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[英]How can I preprocess a tf.data.Dataset using a provided preprocess_input function that expects a tf.Tensor?
[英]How to preprocess a huge dataset and save it such that I can train the data in Python
我想预处理用于训练模型的巨大图像数据集(600k)。 但是,它占用了太多内存,我一直在寻找解决方案,但在这里没有一个适合我的问题。 这是我的代码的一部分。 我还是深度学习的新手,我认为我在预处理数据方面做得很差。 如果有人知道如何解决这个内存问题,将不胜感激。
# Read the CSV File
data_frame = pd.read_csv("D:\\Downloads\\ndsc-beginner\\train.csv")
#Load the image
def load_image(img_path, target_size=(256, 256)):
#Check if the img_path has .jpg behind the name
if img_path[-4:] != '.jpg':
# Load the image
img = load_img(img_path+'.jpg',
target_size=target_size, grayscale=True)
else:
#Load the image
img = load_img(img_path, target_size=target_size, grayscale=True)
# Convert to a numpy array
return img_to_array(img)
IMG_SIZE = 256
image_arr = []
# Get the category column values
category_id = data_frame['Category']
# Change the category to one-hot - has 50 categories
dummy_cat_id = keras.utils.np_utils.to_categorical(category_id, 50)
# Get the image paths column values
path_list = data_frame.iloc[1:, -1]
# Batch generator
def batch_gen(data, batch_size):
for i in range(0, len(data), batch_size):
yield data[i:i+batch_size]
# Append the numpy array(img) and category label into an array.
def extract_data(data_frame):
total_count = len(path_list)
batch_size = 1000
index = 0
for path in batch_gen(path_list,batch_size):
for mini_path in path:
image_arr.append([load_image(mini_path), dummy_cat_id[index]])
print(index)
index+= 1
#extract_data(data_frame)
random.shuffle(image_arr)
# Features and Labels for training data
trainImages = np.array([i[0] for i in image_arr]
).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
trainLabels = np.array([i[1] for i in image_arr])
trainImages = trainImages.astype('float32')
trainImages /= 255.0
我看到在预处理中,您只是将图像灰度化并对其进行标准化。 如果您使用的是 Keras,您可以使用以下内容进行标准化并将您的图像转换为灰度确保您提供包含图像所在的类文件夹的路径。 如果需要,您可以将课程模式更改为分类模式
train_datagen = ImageDataGenerator(rescale=1./255)
train_gen = train_datagen.flow_from_directory(
f'{readPath}/training/',
target_size=(100,100),
color_mode='grayscale',
batch_size=32,
classes=['cat','dog'],
class_mode='binary'
)
要训练,您可以使用 model.fit_generator() 函数
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