def _PadLabels3d(logits, labels):
"""Pads or slices 3-d labels to match logits.
Covers the case of 2-d softmax output, when labels is [batch, height, width]
and logits is [batch, height, width, onehot]
Args:
logits: 4-d Pre-softmax fully-connected output.
labels: 3-d, but not necessarily matching in size.
Returns:
labels: Resized by padding or clipping to match logits.
"""
logits_shape = shapes.tensor_shape(logits)
labels_shape = shapes.tensor_shape(labels)
labels = tf.reshape(labels, [-1, labels_shape[2]])
labels = _PadLabels2d(logits_shape[2], labels)
labels = tf.reshape(labels, [labels_shape[0], -1])
labels = _PadLabels2d(logits_shape[1] * logits_shape[2], labels)
return tf.reshape(labels, [labels_shape[0], logits_shape[1], logits_shape[2]])
def _PadLabels3d(logits, labels):
"""Pads or slices 3-d labels to match logits.
Covers the case of 2-d softmax output, when labels is [batch, height, width]
and logits is [batch, height, width, onehot]
Args:
logits: 4-d Pre-softmax fully-connected output.
labels: 3-d, but not necessarily matching in size.
Returns:
labels: Resized by padding or clipping to match logits.
"""
logits_shape = shapes.tensor_shape(logits)
labels_shape = shapes.tensor_shape(labels)
labels = tf.reshape(labels, [-1, labels_shape[2]])
labels = _PadLabels2d(logits_shape[2], labels)
labels = tf.reshape(labels, [labels_shape[0], -1])
labels = _PadLabels2d(logits_shape[1] * logits_shape[2], labels)
return tf.reshape(labels, [labels_shape[0], logits_shape[1], logits_shape[2]])
