CODEWITHSUNDEEP
tensorflowtensorflow2.xtf.data.dataset
Luciano Dourado
Luciano Dourado

Reputation: 502

How to access tensor shape inside map function

I need to access image shapes to perform an augmentation pipeline although when accessing through image.shape[0] and image.shape[1] I'm unable to perform the augmentations since it outputs that my tensors have shape None.

Related issues: How to access Tensor shape in .map?

Appreciate if anyone could help.

parsed_dataset = tf.data.TFRecordDataset(filenames=train_records_paths).map(parsing_fn) # Returns [image,label]
augmented_dataset = parsed_dataset.map(augment_pipeline) 
augmented_dataset = augmented_dataset.unbatch()

Mapped function

""" 
    Returns:
      5 Versions of the original image: 4 corner crops + a central crop and the respective labels.
"""
def augment_pipeline(original_image,label):
  central_crop = lambda image: tf.image.central_crop(image,0.5)
  corner_crops = lambda image: tf.image.extract_patches(images=tf.expand_dims(image,0), # Transform image in a batch of single sample
                                                sizes=[1, int(0.5 * image.shape[0]), int(0.5 * image.shape[1]), 1], # 50% of the image's height and width
                                                rates=[1, 1, 1, 1],
                                                strides=[1, int(0.5 * image.shape[0]), int(0.5 * image.shape[1]), 1],
                                                padding="SAME")
  reshaped_patches = tf.reshape(corner_crops(original_image), [-1,int(0.5*original_image.shape[0]),int(0.5*original_image.shape[1]),3])
  images = tf.concat([reshaped_patches,tf.expand_dims(central_crop(original_image),axis=0)],axis=0)
  label = tf.reshape(label,[1,1])
  labels = tf.tile(label,[5,1])
  return images,labels

Upvotes: 0

Views: 1263

Answers (2)

Fu678
Fu678

Reputation: 324

Every Dataset object is iterable. Now the Dataset object can either be in the batched form or the unbatched form. I will tell you how to get their elements shapes in both the cases.

Case 1. Dataset object is in unbatched form.

Method 1. Consuming its elements using iter

it = iter(dataset)
element = next(it)
image,label = element
## element is a tuple

Method 2. using take

element = dataset.take(1)
image,label = element
# element is a tuple

Case 2. When the dataset is batched. Now I assume that the dataset contains (image,label) tuples

Method 1. Using iter

it = iter(dataset)
batch = next(it)
images,labels = batch
## batch is a tuple check it using type(batch)

Method 2. Using take

batch = dataset.take(1)
## Note here each element of the dataset is a batch and each batch contains some number of 
## (image,label) tuples
batch = next(iter(batch))
images,labels = batch
## batch is again a tuple

Upvotes: 0

Luciano Dourado
Luciano Dourado

Reputation: 502

After further research i was able to manage by using py_func as suggested here and tf.shape(image)[0] here.

Code:

""" 
    Returns:
      5 Versions of the original image: 4 corner crops + a central crop and the respective labels.
"""
def augment_pipeline(original_image,label):
  height  = int(tf.shape(original_image)[0].numpy() * 0.5)  # 50% of the image's height and width
  width = int(tf.shape(original_image)[1].numpy() * 0.5)
  central_crop = lambda image: tf.image.central_crop(image,0.5)
  corner_crops = lambda image: tf.image.extract_patches(images=tf.expand_dims(image,0), # Transform image in a batch of single sample
                                                sizes=[1, height, width, 1],
                                                rates=[1, 1, 1, 1],
                                                strides=[1, height, width, 1],
                                                padding="SAME")

                                              .
                                              .
                                              .

Then we use py_func to allow accessing numpy values inside map function:

parsed_dataset = tf.data.TFRecordDataset(filenames=train_records_paths).map(parsing_fn) # Returns [image,label]
augmented_dataset = parsed_dataset.map(lambda image,label: tf.py_function(func=augment_pipeline,
                                                                          inp=[image,label],
                                                                          Tout=[tf.float32,tf.int64])) 
augmented_dataset = augmented_dataset.unbatch()

Upvotes: 2

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