S3GAN Kullanarak Küçük Verilerle Görüntü Oluşturma

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın GitHub'da görüntüle Not defterini indir TF Hub modellerine bakın

Bu not defteri, kendi kendine ve yarı denetimli öğrenme teknikleri kullanılarak %2,5 kadar az etiketlenmiş veriyle ImageNet üzerinde eğitilmiş Üretken Çelişki Ağlarının bir demosudur. Hem jeneratör ve ayırt edici modelleri mevcuttur TF Hub .

Modelleri ve eğitim prosedürü hakkında daha fazla bilgi için lütfen bkz blog yayınını ve kağıt [1]. Bu modellerin eğitimi için kod mevcuttur GitHub'dan .

Başlamak için bir çalışma zamanına bağlanın ve şu adımları izleyin:

  1. (İsteğe bağlı) Aşağıdaki ikinci kod hücresinden bir model seçin.
  2. Sırayla her bir hücreyi çalıştırmak için tüm Çalıştır> Runtime tıklayın.
    • Daha sonra, kaydırıcıları ve açılır menüleri kullanarak ayarları değiştirdiğinizde etkileşimli görselleştirmeler otomatik olarak güncellenmelidir.

[1] Mario Lucic * Michael Tschannen * Marvin Ritter *, Xiaohua Zhai, Olivier Bachem Sylvain Gelly, az Etiketler ile Yüksek Sadakat Görüntü Üretimi , ICML 2019.

Kurmak

# @title Imports and utility functions
import os

import IPython
from IPython.display import display
import numpy as np
import PIL.Image
import pandas as pd
import six

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

import tensorflow_hub as hub

def imgrid(imarray, cols=8, pad=1):
  pad = int(pad)
  assert pad >= 0
  cols = int(cols)
  assert cols >= 1
  N, H, W, C = imarray.shape
  rows = int(np.ceil(N / float(cols)))
  batch_pad = rows * cols - N
  assert batch_pad >= 0
  post_pad = [batch_pad, pad, pad, 0]
  pad_arg = [[0, p] for p in post_pad]
  imarray = np.pad(imarray, pad_arg, 'constant')
  H += pad
  W += pad
  grid = (imarray
          .reshape(rows, cols, H, W, C)
          .transpose(0, 2, 1, 3, 4)
          .reshape(rows*H, cols*W, C))
  return grid[:-pad, :-pad]


def imshow(a, format='png', jpeg_fallback=True):
  a = np.asarray(a, dtype=np.uint8)
  if six.PY3:
    str_file = six.BytesIO()
  else:
    str_file = six.StringIO()
  PIL.Image.fromarray(a).save(str_file, format)
  png_data = str_file.getvalue()
  try:
    disp = display(IPython.display.Image(png_data))
  except IOError:
    if jpeg_fallback and format != 'jpeg':
      print ('Warning: image was too large to display in format "{}"; '
             'trying jpeg instead.').format(format)
      return imshow(a, format='jpeg')
    else:
      raise
  return disp


class Generator(object):

  def __init__(self, module_spec):
    self._module_spec = module_spec
    self._sess = None
    self._graph = tf.Graph()
    self._load_model()

  @property
  def z_dim(self):
    return self._z.shape[-1].value

  @property
  def conditional(self):
    return self._labels is not None

  def _load_model(self):
    with self._graph.as_default():
      self._generator = hub.Module(self._module_spec, name="gen_module",
                                   tags={"gen", "bsNone"})
      input_info = self._generator.get_input_info_dict()
      inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
                for k, v in self._generator.get_input_info_dict().items()}
      self._samples = self._generator(inputs=inputs, as_dict=True)["generated"]
      print("Inputs:", inputs)
      print("Outputs:", self._samples)
      self._z = inputs["z"]
      self._labels = inputs.get("labels", None)

  def _init_session(self):
    if self._sess is None:
      self._sess = tf.Session(graph=self._graph)
      self._sess.run(tf.global_variables_initializer())

  def get_noise(self, num_samples, seed=None):
    if np.isscalar(seed):
      np.random.seed(seed)
      return np.random.normal(size=[num_samples, self.z_dim])
    z = np.empty(shape=(len(seed), self.z_dim), dtype=np.float32)
    for i, s in enumerate(seed):
      np.random.seed(s)
      z[i] = np.random.normal(size=[self.z_dim])
    return z

  def get_samples(self, z, labels=None):
    with self._graph.as_default():
      self._init_session()
      feed_dict = {self._z: z}
      if self.conditional:
        assert labels is not None
        assert labels.shape[0] == z.shape[0]
        feed_dict[self._labels] = labels
      samples = self._sess.run(self._samples, feed_dict=feed_dict)
      return np.uint8(np.clip(256 * samples, 0, 255))


class Discriminator(object):

  def __init__(self, module_spec):
    self._module_spec = module_spec
    self._sess = None
    self._graph = tf.Graph()
    self._load_model()

