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.
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 i.e 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.