Ajuda a proteger a Grande Barreira de Corais com TensorFlow em Kaggle Junte Desafio

Gerando imagens com poucos dados usando S3GAN

Ver no TensorFlow.org Executar no Google Colab Ver no GitHub Baixar caderno Veja os modelos TF Hub

Este caderno é uma demonstração de Redes Adversariais Gerativas treinadas em ImageNet com apenas 2,5% de dados rotulados usando técnicas de aprendizagem auto e semissupervisionada. Ambos os modelos de geradores e discriminadores estão disponíveis no TF Hub .

Para mais informações sobre os modelos e o procedimento de treinamento ver o nosso blogpost eo papel [1]. O código para a formação destes modelos está disponível no GitHub .

Para começar, conecte-se a um tempo de execução e siga estas etapas:

  1. (Opcional) Selecione um modelo na segunda célula de código abaixo.
  2. Clique Runtime> Executar tudo a correr cada célula em ordem.
    • Depois disso, as visualizações interativas devem ser atualizadas automaticamente quando você modifica as configurações usando os controles deslizantes e menus suspensos.

[1] Mario Lucic *, Michael Tschannen *, Marvin Ritter *, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly, de alta fidelidade de imagem Geração Com Etiquetas Menos , ICML 2019.

Configurar

# @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

Selecione um modelo

# @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.

Amostra

png

png

Discriminador

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)}