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import click
from app.utils import click_utils
from app.settings import app_cfg
from os.path import join
import time
import numpy as np
import random
from scipy.stats import truncnorm
from PIL import Image
z_dim = {
128: 120,
256: 140,
512: 128,
}
def image_to_uint8(x):
"""Converts [-1, 1] float array to [0, 255] uint8."""
x = np.asarray(x)
x = (256. / 2.) * (x + 1.)
x = np.clip(x, 0, 255)
x = x.astype(np.uint8)
return x
def truncated_z_sample(batch_size, z_dim, truncation):
values = truncnorm.rvs(-2, 2, size=(batch_size, z_dim))
return truncation * values
def create_labels(batch_size, vocab_size, num_classes):
label = np.zeros((batch_size, vocab_size))
for i in range(batch_size):
for _ in range(random.randint(1, num_classes)):
j = random.randint(0, vocab_size-1)
label[i, j] = random.random()
label[i] /= label[i].sum()
return label
@click.command('')
@click.option('-s', '--dims', 'opt_dims', default=256, type=int,
help='Dimensions of BigGAN network (128, 256, 512)')
# @click.option('-i', '--input', 'opt_dir_in', required=True,
# help='Path to input image glob directory')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx, opt_dims):
"""
Generate a random BigGAN image
"""
import tensorflow as tf
import tensorflow_hub as hub
print("Loading module...")
module = hub.Module('https://tfhub.dev/deepmind/biggan-' + str(opt_dims) + '/2')
# module = hub.Module('https://tfhub.dev/deepmind/biggan-256/2')
# module = hub.Module('https://tfhub.dev/deepmind/biggan-512/2')
inputs = {k: tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k)
for k, v in module.get_input_info_dict().items()}
input_z = inputs['z']
input_y = inputs['y']
input_trunc = inputs['truncation']
output = module(inputs)
z_dim = input_z.shape.as_list()[1]
vocab_size = input_y.shape.as_list()[1]
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
sess.run(tf.compat.v1.tables_initializer())
# scalar truncation value in [0.02, 1.0]
batch_size = 8
truncation = 0.5
#z = truncation * tf.random.truncated_normal([batch_size, z_dim]) # noise sample
z = truncated_z_sample(batch_size, z_dim, truncation)
for num_classes in [1, 2, 3, 5, 10, 20, 100]:
print(num_classes)
#y = tf.random.gamma([batch_size, 1000], gamma[0], gamma[1])
#y = np.random.gamma(gamma[0], gamma[1], (batch_size, 1000,))
y = create_labels(batch_size, vocab_size, num_classes)
results = sess.run(output, feed_dict={input_z: z, input_y: y, input_trunc: truncation})
for sample in results:
sample = image_to_uint8(sample)
img = Image.fromarray(sample, "RGB")
fp_img_out = "{}.png".format(int(time.time() * 1000))
img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))
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