我有一个 GAN 输出，我可以将以下输出序列中的图像保存为单个图像吗？

Question

例如考虑这个图像，我想将每个部分保存为单个图像：

这是代码，这将在每个时期后给出一系列输出，我想将每个小输出保存为单个图像。我该如何继续？

import tensorflow as tf
from tensorflow.keras.layers import Input, Reshape, Dropout, Dense 
from tensorflow.keras.layers import Flatten, BatchNormalization
from tensorflow.keras.layers import Activation, ZeroPadding2D
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.layers import UpSampling2D, Conv2D
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.optimizers import Adam
import numpy as np
from PIL import Image
from tqdm import tqdm
import os 
import time
import matplotlib.pyplot as plt

from google.colab import drive
drive.mount('/content/drive')

 GENERATE_RES = 3
 GENERATE_SQUARE = 32 * GENERATE_RES # rows/cols (should be square)
 IMAGE_CHANNELS = 3
 # Preview image 
 PREVIEW_ROWS = 4
 PREVIEW_COLS = 7
 PREVIEW_MARGIN = 16

 # Size vector to generate images from
 SEED_SIZE = 100

 # Configuration
 DATA_PATH = '/content/drive/MyDrive/cars/images'
 EPOCHS = 50
 BATCH_SIZE = 32
 BUFFER_SIZE = 60000

 print(f"Will generate {GENERATE_SQUARE}px square images.")
 def hms_string(sec_elapsed):
    h = int(sec_elapsed / (60 * 60))
    m = int((sec_elapsed % (60 * 60)) / 60)
    s = sec_elapsed % 60
    return "{}:{:>02}:{:>05.2f}".format(h, m, s)



 training_binary_path = os.path.join(DATA_PATH,
        f'training_data_{GENERATE_SQUARE}_{GENERATE_SQUARE}.npy')

 print(f"Looking for file: {training_binary_path}")

 if not os.path.isfile(training_binary_path):
     start = time.time()
     print("Loading training images...")

     training_data = []
     faces_path = os.path.join(DATA_PATH)
     for filename in tqdm(os.listdir(faces_path)):
         path = os.path.join(faces_path,filename)
         image = Image.open(path).resize((GENERATE_SQUARE,
           GENERATE_SQUARE),Image.ANTIALIAS)
         training_data.append(np.asarray(image))
     training_data = np.reshape(training_data,(-1,GENERATE_SQUARE,
            GENERATE_SQUARE,IMAGE_CHANNELS))
     training_data = training_data.astype(np.float32)
     training_data = training_data / 127.5 - 1.


     print("Saving training image binary...")
     np.save(training_binary_path,training_data)
     elapsed = time.time()-start
     print (f'Image preprocess time: {hms_string(elapsed)}')
else:
     print("Loading previous training pickle...")
     training_data = np.load(training_binary_path)

#shuffle the data
 train_dataset = tf.data.Dataset.from_tensor_slices(training_data) \
.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

def build_generator(seed_size, channels):
  model = Sequential()

  model.add(Dense(4*4*256,activation="relu",input_dim=seed_size))
  model.add(Reshape((4,4,256)))

  model.add(UpSampling2D())
  model.add(Conv2D(256,kernel_size=3,padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  model.add(UpSampling2D())
  model.add(Conv2D(256,kernel_size=3,padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  # Output resolution, additional upsampling
  model.add(UpSampling2D())
  model.add(Conv2D(128,kernel_size=3,padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(Activation("relu"))

  if GENERATE_RES>1:
    model.add(UpSampling2D(size=(GENERATE_RES,GENERATE_RES)))
    model.add(Conv2D(128,kernel_size=3,padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

