python - Keras 内存分配问题

Question

我一直在尝试构建一个深度学习模型，使用 Keras 作为 API 与 tensorflow 2.2 和 cuda 102.89。我的数据集“相对较大”（27500 张 400x400 图像），因此我尝试使用keras.utils.sequence（此处）和tf.keras.preprocessing.image.ImageDataGenerator（此处）将这些图像批量放入内存中。然而，到目前为止，这些都不起作用，我一定没有正确使用它，但我看不出我的代码有什么问题。

使用keras.utils.sequence：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from tensorflow import keras

class BatchGenerator(keras.utils.Sequence):

    def __init__(self, inputs, targets, batch_size=32):

        self.inputs = inputs
        self.targets = targets
        self.batch_size = batch_size

    def __len__(self):
        return len(self.targets) // self.batch_size

    def __getitem__(self, idx):
        i = idx * self.batch_size
    
        x = self.inputs[i : i + self.batch_size]
        y = self.targets[i : i + self.batch_size]
    
        return x, y

# Load images and labels with normalization and one hot encoding
# images: np.array, (27512, 480, 480, 3); labels: np.array, (27512, 480, 480, 9); nb_class = 9
images, labels, nb_class = data_loader('../Data/Images', '../Data/labels')

# Creation of the 0.2 validation split
train = list(range(images.shape[0]//5, images.shape[0]))
test = list(range(0, images.shape[0]//5))

# Batch generation
train_gen = BatchGenerator(images[train], labels[train], batch_size, data_aug)
valid_gen = BatchGenerator(images[test], labels[test], batch_size, data_aug)

# Model fitting
history = model.fit(train_gen, epochs=nb_epoch, verbose=1,
        validation_data=valid_gen, shuffle=False, callbacks=callbacks)

使用ImageDataGenerator链接和flow 链接：

from keras.preprocessing.image import ImageDataGenerator

# Load images and labels with normalization and one hot encoding
# images: np.array, (27512, 480, 480, 3); labels: np.array, (27512, 480, 480, 9); nb_class = 9
images, labels, nb_class = data_loader('../Data/Images', '../Data/labels')

# Batch generation with data augmentation instance
datagen = ImageDataGenerator(
    vertical_flip=True,
    horizontal_flip=True,
    preprocessing_function=augmentations_color,
    samplewise_center=True, 
    samplewise_std_normalization=True,
    validation_split=0.2
    )

# Batch generation application
train_gen = datagen.flow(images, labels, batch_size=16,
        shuffle=True, subset='training')
valid_gen = datagen.flow(images, labels, batch_size=16,
        shuffle=True, subset='validation')

# Model fitting 
history = model.fit(train_gen, epochs=nb_epoch, verbose=1,
        validation_data=valid_gen, shuffle=False, callbacks=callbacks)

在这两种情况下，代码在模型拟合部分期间崩溃BatchGenerator并且永远不会到达抱怨：

Traceback (most recent call last):
  File "main.py", line 190, in <module>
    conf_file=train_set.cfg_path)
  File "main.py", line 92, in main
    shuffle=True, subset='training')
  File "/hpc_htom/kjam268/Virtual_ENV/HistoTAG/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py", line 434, in flow
    dtype=self.dtype
  File "/hpc_htom/kjam268/Virtual_ENV/HistoTAG/lib/python3.6/site-packages/keras_preprocessing/image/numpy_array_iterator.py", line 103, in __init__
    np.unique(y[split_idx:]))):
  File "<__array_function__ internals>", line 6, in unique
  File "/hpc_htom/kjam268/Virtual_ENV/HistoTAG/lib/python3.6/site-packages/numpy/lib/arraysetops.py", line 261, in unique
    ret = _unique1d(ar, return_index, return_inverse, return_counts)
  File "/hpc_htom/kjam268/Virtual_ENV/HistoTAG/lib/python3.6/site-packages/numpy/lib/arraysetops.py", line 314, in _unique1d
    ar = np.asanyarray(ar).flatten()
numpy.core._exceptions.MemoryError: Unable to allocate 189. GiB for an array with shape (50711040000,) and data type float32

我已将代码精简到最重要的部分，但如果需要，我可以发布所有代码。我尝试了这两种场景，不同批次的批次小到 1，但没有改进。

关于我可以尝试训练我的模型的任何想法？

谢谢

标签： pythontensorflowkerasout-of-memorybatch-processing

Answer 1

您可以按照这个官方说明使用类似 U-Net 的架构进行图像分割。这是与您的案例最相关的编码部分。

发电机

class BatchGenerator(keras.utils.Sequence):
    """Helper to iterate over the data (as Numpy arrays)."""

    def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):
        self.batch_size = batch_size
        self.img_size = img_size
        self.input_img_paths = input_img_paths
        self.target_img_paths = target_img_paths

    def __len__(self):
        return len(self.target_img_paths) // self.batch_size

    def __getitem__(self, idx):
        """Returns tuple (input, target) correspond to batch #idx."""
        i = idx * self.batch_size
        batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
        batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
        x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype="float32")
        for j, path in enumerate(batch_input_img_paths):
            img = load_img(path, target_size=self.img_size)
            x[j] = img
        y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype="uint8")
        for j, path in enumerate(batch_target_img_paths):
            img = load_img(path, target_size=self.img_size, color_mode="grayscale")
            y[j] = np.expand_dims(img, 2)
            # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:
            y[j] -= 1
        return x, y

数据集

input_dir = "images/"
target_dir = "annotations/trimaps/"
batch_size = 32

input_img_paths = sorted(
    [
        os.path.join(input_dir, fname)
        for fname in os.listdir(input_dir)
        if fname.endswith(".jpg")
    ]
)
target_img_paths = sorted(
    [
        os.path.join(target_dir, fname)
        for fname in os.listdir(target_dir)
        if fname.endswith(".png") and not fname.startswith(".")
    ]
)

print("Number of samples:", len(input_img_paths))

for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):
    print(input_path, "|", target_path)

数据生成器

# Split our img paths into a training and a validation set
val_samples = 1000
random.Random(1337).shuffle(input_img_paths)
random.Random(1337).shuffle(target_img_paths)
train_input_img_paths = input_img_paths[:-val_samples]
train_target_img_paths = target_img_paths[:-val_samples]
val_input_img_paths = input_img_paths[-val_samples:]
val_target_img_paths = target_img_paths[-val_samples:]

# Instantiate data Sequences for each split
train_gen = BatchGenerator(
    batch_size, img_size, train_input_img_paths, train_target_img_paths
)
val_gen = BatchGenerator(batch_size, img_size, val_input_img_paths,
                         val_target_img_paths)