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research/object_detection/utils/ops_test.py
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A. Unique TensorFlower
cleanup of deprecated test methods
10 фев 2025, 13:03
10 фев 2025, 13:03
6afb844
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for object_detection.utils.ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import six from six.moves import range import tensorflow.compat.v1 as tf import tf_slim as slim from object_detection.core import standard_fields as fields from object_detection.utils import ops from object_detection.utils import test_case class NormalizedToImageCoordinatesTest(test_case.TestCase): def test_normalized_to_image_coordinates(self): normalized_boxes_np = np.array([[[0.0, 0.0, 1.0, 1.0]], [[0.5, 0.5, 1.0, 1.0]]]) def graph_fn(normalized_boxes): image_shape = tf.convert_to_tensor([1, 4, 4, 3], dtype=tf.int32) absolute_boxes = ops.normalized_to_image_coordinates( normalized_boxes, image_shape, parallel_iterations=2) return absolute_boxes expected_boxes = np.array([[[0, 0, 4, 4]], [[2, 2, 4, 4]]]) absolute_boxes = self.execute(graph_fn, [normalized_boxes_np]) self.assertAllEqual(absolute_boxes, expected_boxes) class ReduceSumTrailingDimensions(test_case.TestCase): def test_reduce_sum_trailing_dimensions(self): def graph_fn(input_tensor): reduced_tensor = ops.reduce_sum_trailing_dimensions(input_tensor, ndims=2) return reduced_tensor reduced_np = self.execute(graph_fn, [np.ones((2, 2, 2), np.float32)]) self.assertAllClose(reduced_np, 2 * np.ones((2, 2), np.float32)) class MeshgridTest(test_case.TestCase): def test_meshgrid_numpy_comparison(self): """Tests meshgrid op with vectors, for which it should match numpy.""" x = np.arange(4) y = np.arange(6) def graph_fn(): xgrid, ygrid = ops.meshgrid(x, y) return xgrid, ygrid exp_xgrid, exp_ygrid = np.meshgrid(x, y) xgrid_output, ygrid_output = self.execute(graph_fn, []) self.assertAllEqual(xgrid_output, exp_xgrid) self.assertAllEqual(ygrid_output, exp_ygrid) def test_meshgrid_multidimensional(self): np.random.seed(18) x = np.random.rand(4, 1, 2).astype(np.float32) y = np.random.rand(2, 3).astype(np.float32) grid_shape = list(y.shape) + list(x.shape) def graph_fn(): xgrid, ygrid = ops.meshgrid(x, y) self.assertEqual(xgrid.get_shape().as_list(), grid_shape) self.assertEqual(ygrid.get_shape().as_list(), grid_shape) return xgrid, ygrid xgrid_output, ygrid_output = self.execute(graph_fn, []) # Check the shape of the output grids self.assertEqual(xgrid_output.shape, tuple(grid_shape)) self.assertEqual(ygrid_output.shape, tuple(grid_shape)) # Check a few elements test_elements = [((3, 0, 0), (1, 2)), ((2, 0, 1), (0, 0)), ((0, 0, 0), (1, 1))] for xind, yind in test_elements: # These are float equality tests, but the meshgrid op should not introduce # rounding. self.assertEqual(xgrid_output[yind + xind], x[xind]) self.assertEqual(ygrid_output[yind + xind], y[yind]) class OpsTestFixedPadding(test_case.TestCase): def test_3x3_kernel(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.fixed_padding(tensor, 3) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape) def test_5x5_kernel(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.fixed_padding(tensor, 5) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape) def test_3x3_atrous_kernel(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.fixed_padding(tensor, 3, 2) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape) class OpsTestPadToMultiple(test_case.TestCase): def test_zero_padding(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 1) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape) def test_no_padding(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape) def test_non_square_padding(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 2) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape) def test_padding(self): def graph_fn(): tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]]) padded_tensor = ops.pad_to_multiple(tensor, 4) return padded_tensor padded_tensor_out = self.execute(graph_fn, []) self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape) class OpsTestPaddedOneHotEncoding(test_case.TestCase): def test_correct_one_hot_tensor_with_no_pad(self): def graph_fn(): indices = tf.constant([1, 2, 3, 5]) one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=0) return one_hot_tensor expected_tensor = np.array([[0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1]], np.float32) out_one_hot_tensor = self.execute(graph_fn, []) self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10, atol=1e-10) def test_correct_one_hot_tensor_with_pad_one(self): def graph_fn(): indices = tf.constant([1, 2, 3, 5]) one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=1) return one_hot_tensor expected_tensor = np.array([[0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1]], np.float32) out_one_hot_tensor = self.execute(graph_fn, []) self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10, atol=1e-10) def test_correct_one_hot_tensor_with_pad_three(self): def graph_fn(): indices = tf.constant([1, 2, 3, 5]) one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=3) return one_hot_tensor expected_tensor = np.array([[0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1]], np.float32) out_one_hot_tensor = self.execute(graph_fn, []) self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10, atol=1e-10) def test_correct_padded_one_hot_tensor_with_empty_indices(self): depth = 6 pad = 2 def graph_fn(): indices = tf.constant([]) one_hot_tensor = ops.padded_one_hot_encoding( indices, depth=depth, left_pad=pad) return one_hot_tensor expected_tensor = np.zeros((0, depth + pad)) out_one_hot_tensor = self.execute(graph_fn, []) self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10, atol=1e-10) def test_return_none_on_zero_depth(self): indices = tf.constant([1, 2, 3, 4, 5]) one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=0, left_pad=2) self.assertEqual(one_hot_tensor, None) def test_raise_value_error_on_rank_two_input(self): indices = tf.constant(1.0, shape=(2, 3)) with self.assertRaises(ValueError): ops.padded_one_hot_encoding(indices, depth=6, left_pad=2) def test_raise_value_error_on_negative_pad(self): indices = tf.constant(1.0, shape=(2, 3)) with self.assertRaises(ValueError): ops.padded_one_hot_encoding(indices, depth=6, left_pad=-1) def test_raise_value_error_on_float_pad(self): indices = tf.constant(1.0, shape=(2, 3)) with self.assertRaises(ValueError): ops.padded_one_hot_encoding(indices, depth=6, left_pad=0.1) def test_raise_value_error_on_float_depth(self): indices = tf.constant(1.0, shape=(2, 3)) with self.assertRaises(ValueError): ops.padded_one_hot_encoding(indices, depth=0.1, left_pad=2) class OpsDenseToSparseBoxesTest(test_case.TestCase): def test_return_all_boxes_when_all_input_boxes_are_valid(self): num_classes = 4 num_valid_boxes = 3 code_size = 4 def graph_fn(dense_location, dense_num_boxes): box_locations, box_classes = ops.dense_to_sparse_boxes( dense_location, dense_num_boxes, num_classes) return box_locations, box_classes dense_location_np = np.random.uniform(size=[num_valid_boxes, code_size]) dense_num_boxes_np = np.array([1, 0, 0, 2], dtype=np.int32) expected_box_locations = dense_location_np expected_box_classses = np.array([0, 3, 3]) # Executing on CPU only since output shape is not constant. box_locations, box_classes = self.execute_cpu( graph_fn, [dense_location_np, dense_num_boxes_np]) self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6, atol=1e-6) self.assertAllEqual(box_classes, expected_box_classses) def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self): num_classes = 4 num_valid_boxes = 3 num_boxes = 10 code_size = 4 def graph_fn(dense_location, dense_num_boxes): box_locations, box_classes = ops.dense_to_sparse_boxes( dense_location, dense_num_boxes, num_classes) return box_locations, box_classes dense_location_np = np.random.uniform(size=[num_boxes, code_size]) dense_num_boxes_np = np.array([1, 0, 0, 2], dtype=np.int32) expected_box_locations = dense_location_np[:num_valid_boxes] expected_box_classses = np.array([0, 3, 3]) # Executing on CPU only since output shape is not constant. box_locations, box_classes = self.execute_cpu( graph_fn, [dense_location_np, dense_num_boxes_np]) self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6, atol=1e-6) self.assertAllEqual(box_classes, expected_box_classses) class OpsTestIndicesToDenseVector(test_case.TestCase): def test_indices_to_dense_vector(self): size = 10000 num_indices = np.random.randint(size) rand_indices = np.random.permutation(np.arange(size))[0:num_indices] expected_output = np.zeros(size, dtype=np.float32) expected_output[rand_indices] = 1. def graph_fn(): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector(tf_rand_indices, size) return indicator output = self.execute(graph_fn, []) self.assertAllEqual(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) def test_indices_to_dense_vector_size_at_inference(self): size = 5000 num_indices = 250 all_indices = np.arange(size) rand_indices = np.random.permutation(all_indices)[0:num_indices] expected_output = np.zeros(size, dtype=np.float32) expected_output[rand_indices] = 1. def graph_fn(tf_all_indices): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector(tf_rand_indices, tf.shape(tf_all_indices)[0]) return indicator output = self.execute(graph_fn, [all_indices]) self.assertAllEqual(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) def test_indices_to_dense_vector_int(self): size = 500 num_indices = 25 rand_indices = np.random.permutation(np.arange(size))[0:num_indices] expected_output = np.zeros(size, dtype=np.int64) expected_output[rand_indices] = 1 def graph_fn(): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector( tf_rand_indices, size, 1, dtype=tf.int64) return indicator output = self.execute(graph_fn, []) self.assertAllEqual(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) def test_indices_to_dense_vector_custom_values(self): size = 100 num_indices = 10 rand_indices = np.random.permutation(np.arange(size))[0:num_indices] indices_value = np.random.rand(1) default_value = np.random.rand(1) expected_output = np.float32(np.ones(size) * default_value) expected_output[rand_indices] = indices_value def graph_fn(): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector( tf_rand_indices, size, indices_value=indices_value, default_value=default_value) return indicator output = self.execute(graph_fn, []) self.assertAllClose(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) def test_indices_to_dense_vector_all_indices_as_input(self): size = 500 num_indices = 500 rand_indices = np.random.permutation(np.arange(size))[0:num_indices] expected_output = np.ones(size, dtype=np.float32) def graph_fn(): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector(tf_rand_indices, size) return indicator output = self.execute(graph_fn, []) self.assertAllEqual(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) def test_indices_to_dense_vector_empty_indices_as_input(self): size = 500 rand_indices = [] expected_output = np.zeros(size, dtype=np.float32) def graph_fn(): tf_rand_indices = tf.constant(rand_indices) indicator = ops.indices_to_dense_vector(tf_rand_indices, size) return indicator output = self.execute(graph_fn, []) self.assertAllEqual(output, expected_output) self.assertEqual(output.dtype, expected_output.dtype) class GroundtruthFilterTest(test_case.TestCase): def test_filter_groundtruth(self): def graph_fn(input_image, input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_label_types, input_confidences, valid_indices): input_tensors = { fields.InputDataFields.image: input_image, fields.InputDataFields.groundtruth_boxes: input_boxes, fields.InputDataFields.groundtruth_classes: input_classes, fields.InputDataFields.groundtruth_is_crowd: input_is_crowd, fields.InputDataFields.groundtruth_area: input_area, fields.InputDataFields.groundtruth_difficult: input_difficult, fields.InputDataFields.groundtruth_label_types: input_label_types, fields.InputDataFields.groundtruth_confidences: input_confidences, } output_tensors = ops.retain_groundtruth(input_tensors, valid_indices) return output_tensors input_image = np.random.rand(224, 224, 3) input_boxes = np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float32) input_classes = np.array([1, 2], dtype=np.int32) input_is_crowd = np.array([False, True], dtype=bool) input_area = np.array([32, 48], dtype=np.float32) input_difficult = np.array([True, False], dtype=bool) input_label_types = np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.bytes_) input_confidences = np.array([0.99, 0.5], dtype=np.float32) valid_indices = np.array([0], dtype=np.int32) # Strings are not supported on TPU. output_tensors = self.execute_cpu( graph_fn, [input_image, input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_label_types, input_confidences, valid_indices] ) expected_tensors = { fields.InputDataFields.image: input_image, fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1], fields.InputDataFields.groundtruth_is_crowd: [False], fields.InputDataFields.groundtruth_area: [32], fields.InputDataFields.groundtruth_difficult: [True], fields.InputDataFields.groundtruth_label_types: [six.b('APPROPRIATE')], fields.InputDataFields.groundtruth_confidences: [0.99], } for key in [fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd, fields.InputDataFields.groundtruth_label_types]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) def test_filter_with_missing_fields(self): input_boxes = np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=float) input_classes = np.array([1, 2], dtype=np.int32) valid_indices = np.array([0], dtype=np.int32) expected_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1] } def graph_fn(input_boxes, input_classes, valid_indices): input_tensors = { fields.InputDataFields.groundtruth_boxes: input_boxes, fields.InputDataFields.groundtruth_classes: input_classes } output_tensors = ops.retain_groundtruth(input_tensors, valid_indices) return output_tensors output_tensors = self.execute(graph_fn, [input_boxes, input_classes, valid_indices]) for key in [fields.InputDataFields.groundtruth_boxes]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) def test_filter_with_empty_fields(self): def graph_fn(input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_confidences, valid_indices): input_tensors = { fields.InputDataFields.groundtruth_boxes: input_boxes, fields.InputDataFields.groundtruth_classes: input_classes, fields.InputDataFields.groundtruth_is_crowd: input_is_crowd, fields.InputDataFields.groundtruth_area: input_area, fields.InputDataFields.groundtruth_difficult: input_difficult, fields.InputDataFields.groundtruth_confidences: input_confidences, } output_tensors = ops.retain_groundtruth(input_tensors, valid_indices) return output_tensors input_boxes = np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=float) input_classes = np.array([1, 2], dtype=np.int32) input_is_crowd = np.array([False, True], dtype=bool) input_area = np.array([], dtype=np.float32) input_difficult = np.array([], dtype=np.float32) input_confidences = np.array([0.99, 0.5], dtype=np.float32) valid_indices = np.array([0], dtype=np.int32) expected_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1], fields.InputDataFields.groundtruth_is_crowd: [False], fields.InputDataFields.groundtruth_area: [], fields.InputDataFields.groundtruth_difficult: [], fields.InputDataFields.groundtruth_confidences: [0.99], } output_tensors = self.execute(graph_fn, [ input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_confidences, valid_indices]) for key in [fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) def test_filter_with_empty_groundtruth_boxes(self): def graph_fn(input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_confidences, valid_indices): input_tensors = { fields.InputDataFields.groundtruth_boxes: input_boxes, fields.InputDataFields.groundtruth_classes: input_classes, fields.InputDataFields.groundtruth_is_crowd: input_is_crowd, fields.InputDataFields.groundtruth_area: input_area, fields.InputDataFields.groundtruth_difficult: