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import os
import sys
import glob
import h5py
import numpy as np
import params
import tensorflow as tf
import tensorflow_probability as tfp
import tensorflow_hub as hub
import time
import random
from scipy.stats import truncnorm
from PIL import Image
import visualize as vs
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../live-cortex/rpc/'))
from rpc import CortexRPC
from params import Params
FPS = 25
params = Params('params_dense-512.json')
# --------------------------
# Make directories.
# --------------------------
tag = "test"
OUTPUT_DIR = os.path.join('output', tag)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
# --------------------------
# Load Graph.
# --------------------------
print("Loading module...")
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)
BATCH_SIZE = 1
Z_DIM = input_info['z'].get_shape().as_list()[1]
N_CLASS = input_info['y'].get_shape().as_list()[1]
# --------------------------
# Initializers
# --------------------------
def label_sampler(num_classes=2, shape=(BATCH_SIZE, N_CLASS,)):
label = np.zeros(shape)
for i in range(shape[0]):
for _ in range(random.randint(1, shape[1])):
j = random.randint(0, shape[1]-1)
label[i, j] = random.random()
label[i] /= label[i].sum()
return label
def truncated_z_sample(shape=(BATCH_SIZE, Z_DIM,), truncation=1.0):
values = truncnorm.rvs(-2, 2, size=shape)
return truncation * values
def normal_z_sample(shape=(BATCH_SIZE, Z_DIM,)):
return np.random.normal(size=shape)
# --------------------------
# More complex ops
# --------------------------
def sin(opts, key, shape):
noise = lerp(opts, key + '_noise', shape)
scale = InterpolatorParam(name=key + '_scale')
speed = InterpolatorParam(name=key + '_speed')
time = opts['time'].variable
out = tf.sin(time + noise) * scale.variable
opts[key + '_scale'] = scale
opts[key + '_speed'] = speed
return out
def lerp(opts, key, shape):
a = InterpolatorParam(name=key + '_a', shape=shape)
b = InterpolatorParam(name=key + '_b', shape=shape)
n = InterpolatorParam(name=key + '_n')
speed = InterpolatorParam(name=key + '_speed')
out = a.variable * (1 - n.variable) + b.variable * n.variable
opts[key + '_a'] = a
opts[key + '_b'] = b
opts[key + '_n'] = n
opts[key + '_speed'] = speed
return out
# --------------------------
# Placeholder params
# --------------------------
class InterpolatorParam:
def __init__(self, name, dtype=tf.float32, shape=(), value=None, type="noise"):
self.scalar = shape == ()
self.shape = shape
self.type = type
self.value = value or np.zeros(shape)
self.variable = tf.placeholder(dtype=dtype, shape=shape)
def assign(self, value):
self.value = value
def randomize(self, num_classes):
if self.type == 'noise':
val = truncated_z_sample(shape=self.shape)
elif self.type == 'label':
val = label_sampler(shape=self.shape)
self.assign(val)
# --------------------------
# Interpolator graph
# --------------------------
class Interpolator:
def __init__(self):
self.opts = {
'time': InterpolatorParam(name='t', value=time.time()),
'truncation' : InterpolatorParam(name='truncation', value=1.0),
}
def build(self):
lerp_z = lerp(self.opts, 'latent', [BATCH_SIZE, Z_DIM])
sin_z = sin(self.opts, 'orbit', [BATCH_SIZE, Z_DIM])
lerp_label = lerp(self.opts, 'label', [BATCH_SIZE, N_CLASS])
gen_in = {}
gen_in['truncation'] = self.opts['truncation'].variable
gen_in['z'] = lerp_z + sin_z
gen_in['y'] = lerp_label
gen_img = generator(gen_in, signature=gen_signature)
# Convert generated image to channels_first.
self.gen_img = tf.transpose(gen_img, [0, 3, 1, 2])
def get_feed_dict(self):
opt = {}
for key, param in self.opts.items():
opt[param.variable] = param.value
return opt
def get_state(self):
opt = {}
for key, param in self.opts.items():
if param.scalar:
opt[key] = param.value
return opt
def set_value(self, key, value):
self.opts[key].assign(value)
def on_step(self, i, sess):
gen_images = sess.run(self.gen_img, feed_dict=self.get_feed_dict())
return gen_images
def run(self, cmd, payload):
# do things like create a new B and interpolate to it
pass
# --------------------------
# RPC Listener
# --------------------------
class Listener:
def __init__(self):
self.interpolator = Interpolator()
self.interpolator.build()
def connect(self):
self.rpc_client = CortexRPC(self.on_get, self.on_set, self.on_ready, self.on_cmd)
def on_set(self, key, value):
self.opts[key].assign(value)
def on_get(self):
return self.interpolator.get_state()
def on_cmd(self, cmd, payload):
print("got command {}".format(cmd))
self.interpolator.run(cmd, payload)
def on_ready(self, rpc_client):
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.tables_initializer())
print("Ready!")
self.rpc_client = rpc_client
self.rpc_client.send_status('processing', True)
dt = 1 / FPS
for i in range(99999):
print("Step {}".format(i))
gen_time = time.time()
self.interpolator.opts['time'].assign(gen_time)
gen_images = self.interpolator.on_step(i, self.sess)
if gen_images is None:
print("Exiting...")
break
print("Generation time: {:.1f}s".format(time.time() - gen_time))
out_img = vs.data2pil(gen_images[0])
if out_img is not None:
#if out_img.resize_before_sending:
img_to_send = out_img.resize((256, 256), Image.BICUBIC)
meta = {
'i': i,
'sequence_i': i,
'skip_i': 0,
'sequence_len': 99999,
}
self.rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), meta, img_to_send, 'jpg')
self.rpc_client.send_status('processing', False)
self.sess.close()
if __name__ == '__main__':
listener = Listener()
listener.connect()
# layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer
# gen_encoding = tf.get_default_graph().get_tensor_by_name(layer_name)
# ENC_SHAPE = gen_encoding.get_shape().as_list()[1:]
# encoding = tf.get_variable(name='encoding', dtype=tf.float32,
# shape=[BATCH_SIZE,] + ENC_SHAPE)
# tf.contrib.graph_editor.swap_ts(gen_encoding, tf.convert_to_tensor(encoding))
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