如何加入两个 tf.data.Dataset 张量切片？

Question

I have one tensor slice with all image and one tensor with its masking image. how do i combine/join/add them and make it a single tensor dataset tf.data.dataset

# turning them into tensor data
val_img_data = tf.data.Dataset.from_tensor_slices(np.array(all_val_img))
val_mask_data = tf.data.Dataset.from_tensor_slices(np.array(all_val_mask))

then i mapped a function to paths to make them image

val_img_tensor = val_img_data.map(get_image)
val_mask_tensor = val_mask_data.map(get_image)

So now i have two tensors one image and other mask. how do i join them and make it a tensor data combined?

I tried zipping them: it didn't work.

val_data = tf.data.Dataset.from_tensor_slices(zip(val_img_tensor, val_mask_tensor))

Error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/util/structure.py in normalize_element(element, element_signature)
    101         if spec is None:
--> 102           spec = type_spec_from_value(t, use_fallback=False)
    103       except TypeError:

11 frames
TypeError: Could not build a `TypeSpec` for <zip object at 0x7f08f3862050> with type zip

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
    100       dtype = dtypes.as_dtype(dtype).as_datatype_enum
    101   ctx.ensure_initialized()
--> 102   return ops.EagerTensor(value, ctx.device_name, dtype)
    103 
    104 

ValueError: Attempt to convert a value (<zip object at 0x7f08f3862050>) with an unsupported type (<class 'zip'>) to a Tensor.

Answer 1

The comment of Djinn is mostly you need to follow. Here is the end to end answer. Here is how you can build data pipeline for segmentation model training, generally a training paris with both images, masks .

First, get the sample paths.

images = [
        1.jpg,
        2.jpg,
        3.jpg, ...
]

masks = [
       1.png,
       2.png,
       3.png, ...
]

Second, define the hyper-params ie image size, batch size etc. And build the tf.data API input pipelines.

IMAGE_SIZE = 128
BATCH_SIZE = 86

def read_image(image_path, mask=False):
    image = tf.io.read_file(image_path)
    
    if mask:
        image = tf.image.decode_png(image, channels=1)
        image.set_shape([None, None, 1])
        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
        image = tf.cast(image, tf.int32)
    else:
        image = tf.image.decode_png(image, channels=3)
        image.set_shape([None, None, 3])
        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
        image = image / 255.
        
    return image

def load_data(image_list, mask_list):
    image = read_image(image_list)
    mask  = read_image(mask_list, mask=True)
    return image, mask

def data_generator(image_list, mask_list, split='train'):
    dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))
    dataset = dataset.shuffle(8*BATCH_SIZE) if split == 'train' else dataset 
    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
    dataset = dataset.prefetch(tf.data.AUTOTUNE)
    return dataset

Lastly, pass the list of images paths (image + mask) to build data generator.

train_dataset = data_generator(images, masks)
image, mask = next(iter(train_dataset.take(1))) 

print(image.shape, mask.shape)
(86, 128, 128, 3) (86, 128, 128, 1)

Here you can see that, the tf.data.Dataset.from_tensor_slices successfully load the training pairs and return as tuple (no need zipping). Hope it will resolve your problem. I've also answered your other query regarding augmentaiton pipelines, HERE . To add more, check out the following resources, I've shared plenty of semantic segmentaiton modeling approach. It may help.

• Carvana Image Semantic Segmentation: Starter
• Stanford Background Scene Understanding: Starter
• Retinal Vessel Segmentation: Starter

Answer 2

Maybe try tf.data.Dataset.zip :

val_data = tf.data.Dataset.zip((val_img_tensor, val_mask_tensor))