input_difficult, fields.InputDataFields.groundtruth_confidences: input_confidences, } output_tensors = ops.retain_groundtruth(input_tensors, valid_indices) return output_tensors input_boxes = np.array([], dtype=float).reshape(0, 4) input_classes = np.array([], dtype=np.int32) input_is_crowd = np.array([], dtype=bool) input_area = np.array([], dtype=np.float32) input_difficult = np.array([], dtype=np.float32) input_confidences = np.array([], dtype=np.float32) valid_indices = np.array([], dtype=np.int32) output_tensors = self.execute(graph_fn, [input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_confidences, valid_indices]) for key in output_tensors: if key == fields.InputDataFields.groundtruth_boxes: self.assertAllEqual([0, 4], output_tensors[key].shape) else: self.assertAllEqual([0], output_tensors[key].shape) class RetainGroundTruthWithPositiveClasses(test_case.TestCase): def test_filter_groundtruth_with_positive_classes(self): def graph_fn(input_image, input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_label_types, input_confidences): input_tensors = { fields.InputDataFields.image: input_image, fields.InputDataFields.groundtruth_boxes: input_boxes, fields.InputDataFields.groundtruth_classes: input_classes, fields.InputDataFields.groundtruth_is_crowd: input_is_crowd, fields.InputDataFields.groundtruth_area: input_area, fields.InputDataFields.groundtruth_difficult: input_difficult, fields.InputDataFields.groundtruth_label_types: input_label_types, fields.InputDataFields.groundtruth_confidences: input_confidences, } output_tensors = ops.retain_groundtruth_with_positive_classes( input_tensors) return output_tensors input_image = np.random.rand(224, 224, 3) input_boxes = np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=float) input_classes = np.array([1, 0], dtype=np.int32) input_is_crowd = np.array([False, True], dtype=bool) input_area = np.array([32, 48], dtype=np.float32) input_difficult = np.array([True, False], dtype=bool) input_label_types = np.array(['APPROPRIATE', 'INCORRECT'], dtype=np.bytes_) input_confidences = np.array([0.99, 0.5], dtype=np.float32) expected_tensors = { fields.InputDataFields.image: input_image, fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1], fields.InputDataFields.groundtruth_is_crowd: [False], fields.InputDataFields.groundtruth_area: [32], fields.InputDataFields.groundtruth_difficult: [True], fields.InputDataFields.groundtruth_label_types: [six.b('APPROPRIATE')], fields.InputDataFields.groundtruth_confidences: [0.99], } # Executing on CPU because string types are not supported on TPU. output_tensors = self.execute_cpu(graph_fn, [input_image, input_boxes, input_classes, input_is_crowd, input_area, input_difficult, input_label_types, input_confidences]) for key in [fields.InputDataFields.image, fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd, fields.InputDataFields.groundtruth_label_types]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) class ReplaceNaNGroundtruthLabelScoresWithOnes(test_case.TestCase): def test_replace_nan_groundtruth_label_scores_with_ones(self): def graph_fn(): label_scores = tf.constant([np.nan, 1.0, np.nan]) output_tensor = ops.replace_nan_groundtruth_label_scores_with_ones( label_scores) return output_tensor expected_tensor = [1.0, 1.0, 1.0] output_tensor = self.execute(graph_fn, []) self.assertAllClose(expected_tensor, output_tensor) def test_input_equals_output_when_no_nans(self): input_label_scores = [0.5, 1.0, 1.0] def graph_fn(): label_scores_tensor = tf.constant(input_label_scores) output_label_scores = ops.replace_nan_groundtruth_label_scores_with_ones( label_scores_tensor) return output_label_scores output_label_scores = self.execute(graph_fn, []) self.assertAllClose(input_label_scores, output_label_scores) class GroundtruthFilterWithCrowdBoxesTest(test_case.TestCase): def test_filter_groundtruth_with_crowd_boxes(self): def graph_fn(): input_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.1, 0.2, 0.6, 0.8], [0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1, 