path: root/cli/app/search/live.py

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import glob
import h5py
import numpy as np
import json
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
from urllib.parse import parse_qs
import app.search.visualize as vs
from app.search.json import params_dense_dict
from app.utils.file_utils import load_pickle
from app.settings import app_cfg
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 app.search.params import timestamp
from app.utils.tf_utils import read_checkpoint

FPS = 25

params = params_dense_dict('live')

# --------------------------
# 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))
print("Loaded!")

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]

# --------------------------
# Utils
# --------------------------

def clamp(n, a=0, b=1):
  if n < a:
    return a
  if n > b:
    return b
  return n

# --------------------------
# Initializers
# --------------------------

def label_sampler(num_classes=1, shape=(BATCH_SIZE, N_CLASS,)):
  label = np.zeros(shape)
  for i in range(shape[0]):
    for _ in range(int(num_classes)):
      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)

# --------------------------
# Disentangled Latents
# --------------------------

disentangled = {
  'zoom': read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'zoom/model.ckpt'), 'walk')[0],
  'shiftx': read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'shiftx/model.ckpt'), 'walk')[0],
  'shifty': read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'shifty/model.ckpt'), 'walk')[0],
  'rotate2d': read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'rotate2d/model.ckpt'), 'walk')[0],
  'rotate3d': read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'rotate3d/model.ckpt'), 'walk')[0],
}
disentangled_color = read_checkpoint(os.path.join(app_cfg.DISENTANGLED, 'rotate2d/model.ckpt'), 'walk')[0]
disentangled['r'] = disentangled_color[:, 0]
disentangled['g'] = disentangled_color[:, 1]
disentangled['b'] = disentangled_color[:, 2]
disentangled['luminance'] = np.sum(disentangled_color, axis=1)

# --------------------------
# More complex ops
# --------------------------

class SinParam:
  def __init__(self, name, shape, datatype="noise"):
    noise = LerpParam(name + '_noise', shape=shape, datatype=datatype)
    orbit_radius = InterpolatorParam(name=name + '_radius', value=0.25)
    orbit_speed = InterpolatorParam(name=name + '_speed', value=FPS)
    orbit_time = InterpolatorParam(name=name + '_time', value=0.0)
    output = tf.math.sin(orbit_time.variable + noise.output) * orbit_radius.variable
    interpolator.sin_params[name] = self
    self.name = name
    self.orbit_speed = orbit_speed
    self.orbit_time = orbit_time
    self.output = output

  def update(self, dt):
    self.orbit_time.value += self.orbit_speed.value / FPS * dt

class LerpParam:
  def __init__(self, name, shape, a_in=None, b_in=None, datatype="noise"):
    if a_in is not None and b_in is not None:
      a = InterpolatorVariable(variable=a_in)
      b = InterpolatorVariable(variable=b_in)
    else:
      a = InterpolatorParam(name=name + '_a', shape=shape, datatype=datatype)
      b = InterpolatorParam(name=name + '_b', shape=shape, datatype=datatype)
    n = InterpolatorParam(name=name + '_n', value=0.0)
    speed = InterpolatorParam(name=name + '_speed', value=FPS)
    output = a.variable * (1 - n.variable) + b.variable * n.variable
    interpolator.lerp_params[name] = self
    self.name = name
    self.a = a
    self.b = b
    self.n = n
    self.speed = speed
    self.output = output
    self.direction = 0

  def switch(self, target_value=None):
    if self.n.value > 0.5:
      target_param = self.a
      self.direction = -1
    else:
      target_param = self.b
      self.direction = 1
    if target_value is None:
      target_param.randomize()
    else:
      target_param.assign(target_value)

  def update(self, dt):
    if self.direction != 0:
      self.n.value = clamp(self.n.value + self.direction * self.speed.value / FPS * dt)
      print("set_opt: {}_n {}".format(self.name, self.n.value))
      if self.n.value == 0 or self.n.value == 1:
        self.direction = 0

# --------------------------
# Placeholder params
# --------------------------

class InterpolatorParam:
  def __init__(self, name, dtype=tf.float32, shape=(), value=None, datatype="float"):
    self.scalar = shape == ()
    self.shape = shape
    self.datatype = datatype
    if datatype == "float":
      self.assign(value or 0.0)
    else:
      self.randomize()
    self.variable = tf.placeholder(dtype=dtype, shape=shape)
    interpolator.opts[name] = self

  def assign(self, value):
    if self.datatype == 'float':
      self.value = float(value)
    else:
      self.value = value

