_map_.eval()
inverse = change_range(inverse, -1, 1, 0, 255).astype(np.uint8)
if not flags.FLAGS.image_path:
img = img.eval()
# Uncomment to visualize saliencies
# plot_img = change_range(img, -1, 1, 0, 255).astype(np.uint8)[0]
# visualize_attention(plot_img, g_attn_map_[0])
img = change_range(img, -1, 1, 0, 255).astype(np.uint8)
save_img_cv2(
img[0],
os.path.join(flags.FLAGS.out_dir, f'real_{flags.FLAGS.seed}.png'))
for i in range(flags.FLAGS.bs):
save_img_cv2(
inverse[i],
os.path.join(flags.FLAGS.out_dir,
f'reconstructed_{flags.FLAGS.seed + i}.png'))