tensorflow - 制作图像生成器时遇到问题

Question

所以我已经关注了这个https://www.tensorflow.org/tutorials/generation/dcgan，它工作得很好。所以现在我试图做同样的事情，但我自己的数据（69 850x480 图像）。很明显，我使用的数组之一是错误的大小 [32, 850]。我最好的猜测是数组的一部分正在某处丢失，因为它的形状应该是 [32, 850, 480, 1]。我真的很难看出假设这实际上是问题所在。

更改示例代码：

    import tensorflow as tf 
    #print(tf.__version__)#want 2.2.0
    import glob
    import imageio
    import matplotlib.pyplot as plt
    import numpy as np
    import os
    import cv2
    import PIL
    from tensorflow.keras import layers
    import time
    
    from IPython import display
    
    DATADIR = "C:\image generator\marudata"
    Category = ["meow"]
    
    path = os.path.join(DATADIR)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
    train_images = img_array
    
    train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]
    #127.5 = 255/2 (0 is black 255 is white)
    
    BUFFER_SIZE = 13056000
    BATCH_SIZE = 32
###
    def make_generator_model():
        model = tf.keras.Sequential()
        model.add(layers.Dense(5*6*13600, use_bias=False, input_shape=(100,)))
        model.add(layers.BatchNormalization())
        model.add(layers.LeakyReLU())
    
        model.add(layers.Reshape((5, 6, 13600)))
        assert model.output_shape == (None, 5, 6, 13600) # Note: None is the batch size
    
        model.add(layers.Conv2DTranspose(256, (5, 5), strides=(1, 1), padding='same', use_bias=False))#transpose layer is the inverse of a normal filter layer. 
        #so strides act more like 1/2 instead of 2
        assert model.output_shape == (None, 5, 6, 256)
        model.add(layers.BatchNormalization())
        model.add(layers.LeakyReLU())
    
        model.add(layers.Conv2DTranspose(128, (5, 5), strides=(5, 2), padding='same', use_bias=False))
        assert model.output_shape == (None, 25, 12, 128)
        model.add(layers.BatchNormalization())
        model.add(layers.LeakyReLU())
    
        model.add(layers.Conv2DTranspose(1, (5, 5), strides=(34, 40), padding='same', use_bias=False, activation='tanh'))
        assert model.output_shape == (None, 850, 480, 1)
    
        return model
###useing this to cut up code to reduce wall of text size

    def make_discriminator_model():
        model = tf.keras.Sequential()
        model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                         input_shape=[850, 480, 1]))
        model.add(layers.LeakyReLU())
        model.add(layers.Dropout(0.3))
    
        model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
        model.add(layers.LeakyReLU())
        model.add(layers.Dropout(0.3))
    
        model.add(layers.Flatten())
        model.add(layers.Dense(1))
    
        return model

错误

Traceback (most recent call last):
  File "C:\image generator\image_generator.py", line 197, in <module>
    train(train_dataset, EPOCHS)
  File "C:\image generator\image_generator.py", line 162, in train
    train_step(image_batch)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 627, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 506, in _initialize
    *args, **kwds))
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\function.py", line 2446, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\function.py", line 2777, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\function.py", line 2667, in _create_graph_function
    capture_by_value=self._capture_by_value),
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\framework\func_graph.py", line 981, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 441, in wrapped_fn
    return weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\framework\func_graph.py", line 968, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    C:\image generator\image_generator.py:145 train_step  *
        real_output = discriminator(images, training=True)
    C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:886 __call__  **
        self.name)
    C:\Users\will\miniconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:180 assert_input_compatibility
        str(x.shape.as_list()))

    ValueError: Input 0 of layer sequential_1 is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [32, 850]

第 138-197 行

@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
      generated_images = generator(noise, training=True)

      real_output = discriminator(images, training=True)
      fake_output = discriminator(generated_images, training=True)

      gen_loss = generator_loss(fake_output)
      disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

def train(dataset, epochs):
  for epoch in range(epochs):
    start = time.time()

    for image_batch in dataset:
      train_step(image_batch)#line 162

    # Produce images for the GIF as we go
    display.clear_output(wait=True)
    generate_and_save_images(generator,
                             epoch + 1,
                             seed)

    # Save the model every 10 epochs
    if (epoch + 1) % 10 == 0:
      checkpoint.save(file_prefix = checkpoint_prefix)

    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))

  # Generate after the final epoch
  display.clear_output(wait=True)
  generate_and_save_images(generator,
                           epochs,
                           seed)

def generate_and_save_images(model, epoch, test_input):
  # Notice `training` is set to False.
  # This is so all layers run in inference mode (batchnorm).
  predictions = model(test_input, training=False)

  fig = plt.figure(figsize=(4,4))

  for i in range(predictions.shape[0]):
      plt.subplot(4, 4, i+1)
      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
      plt.axis('off')

  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
  #plt.show()

train(train_dataset, EPOCHS)#line 197

其他所有内容仍应是所提供链接中代码的副本。

Answer 1

我是个白痴，只导入了 1 个 img

import tensorflow as tf 
#print(tf.__version__)#want 2.2.0
import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import cv2
import PIL
from tensorflow.keras import layers
import time

from IPython import display

DATADIR = "C:\image generator\marudata"
Category = ["meow"]

path = os.path.join(DATADIR)
for img in os.listdir(path):
  img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
  #plt.imshow(img_array, cmap="gray")
  #plt.show()#just show first images in directory
  #break
#print(img_array.shape)#shows array size
WIDTH = 448
HEIGHT = 256


#plt.imshow(new_array, cmap='gray')
#plt.show()

training_data = []

def create_training_data():
  path = os.path.join(DATADIR)
  for img in os.listdir(path):
    img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
    new_array = cv2.resize(img_array, (WIDTH, HEIGHT))
    training_data.append([new_array])

create_training_data()
print(len(training_data))

train_images = []

for features in training_data:
  train_images.append(features)

train_images = np.array(train_images).reshape(-1, HEIGHT, WIDTH, 1)
print(train_images.shape)
BUFFER_SIZE = 284
BATCH_SIZE = 32

train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

我现在遇到了使用太多内存的问题。对于比 28X28 大得多的图像，也许不可能做这样的事情。它确实适用于 425x240 图像：O