  # Final CNN layer
  model.add(Conv2D(channels,kernel_size=3,padding="same"))
  model.add(Activation("tanh"))

  return model


def build_discriminator(image_shape):
  model = Sequential()

  model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=image_shape, 
                 padding="same"))
  model.add(LeakyReLU(alpha=0.2))

  model.add(Dropout(0.25))
  model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
  model.add(ZeroPadding2D(padding=((0,1),(0,1))))
  model.add(BatchNormalization(momentum=0.8))
  model.add(LeakyReLU(alpha=0.2))

  model.add(Dropout(0.25))
  model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(LeakyReLU(alpha=0.2))

  model.add(Dropout(0.25))
  model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(LeakyReLU(alpha=0.2))

  model.add(Dropout(0.25))
  model.add(Conv2D(512, kernel_size=3, strides=1, padding="same"))
  model.add(BatchNormalization(momentum=0.8))
  model.add(LeakyReLU(alpha=0.2))

  model.add(Dropout(0.25))
  model.add(Flatten())
  model.add(Dense(1, activation='sigmoid'))

  return model

def save_images(cnt,noise):
  image_array = np.full(( 
    PREVIEW_MARGIN + (PREVIEW_ROWS * (GENERATE_SQUARE+PREVIEW_MARGIN)), 
    PREVIEW_MARGIN + (PREVIEW_COLS * (GENERATE_SQUARE+PREVIEW_MARGIN)), 3), 
    255, dtype=np.uint8)

generated_images = generator.predict(noise)

generated_images = 0.5 * generated_images + 0.5

image_count = 0
for row in range(PREVIEW_ROWS):
    for col in range(PREVIEW_COLS):
      r = row * (GENERATE_SQUARE+16) + PREVIEW_MARGIN
      c = col * (GENERATE_SQUARE+16) + PREVIEW_MARGIN
      image_array[r:r+GENERATE_SQUARE,c:c+GENERATE_SQUARE] \
        = generated_images[image_count] * 255
      image_count += 1

      
output_path = os.path.join(DATA_PATH,'output')
if not os.path.exists(output_path):
  os.makedirs(output_path)

filename = os.path.join(output_path,f"train-{cnt}.png")
im = Image.fromarray(image_array)
im.save(filename)

generator = build_generator(SEED_SIZE, IMAGE_CHANNELS)

noise = tf.random.normal([1, SEED_SIZE])
generated_image = generator(noise, training=False)

plt.imshow(generated_image[0, :, :, 0])

image_shape = (GENERATE_SQUARE,GENERATE_SQUARE,IMAGE_CHANNELS)

discriminator = build_discriminator(image_shape)
decision = discriminator(generated_image)
print (decision)

cross_entropy = tf.keras.losses.BinaryCrossentropy()

def discriminator_loss(real_output, fake_output):
  real_loss = cross_entropy(tf.ones_like(real_output), real_output)
  fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
  total_loss = real_loss + fake_loss
  return total_loss

def generator_loss(fake_output):
  return cross_entropy(tf.ones_like(fake_output), fake_output)

generator_optimizer = tf.keras.optimizers.Adam(1.5e-4,0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(1.5e-4,0.5)

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

  with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
    generated_images = generator(seed, 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))
  return gen_loss,disc_loss

def train(dataset, epochs):
  fixed_seed = np.random.normal(0, 1, (PREVIEW_ROWS * PREVIEW_COLS, 
                                   SEED_SIZE))
  start = time.time()

  for epoch in range(epochs):
    epoch_start = time.time()

    gen_loss_list = []
    disc_loss_list = []

   for image_batch in dataset:
     t = train_step(image_batch)
     gen_loss_list.append(t[0])
     disc_loss_list.append(t[1])

    g_loss = sum(gen_loss_list) / len(gen_loss_list)
    d_loss = sum(disc_loss_list) / len(disc_loss_list)

    epoch_elapsed = time.time()-epoch_start
    print (f'Epoch {epoch+1}, gen loss={g_loss},disc loss={d_loss},'\
       ' {hms_string(epoch_elapsed)}')
    save_images(epoch,fixed_seed)

elapsed = time.time()-start
print (f'Training time: {hms_string(elapsed)}')

train(train_dataset, EPOCHS)

Answer 1

#预览图片设置如下 PREVIEW_ROWS = 1 PREVIEW_COLS = 1 PREVIEW_MARGIN = 16