  def randomize(self):
    if self.datatype == 'noise':
      val = truncated_z_sample(shape=self.shape, truncation=interpolator.opts['truncation'].value)
    elif self.datatype == 'label':
      val = label_sampler(shape=self.shape, num_classes=interpolator.opts['num_classes'].value)
    elif self.datatype == 'encoding':
      val = np.zeros(self.shape)
    else:
      val = 0.0
    self.assign(val)

class InterpolatorVariable:
  def __init__(self, variable):
    self.scalar = False
    self.variable = variable

  def assign(self):
    pass

  def randomize(self):
    pass

# --------------------------
# Interpolator graph
# --------------------------

class Interpolator:
  def __init__(self):
    self.opts = {}
    self.sin_params = {}
    self.lerp_params = {}

  def build(self):
    InterpolatorParam(name='truncation', value=1.0)
    InterpolatorParam(name='num_classes', value=1.0)

    # Latent - initial lerp and wobble
    lerp_z = LerpParam('latent', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
    sin_z = SinParam('orbit', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
    z_sum = lerp_z.output + sin_z.output

    # Latent - saturation
    abs_zoom = InterpolatorParam(name='abs_zoom', value=1.0)
    z_abs = z_sum / tf.abs(z_sum) * abs_zoom.variable
    z_mix = LerpParam('abs_mix', a_in=z_sum, b_in=z_abs, shape=[BATCH_SIZE, Z_DIM], datatype="input")

    # Latent - disentangled vectors
    zoom = InterpolatorParam(name='zoom', value=0.0).variable * disentangled['zoom']
    shiftx = InterpolatorParam(name='shiftx', value=0.0).variable * disentangled['shiftx']
    shifty = InterpolatorParam(name='shifty', value=0.0).variable * disentangled['shifty']
    luminance = InterpolatorParam(name='luminance', value=0.0).variable * disentangled['luminance']
    disentangled = z_mix.output + zoom + shiftx + shifty + luminance

    # Latent - stored vector
    latent_stored = LerpParam(name='latent_stored', shape=[BATCH_SIZE, Z_DIM], datatype="noise")
    latent_stored_mix = LerpParam('latent_stored_mix', a_in=disentangled, b_in=latent_stored.output, shape=[BATCH_SIZE, Z_DIM], datatype="input")

    # Label
    lerp_label = LerpParam('label', shape=[BATCH_SIZE, N_CLASS], datatype="label")

    # Latent - stored vector
    label_stored = LerpParam(name='label_stored', shape=[BATCH_SIZE, N_CLASS], datatype="label")
    label_stored_mix = LerpParam('label_stored_mix', a_in=lerp_label.output, b_in=label_stored.output, shape=[BATCH_SIZE, Z_DIM], datatype="input")

    # Generator
    gen_in = {}
    gen_in['truncation'] = 1.0 # self.opts['truncation'].variable
    gen_in['z'] = latent_stored_mix.output
    gen_in['y'] = label_stored_mix.output
    self.gen_img = generator(gen_in, signature=gen_signature)

    # Encoding - first hidden layer
    gen_layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer
    encoding_latent = tf.get_default_graph().get_tensor_by_name(gen_layer_name)

    encoding_shape = encoding_latent.get_shape().as_list()
    encoding_shape_np = tuple([1,] + encoding_shape[1:])

    encoding_shape_placeholder = tf.constant(np.zeros(encoding_shape_np, dtype=np.float32))
    encoding_stored = LerpParam('encoding_stored', shape=encoding_shape_np, datatype="encoding")
    encoding_stored_mix = LerpParam('encoding_stored_mix', a_in=encoding_shape_placeholder, b_in=encoding_stored.output, shape=encoding_shape_np, datatype="encoding")

    # Use the placeholder to redirect parts of the graph.
    # - computed encoding goes into the encoding_mix
    # - encoding mix output goes into the main biggan graph
    # We do it this way so the encoding_latent won't be going into two places at once.
    tf.contrib.graph_editor.swap_ts(encoding_shape_placeholder, encoding_latent)
    tf.contrib.graph_editor.swap_ts(encoding_stored_mix.output, encoding_shape_placeholder)

