使用 S3GAN 通过少量数据生成图像

View 在 TensorFlow.org 上查看 在 Google Colab 中运行 在 GitHub 上查看源代码 下载笔记本 查看 TF Hub 模型

此笔记本演示了一个生成对抗网络,该网络运用自监督和半监督学习技术,仅使用 2.5% 的带标签数据在 ImageNet 上进行了训练。生成器和判别器模型均可从 TF Hub 获得。

有关模型和训练过程的更多信息,请参阅我们的博文论文 [1]。训练这些模型的代码可在 GitHub 上获得。

首先,连接到运行时并按照以下步骤操作:

  1. (可选)在下面的第二个代码单元中选择一个模型。
  2. 点击 Runtime > Run all 按顺序运行每个单元。
  • 之后,当您使用滑块和下拉菜单修改设置时,交互式可视化应该会自动更新。

注:如果遇到任何问题,您可以通过点击 Runtime > Restart and run all...,尝试重启运行时并重新运行所有单元。

[1] Mario Lucic, Michael Tschannen, Marvin Ritter*, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly, High-Fidelity Image Generation With Fewer Labels, ICML 2019.

设置

# @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:101: 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

选择模型

# @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.

样本

2021-08-13 20:28:05.599404: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:05.609889: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:05.610890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:05.612560: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-08-13 20:28:05.613138: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:05.614197: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:05.615149: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:06.211132: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:06.212169: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:06.213070: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:06.213970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0
2021-08-13 20:28:09.092406: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100
2021-08-13 20:28:09.680099: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory

png

png

判别器

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>}
2021-08-13 20:28:11.462816: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:11.463333: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:11.463655: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:11.464048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:11.464439: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-13 20:28:11.464740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14648 MB memory:  -> device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:00:05.0, compute capability: 7.0
{'prediction': array([[0.82321566],
        [0.89030766],
        [0.8621534 ],
        [0.88563395]], dtype=float32)}