  @property
  def conditional(self):
    return "labels" in self._inputs

  @property
  def image_shape(self):
    return self._inputs["images"].shape.as_list()[1:]

  def _load_model(self):
    with self._graph.as_default():
      self._discriminator = hub.Module(self._module_spec, name="disc_module",
                                       tags={"disc", "bsNone"})
      input_info = self._discriminator.get_input_info_dict()
      self._inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)
                      for k, v in input_info.items()}
      self._outputs = self._discriminator(inputs=self._inputs, as_dict=True)
      print("Inputs:", self._inputs)
      print("Outputs:", self._outputs)

  def _init_session(self):
    if self._sess is None:
      self._sess = tf.Session(graph=self._graph)
      self._sess.run(tf.global_variables_initializer())

  def predict(self, images, labels=None):
    with self._graph.as_default():
      self._init_session()
      feed_dict = {self._inputs["images"]: images}
      if "labels" in self._inputs:
        assert labels is not None
        assert labels.shape[0] == images.shape[0]
        feed_dict[self._inputs["labels"]] = labels
      return self._sess.run(self._outputs, feed_dict=feed_dict)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:111: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.
Instructions for updating:
non-resource variables are not supported in the long term

Bir model seçin

# @title Select a model { run: "auto" }

model_name = "S3GAN 128x128 20% labels (FID 6.9, IS 98.1)"  # @param ["S3GAN 256x256 10% labels (FID 8.8, IS 130.7)", "S3GAN 128x128 2.5% labels (FID 12.6, IS 48.7)", "S3GAN 128x128 5% labels (FID 8.4, IS 74.0)", "S3GAN 128x128 10% labels (FID 7.6, IS 90.3)", "S3GAN 128x128 20% labels (FID 6.9, IS 98.1)"]
models = {
    "S3GAN 256x256 10% labels": "https://tfhub.dev/google/compare_gan/s3gan_10_256x256/1",
    "S3GAN 128x128 2.5% labels": "https://tfhub.dev/google/compare_gan/s3gan_2_5_128x128/1",
    "S3GAN 128x128 5% labels": "https://tfhub.dev/google/compare_gan/s3gan_5_128x128/1",
    "S3GAN 128x128 10% labels": "https://tfhub.dev/google/compare_gan/s3gan_10_128x128/1",
    "S3GAN 128x128 20% labels": "https://tfhub.dev/google/compare_gan/s3gan_20_128x128/1",
}

module_spec = models[model_name.split(" (")[0]]
print("Module spec:", module_spec)

tf.reset_default_graph()
print("Loading model...")
sampler = Generator(module_spec)
print("Model loaded.")

Module spec: https://tfhub.dev/google/compare_gan/s3gan_20_128x128/1
Loading model...
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
Inputs: {'labels': <tf.Tensor 'labels:0' shape=(?,) dtype=int32>, 'z': <tf.Tensor 'z:0' shape=(?, 120) dtype=float32>}
Outputs: Tensor("gen_module_apply_default/generator_1/truediv:0", shape=(?, 128, 128, 3), dtype=float32)
Model loaded.

Örnek

png

png

ayrımcı

disc = Discriminator(module_spec)

batch_size = 4
num_classes = 1000
images = np.random.random(size=[batch_size] + disc.image_shape)
labels = np.random.randint(0, num_classes, size=(batch_size))

disc.predict(images, labels=labels)

INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
Inputs: {'labels': <tf.Tensor 'labels:0' shape=(?,) dtype=int32>, 'images': <tf.Tensor 'images:0' shape=(?, 128, 128, 3) dtype=float32>}
Outputs: {'prediction': <tf.Tensor 'disc_module_apply_default/discriminator/Sigmoid:0' shape=(?, 1) dtype=float32>}
{'prediction': array([[0.82321566],
        [0.89030766],
        [0.8621534 ],
        [0.88563395]], dtype=float32)}