def test_unequal_static_shape_along_first_dim_raises_exception(self): shape_a = tf.constant(np.zeros([4, 2, 2, 1])) shape_b = tf.constant(np.zeros([6, 2, 3, 1])) with self.assertRaisesRegex(ValueError, 'Unequal first dimension'): shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(shape_a), shape_utils.combined_static_and_dynamic_shape(shape_b))
def graph_fn(): shape_a = tf.constant(np.zeros([4, 2, 2, 1])) shape_b = tf.constant(np.zeros([4, 7, 2])) shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(shape_a), shape_utils.combined_static_and_dynamic_shape(shape_b)) return tf.constant(0)
def test_unequal_static_shape_along_first_dim_raises_exception(self): shape_a = tf.constant(np.zeros([4, 2, 2, 1])) shape_b = tf.constant(np.zeros([6, 2, 3, 1])) with self.assertRaisesRegexp( ValueError, 'Unequal first dimension'): shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(shape_a), shape_utils.combined_static_and_dynamic_shape(shape_b))
def test_equal_static_shape_along_first_dim_succeeds(self): shape_a = tf.constant(np.zeros([4, 2, 2, 1])) shape_b = tf.constant(np.zeros([4, 7, 2])) with self.test_session() as sess: op = shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(shape_a), shape_utils.combined_static_and_dynamic_shape(shape_b)) sess.run(op)
def test_equal_dynamic_shape_along_first_dim_succeeds(self): tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3]) tensor_b = tf.placeholder(tf.float32, shape=[None]) op = shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(tensor_a), shape_utils.combined_static_and_dynamic_shape(tensor_b)) with self.test_session() as sess: sess.run(op, feed_dict={tensor_a: np.zeros([5, 2, 2, 3]), tensor_b: np.zeros([5])})
def test_unequal_dynamic_shape_along_first_dim_raises_tf_assert(self): tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3]) tensor_b = tf.placeholder(tf.float32, shape=[None, None, 3]) op = shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(tensor_a), shape_utils.combined_static_and_dynamic_shape(tensor_b)) with self.test_session() as sess: with self.assertRaises(tf.errors.InvalidArgumentError): sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]), tensor_b: np.zeros([2, 4, 3])})
def merge_boxes_with_multiple_labels(boxes, classes, confidences, num_classes, quantization_bins=10000): """Merges boxes with same coordinates and returns K-hot encoded classes. Args: boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only normalized coordinates are allowed. classes: A tf.int32 tensor with shape [N] holding class indices. The class index starts at 0. confidences: A tf.float32 tensor with shape [N] holding class confidences. num_classes: total number of classes to use for K-hot encoding. quantization_bins: the number of bins used to quantize the box coordinate. Returns: merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes, where N' <= N. class_encodings: A tf.int32 tensor with shape [N', num_classes] holding K-hot encodings for the merged boxes. confidence_encodings: A tf.float32 tensor with shape [N', num_classes] holding encodings of confidences for the merged boxes. merged_box_indices: A tf.int32 tensor with shape [N'] holding original indices of the boxes. """ boxes_shape = tf.shape(boxes) classes_shape = tf.shape(classes) confidences_shape = tf.shape(confidences) box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension( boxes_shape, classes_shape) box_confidence_shape_assert = ( shape_utils.assert_shape_equal_along_first_dimension( boxes_shape, confidences_shape)) box_dimension_assert = tf.assert_equal(boxes_shape[1], 4) box_normalized_assert = shape_utils.assert_box_normalized(boxes) with tf.control_dependencies([ box_class_shape_assert, box_confidence_shape_assert, box_dimension_assert, box_normalized_assert ]): quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1)) ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1) hashcodes = ( ymin + xmin * quantization_bins + ymax * quantization_bins * quantization_bins + xmax * quantization_bins * quantization_bins * quantization_bins) unique_hashcodes, unique_indices = tf.unique(hashcodes) num_boxes = tf.shape(boxes)[0] num_unique_boxes = tf.shape(unique_hashcodes)[0] merged_box_indices = tf.unsorted_segment_min(tf.range(num_boxes), unique_indices, num_unique_boxes) merged_boxes = tf.gather(boxes, merged_box_indices) def map_box_encodings(i): """Produces box K-hot and score encodings for each class