having trouble making an image generator

Question

So i already followed this https://www.tensorflow.org/tutorials/generative/dcgan and it worked just fine. So now I'm attempting to do the same thing but on my own data (of 69 850x480 images). It seems pretty obvious that one of the arrays I'm using is the wrong size [32, 850]. my best guess is part of the array are being lost somewhere as it should be of shape [32, 850, 480, 1]. I'm just really struggling to see where assuming that's actually the problem.

changes to example code:

    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

error

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]

lines 138-197

@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

everything else should still be a carbon copy of the code in the link provided.

Will Mulcahey · Answer 1 · 2020-07-09T19:24:34.247

I'm an idiot and was only importing 1 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)

I'm now having problem with using way too much memory. Maybe it's just impossible to do something like this for images that are much larger than 28X28. It does work on 425x240 images though :O

having trouble making an image generator

1 Answers1