    # Make all the stored lerps use the same interpolation amount.
    tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, latent_stored.n.variable)
    tf.contrib.graph_editor.reroute_ts(encoding_stored.n.variable, label_stored.n.variable)
    tf.contrib.graph_editor.reroute_ts(encoding_stored_mix.n.variable, latent_stored_mix.n.variable)
    tf.contrib.graph_editor.reroute_ts(encoding_stored_mix.n.variable, label_stored_mix.n.variable)

    sys.stderr.write("Sin params: {}\n".format(", ".join(self.sin_params.keys())))
    sys.stderr.write("Lerp params: {}\n".format(", ".join(self.lerp_params.keys())))
    sys.stderr.write("Opts: {}\n".format(", ".join(self.opts.keys())))

  def get_feed_dict(self):
    opt = {}
    for param in self.opts.values():
      opt[param.variable] = param.value
    return opt

  def get_state(self):
    opt = {}
    for key, param in self.opts.items():
      if param.scalar:
        if type(param.value) is np.ndarray:
          sys.stderr.write('{} is ndarray\n'.format(key))
          opt[key] = param.value.tolist()
        else:
          opt[key] = param.value
    return opt

  def set_value(self, key, value):
    if key in self.opts:
      self.opts[key].assign(float(value))
    else:
      sys.stderr.write('{} not a valid option\n'.format(key))

  def set_category(self, category):
    print("Set category: {}".format(category))
    categories = category.split(" ")
    label = np.zeros((BATCH_SIZE, N_CLASS,))
    for category in categories:
      index = int(category)
      if index > 0 and index < N_CLASS:
        label[0, index] = 1.0
    label[0] /= label[0].sum()
    self.lerp_params['label'].switch(target_value=label)

  def set_encoding(self, opt):
    next_id = opt['id']
    data = load_pickle(os.path.join(app_cfg.DIR_VECTORS, "file_{}.pkl".format(next_id)))
    
    new_encoding = np.expand_dims(data['encoding'], axis=0)
    new_label = np.expand_dims(data['label'], axis=0)
    new_latent = np.expand_dims(data['latent'], axis=0)

    latent_stored = self.lerp_params['latent_stored']
    label_stored = self.lerp_params['label_stored']
    encoding_stored = self.lerp_params['encoding_stored']
    encoding_stored_mix = self.lerp_params['encoding_stored_mix']

    # if we're showing an encoding already, lerp to the next one
    if encoding_stored_mix.n.value > 0:
      encoding_stored.switch(target_value=new_encoding)
      label_stored.switch(target_value=new_label)
      latent_stored.switch(target_value=new_latent)
    # otherwise (we're showing the latent)...
    else:
      # jump to the stored encoding, then switch
      if encoding_stored.n.value < 0.5:
        encoding_stored.n.assign(0)
        encoding_stored.a.assign(new_encoding)
        latent_stored.a.assign(new_latent)
        label_stored.a.assign(new_label)
      else:
        encoding_stored.n.assign(1)
        encoding_stored.b.assign(new_encoding)
        latent_stored.b.assign(new_latent)
        label_stored.b.assign(new_label)
      encoding_stored_mix.switch()

  def on_step(self, i, dt, sess):
    for param in self.sin_params.values():
      param.update(dt)
    for param in self.lerp_params.values():
      param.update(dt)
    gen_images = sess.run(self.gen_img, feed_dict=self.get_feed_dict())
    return gen_images

  def run(self, cmd, payload):
    print("Command: {} {}".format(cmd, payload))
    if cmd == 'switch' and payload in self.lerp_params:
      self.lerp_params[payload].switch()
    if cmd == 'setCategory':
      self.set_category(payload)
    if cmd == 'setEncoding':
      self.set_encoding(json.loads(payload))
    pass

# --------------------------
# RPC Listener
# --------------------------

interpolator = Interpolator()

class Listener:
  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):
    print("{}: {} {}".format(key, str(type(value)), value))
    interpolator.set_value(key, value)

  def on_get(self):
    state = interpolator.get_state()
    sys.stderr.write(json.dumps(state) + "\n")
    sys.stderr.flush()
    return state

  def on_cmd(self, cmd, payload):
    print("got command {}".format(cmd))
    interpolator.run(cmd, payload)

  def on_ready(self, rpc_client):
    self.rpc_client = rpc_client
    print("Starting session...")
    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("Building interpolator...")
    interpolator.build()
    self.rpc_client.send_status('processing', True)
    tag = "biggan_" + timestamp()
    path_out = os.path.join(app_cfg.DIR_RESULTS, tag)
    os.makedirs(path_out, exist_ok=True)
    dt = 1 / FPS
    for i in range(99999):
      if i == 0:
        print("Loading network...")
      elif i == 1:
        print("Processing!")
      gen_time = time.time()
      gen_images = interpolator.on_step(i, dt, self.sess)
      if gen_images is None:
        print("Exiting...")
        break
      if (i % 100) == 0 or i == 1:
        print("Step {}. Generation time: {:.2f}s".format(i, time.time() - gen_time))
      out_img = vs.data2pil(gen_images[0])
      if out_img is not None:
        out_img.save(os.path.join(path_out, "frame_{:05d}.png".format(i)), format='png')
        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()