index.""" box_mask = tf.equal(unique_indices, i * tf.ones(num_boxes, dtype=tf.int32)) box_mask = tf.reshape(box_mask, [-1]) box_indices = tf.boolean_mask(classes, box_mask) box_confidences = tf.boolean_mask(confidences, box_mask) box_class_encodings = tf.sparse_to_dense(box_indices, [num_classes], 1, validate_indices=False) box_confidence_encodings = tf.sparse_to_dense( box_indices, [num_classes], box_confidences, validate_indices=False) return box_class_encodings, box_confidence_encodings class_encodings, confidence_encodings = tf.map_fn( map_box_encodings, tf.range(num_unique_boxes), back_prop=False, dtype=(tf.int32, tf.float32)) merged_boxes = tf.reshape(merged_boxes, [-1, 4]) class_encodings = tf.reshape(class_encodings, [-1, num_classes]) confidence_encodings = tf.reshape(confidence_encodings, [-1, num_classes]) merged_box_indices = tf.reshape(merged_box_indices, [-1]) return (merged_boxes, class_encodings, confidence_encodings, merged_box_indices)
def merge_boxes_with_multiple_labels(boxes, classes, confidences, num_classes, quantization_bins=10000): """Merges boxes with same coordinates and returns K-hot encoded classes. Args: boxes: A tf.float32 tensor with shape [N, 4] holding N boxes. Only normalized coordinates are allowed. classes: A tf.int32 tensor with shape [N] holding class indices. The class index starts at 0. confidences: A tf.float32 tensor with shape [N] holding class confidences. num_classes: total number of classes to use for K-hot encoding. quantization_bins: the number of bins used to quantize the box coordinate. Returns: merged_boxes: A tf.float32 tensor with shape [N', 4] holding boxes, where N' <= N. class_encodings: A tf.int32 tensor with shape [N', num_classes] holding K-hot encodings for the merged boxes. confidence_encodings: A tf.float32 tensor with shape [N', num_classes] holding encodings of confidences for the merged boxes. merged_box_indices: A tf.int32 tensor with shape [N'] holding original indices of the boxes. """ boxes_shape = tf.shape(boxes) classes_shape = tf.shape(classes) confidences_shape = tf.shape(confidences) box_class_shape_assert = shape_utils.assert_shape_equal_along_first_dimension( boxes_shape, classes_shape) box_confidence_shape_assert = ( shape_utils.assert_shape_equal_along_first_dimension( boxes_shape, confidences_shape)) box_dimension_assert = tf.assert_equal(boxes_shape[1], 4) box_normalized_assert = shape_utils.assert_box_normalized(boxes) with tf.control_dependencies( [box_class_shape_assert, box_confidence_shape_assert, box_dimension_assert, box_normalized_assert]): quantized_boxes = tf.to_int64(boxes * (quantization_bins - 1)) ymin, xmin, ymax, xmax = tf.unstack(quantized_boxes, axis=1) hashcodes = ( ymin + xmin * quantization_bins + ymax * quantization_bins * quantization_bins + xmax * quantization_bins * quantization_bins * quantization_bins) unique_hashcodes, unique_indices = tf.unique(hashcodes) num_boxes = tf.shape(boxes)[0] num_unique_boxes = tf.shape(unique_hashcodes)[0] merged_box_indices = tf.unsorted_segment_min( tf.range(num_boxes), unique_indices, num_unique_boxes) merged_boxes = tf.gather(boxes, merged_box_indices) def map_box_encodings(i): """Produces box K-hot and score encodings for each class index.""" box_mask = tf.equal( unique_indices, i * tf.ones(num_boxes, dtype=tf.int32)) box_mask = tf.reshape(box_mask, [-1]) box_indices = tf.boolean_mask(classes, box_mask) box_confidences = tf.boolean_mask(confidences, box_mask) box_class_encodings = tf.sparse_to_dense( box_indices, [num_classes], 1, validate_indices=False) box_confidence_encodings = tf.sparse_to_dense( box_indices, [num_classes], box_confidences, validate_indices=False) return box_class_encodings, box_confidence_encodings class_encodings, confidence_encodings = tf.map_fn( map_box_encodings, tf.range(num_unique_boxes), back_prop=False, dtype=(tf.int32, tf.float32)) merged_boxes = tf.reshape(merged_boxes, [-1, 4]) class_encodings = tf.reshape(class_encodings, [-1, num_classes]) confidence_encodings = tf.reshape(confidence_encodings, [-1, num_classes]) merged_box_indices = tf.reshape(merged_box_indices, [-1]) return (merged_boxes, class_encodings, confidence_encodings, merged_box_indices)
def graph_fn(tensor_a, tensor_b): shape_utils.assert_shape_equal_along_first_dimension( shape_utils.combined_static_and_dynamic_shape(tensor_a), shape_utils.combined_static_and_dynamic_shape(tensor_b)) return tf.constant(0)