我在tensorflow数据管道上使用错误

Question

我在深度学习和tensorflow方面很新，然后当我尝试使用tensorflow数据管道上的分叉时，就会发生这个错误(我使用google )：

error: OpenCV(4.1.2) /io/opencv/modules/core/src/matrix.cpp:757: error: (-215:Assertion failed) dims <= 2 && step[0] > 0 in function 'locateROI'

这是我的代码：

• 首先，我使用keras.utils.image_dataset_from_directory加载数据集并进行一些规范化

images= keras.utils.image_dataset_from_directory(
    directory= IMAGE_DIR, 
    batch_size= BATCH_SIZE,
    image_size= RESIZED_IMAGE_SIZE,
    shuffle= True,
    labels='inferred',
    class_names= CLASS_LABELS
)

def normalize(image, label):
    return image/255, label

images= images.map(normalize)

• 然后我试着用这个函数来画图。

def view_image(ds, class_labels):
    image, label = next(iter(ds))
    image = image.numpy()
    label = label.numpy()
    
    fig = plt.figure(figsize=(22, 22))
    for i in range(5):
        try:
            ax = fig.add_subplot(1, 5, i+1, xticks=[], yticks=[])
            ax.imshow(image[i])
            ax.set_title(f"{class_labels[label[i]]}")
        except:
            pass

view_image(images, CLASS_LABELS)

这是

​

有点搞砸了，但至少我知道数据加载是正确的

• 接下来，我从流花实例复制了一个用于增强的映射函数

from albumentations.core.composition import OneOf
transforms = A.Compose([
            # A.RandomResizedCrop(height= RESIZED_IMAGE_SIZE[0], width= RESIZED_IMAGE_SIZE[1], scale= (0.75, 1), p=0.8),
            # A.Rotate(limit=50, p=1),
            # A.ColorJitter(brightness=0.75, contrast=0.4, saturation=0.5, hue= 0, p=1),
            A.GaussianBlur(p=1),
            # A.Flip(p=0.25),
            # A.OneOf([
            #     A.GridDistortion(p=0.5),
            #     A.OpticalDistortion(p=0.5)
            # ], p=1),
        ])

def aug_fn(image, img_size):
    data = {"image":image}
    aug_data = transforms(**data)
    aug_img = aug_data["image"]
    aug_img = tf.cast(aug_img, tf.float32)
    
    return aug_img

def process_data(image, label, img_size):
    aug_img = tf.numpy_function(func=aug_fn, inp=[image, img_size], Tout=tf.float32)
    return aug_img, label

aug_images= images.map(partial(process_data, img_size=[416, 312]), num_parallel_calls=AUTOTUNE).prefetch(AUTOTUNE)

• 然后，当我调用view_image()函数或简单地调用next(iter(aug_images))时，它会引发我前面指定的错误。下面是完整的错误消息：

UnknownError: error: OpenCV(4.1.2) /io/opencv/modules/core/src/matrix.cpp:757: error: (-215:Assertion failed) dims <= 2 && step[0] > 0 in function 'locateROI'

Traceback (most recent call last):

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/script_ops.py", line 271, in __call__
    ret = func(*args)

  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py", line 642, in wrapper
    return func(*args, **kwargs)

  File "<ipython-input-49-d101fe365707>", line 3, in aug_fn
    aug_data = transforms(**data)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/core/composition.py", line 210, in __call__
    data = t(force_apply=force_apply, **data)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/core/transforms_interface.py", line 97, in __call__
    return self.apply_with_params(params, **kwargs)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/core/transforms_interface.py", line 112, in apply_with_params
    res[key] = target_function(arg, **dict(params, **target_dependencies))

  File "/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/transforms.py", line 1963, in apply
    return F.gaussian_blur(image, ksize, sigma=sigma)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/functional.py", line 54, in wrapped_function
    result = func(img, *args, **kwargs)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/functional.py", line 615, in gaussian_blur
    return blur_fn(img)

  File "/usr/local/lib/python3.7/dist-packages/albumentations/augmentations/functional.py", line 189, in __process_fn
    img = process_fn(img, **kwargs)

cv2.error: OpenCV(4.1.2) /io/opencv/modules/core/src/matrix.cpp:757: error: (-215:Assertion failed) dims <= 2 && step[0] > 0 in function 'locateROI'



     [[{{node PyFunc}}]] [Op:IteratorGetNext]

我做了什么，

我试着重新读了几遍tensorflow代码示例，我没有认为我做错了什么(我可能做错了)，然后我决定创建下面的映射函数，它仍然不起作用。

def augmentate_image(image, label):
    aug_img = transforms(image=image)['image']
    aug_img = tf.cast(aug_img, tf.float32)
    return aug_img, label

如果有人能回答我的问题，我将非常感激：

Answer 1

在使用tensorflow docs的所有实现中，它都指出数据集在应用tf.numpy_function后失去了形状。您需要重置数据的形状。你可以试试这段代码。

def set_shapes(img, label, img_shape=<you desired shape in 3d>):
    img.set_shape(img_shape)
    label.set_shape([])
    return img, label

然后将其映射到aug_images；aug_images = aug_images.map(set_shapes,num_parallel_calls=AUTOTUNE).prefetch(AUTOTUNE)