U net Multiclass segmentation image input dataset error

Question

I am trying to do multiclass segmentation with U-net. In the previous trials I tried the binary segmentation and it works. But when I try to do multiclass I am facing this error.

ValueError: 'generator yielded an element of shape (128,192,1) where an element of shape (128,192,5) was expected

This 5 denoted the number of classes. This is how I defined my output layer. output:Tensor("output/sigmoid:0",shape(?,128,192,5),dtype=float32).

I kept a crop size of input_shape:(128,192,1) because of grayscale image
and label_shape:(128,192,5)

Data is loaded in the tensorflow dataset and uses a tf.iterator.
A generator yields data from tf.dataset.

def get_datapoint_generator(self):
  def generator():
   for i in itertools.count(1):
    datapoint_dict=self._get_next_datapoint()
    yield datapoint_dict['image'],datapoint_dict['mask']

The _get_next_datapoint_ function gets next datapoint from ram, and processes cropping and augmentation.

Now, where would have it gone wrong that the it doesnt match with the output shape?

Asked By: ram

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Source

Answer 1

Can you try to use this implementation? I am using this one but it is in Keras

def sparse_crossentropy(y_true, y_pred):
    nb_classes = K.int_shape(y_pred)[-1]
    y_true = K.one_hot(tf.cast(y_true[:, :, 0], dtype=tf.int32), nb_classes + 1)
    return K.categorical_crossentropy(y_true, y_pred)

Answered By: Cenk Bircanoglu

U net Multiclass segmentation image input dataset error

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