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# ------------------------------------------------------------------------------
# Generate random samples of the generator and save the images to a hdf5 file.
# ------------------------------------------------------------------------------
import h5py
import numpy as np
import os
import params
import sys
import tensorflow as tf
import tensorflow_hub as hub
import time
import visualize as vs
# --------------------------
# Hyper-parameters.
# --------------------------
# Expected parameters:
# generator_path: path to generator module.
# generator_fixed_inputs: dictionary of fixed generator's input parameters.
# dataset_out: name for the output created dataset (hdf5 file).
# General parameters:
# batch_size: number of images generated at the same time.
# random_label: choose random labels.
# num_imgs: number of instances to generate.
# custom_label: custom label to be fixed.
# Logging:
# sample_size: number of images included in sampled images.
if len(sys.argv) < 2:
sys.exit('Must provide a configuration file.')
params = params.Params(sys.argv[1])
# --------------------------
# Hyper-parameters.
# --------------------------
# General parameters.
BATCH_SIZE = params.batch_size
SAMPLE_SIZE = params.sample_size
assert SAMPLE_SIZE <= BATCH_SIZE
NUM_IMGS = params.num_imgs
# --------------------------
# Global directories.
# --------------------------
SAMPLES_DIR = 'random_samples'
INVERSES_DIR = 'inverses'
if not os.path.exists(SAMPLES_DIR):
os.makedirs(SAMPLES_DIR)
if not os.path.exists(INVERSES_DIR):
os.makedirs(INVERSES_DIR)
# --------------------------
# Util functions.
# --------------------------
def one_hot(values):
return np.eye(N_CLASS)[values]
def label_sampler(size=1):
return np.random.random_integers(low=0, high=N_CLASS-1, size=size)
# --------------------------
# Load Graph.
# --------------------------
generator = hub.Module(str(params.generator_path))
gen_signature = 'generator'
if 'generator' not in generator.get_signature_names():
gen_signature = 'default'
input_info = generator.get_input_info_dict(gen_signature)
COND_GAN = 'y' in input_info
if COND_GAN:
Z_DIM = input_info['z'].get_shape().as_list()[1]
latent = tf.get_variable(name='latent', dtype=tf.float32,
shape=[BATCH_SIZE, Z_DIM])
N_CLASS = input_info['y'].get_shape().as_list()[1]
label = tf.get_variable(name='label', dtype=tf.float32,
shape=[BATCH_SIZE, N_CLASS])
gen_in = dict(params.generator_fixed_inputs)
gen_in['z'] = latent
gen_in['y'] = label
gen_img = generator(gen_in, signature=gen_signature)
else:
Z_DIM = input_info['default'].get_shape().as_list()[1]
latent = tf.get_variable(name='latent', dtype=tf.float32,
shape=[BATCH_SIZE, Z_DIM])
if (params.generator_fixed_inputs):
gen_in = dict(params.generator_fixed_inputs)
gen_in['z'] = latent
gen_img = generator(gen_in, signature=gen_signature)
else:
gen_img = generator(latent, signature=gen_signature)
# Convert generated image to channels_first.
gen_img = tf.transpose(gen_img, [0, 3, 1, 2])
# Define image shape.
IMG_SHAPE = gen_img.get_shape().as_list()[1:]
# --------------------------
# Noise source.
# --------------------------
def noise_sampler():
return np.random.normal(size=[BATCH_SIZE, Z_DIM])
# --------------------------
# Generation.
# --------------------------
# Start session.
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
# Output file.
out_file = h5py.File(os.path.join(INVERSES_DIR, params.dataset_out), 'w')
out_images = out_file.create_dataset('xtrain', [NUM_IMGS,] + IMG_SHAPE,
dtype='uint8')
if COND_GAN:
out_labels = out_file.create_dataset('ytrain', (NUM_IMGS,), dtype='uint32')
for i in range(0, NUM_IMGS, BATCH_SIZE):
n_encs = min(BATCH_SIZE, NUM_IMGS - i)
if COND_GAN:
if params.random_label:
label_batch = label_sampler(BATCH_SIZE)
else:
label_batch = [params.custom_label]*BATCH_SIZE
sess.run(label.assign(one_hot(label_batch)))
sess.run(latent.assign(noise_sampler()))
gen_images = sess.run(gen_img)
gen_images = vs.data2img(gen_images)
out_images[i:i+n_encs] = gen_images[:n_encs]
if COND_GAN:
out_labels[i:i+n_encs] = label_batch[:n_encs]
out_batch = vs.grid_transform(gen_images[:SAMPLE_SIZE])
vs.save_image('{}/generated_{}.png'.format(SAMPLES_DIR, i), out_batch)
print('Saved samples for imgs: {}-{}.'.format(i,i+n_encs))
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