2], fields.InputDataFields.groundtruth_is_crowd: [True, False], fields.InputDataFields.groundtruth_area: [100.0, 238.7], fields.InputDataFields.groundtruth_confidences: [0.5, 0.99], } output_tensors = ops.filter_groundtruth_with_crowd_boxes( input_tensors) return output_tensors expected_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [2], fields.InputDataFields.groundtruth_is_crowd: [False], fields.InputDataFields.groundtruth_area: [238.7], fields.InputDataFields.groundtruth_confidences: [0.99], } output_tensors = self.execute(graph_fn, []) for key in [fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) class GroundtruthFilterWithNanBoxTest(test_case.TestCase): def test_filter_groundtruth_with_nan_box_coordinates(self): def graph_fn(): input_tensors = { fields.InputDataFields.groundtruth_boxes: [[np.nan, np.nan, np.nan, np.nan], [0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [1, 2], fields.InputDataFields.groundtruth_is_crowd: [False, True], fields.InputDataFields.groundtruth_area: [100.0, 238.7], fields.InputDataFields.groundtruth_confidences: [0.5, 0.99], } output_tensors = ops.filter_groundtruth_with_nan_box_coordinates( input_tensors) return output_tensors expected_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [2], fields.InputDataFields.groundtruth_is_crowd: [True], fields.InputDataFields.groundtruth_area: [238.7], fields.InputDataFields.groundtruth_confidences: [0.99], } output_tensors = self.execute(graph_fn, []) for key in [fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) class GroundtruthFilterWithUnrecognizedClassesTest(test_case.TestCase): def test_filter_unrecognized_classes(self): def graph_fn(): input_tensors = { fields.InputDataFields.groundtruth_boxes: [[.3, .3, .5, .7], [0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [-1, 2], fields.InputDataFields.groundtruth_is_crowd: [False, True], fields.InputDataFields.groundtruth_area: [100.0, 238.7], fields.InputDataFields.groundtruth_confidences: [0.5, 0.99], } output_tensors = ops.filter_unrecognized_classes(input_tensors) return output_tensors expected_tensors = { fields.InputDataFields.groundtruth_boxes: [[0.2, 0.4, 0.1, 0.8]], fields.InputDataFields.groundtruth_classes: [2], fields.InputDataFields.groundtruth_is_crowd: [True], fields.InputDataFields.groundtruth_area: [238.7], fields.InputDataFields.groundtruth_confidences: [0.99], } output_tensors = self.execute(graph_fn, []) for key in [fields.InputDataFields.groundtruth_boxes, fields.InputDataFields.groundtruth_area, fields.InputDataFields.groundtruth_confidences]: self.assertAllClose(expected_tensors[key], output_tensors[key]) for key in [fields.InputDataFields.groundtruth_classes, fields.InputDataFields.groundtruth_is_crowd]: self.assertAllEqual(expected_tensors[key], output_tensors[key]) class OpsTestNormalizeToTarget(test_case.TestCase): def test_create_normalize_to_target(self): if self.is_tf2(): self.skipTest('Skipping as variable names not supported in eager mode.') inputs = tf.random_uniform([5, 10, 12, 3]) target_norm_value = 4.0 dim = 3 with self.test_session(): output = ops.normalize_to_target(inputs, target_norm_value, dim) self.assertEqual(output.op.name, 'NormalizeToTarget/mul') var_name = slim.get_variables()[0].name self.assertEqual(var_name, 'NormalizeToTarget/weights:0') def test_invalid_dim(self): inputs = tf.random_uniform([5, 10, 12, 3]) target_norm_value = 4.0 dim = 10 with self.assertRaisesRegex( ValueError, 'dim must be non-negative but smaller than the input rank.'): ops.normalize_to_target(inputs, target_norm_value, dim) def test_invalid_target_norm_values(self): inputs = tf.random_uniform([5, 10, 12, 3]) target_norm_value = [4.0, 4.0] dim = 3 with self.assertRaisesRegex( ValueError, 'target_norm_value must be a float or a list of floats'): ops.normalize_to_target(inputs, target_norm_value, dim) def test_correct_output_shape(self): if self.is_tf2(): self.skipTest('normalize_to_target not supported in eager mode because,' ' it requires creating variables.') inputs = np.random.uniform(size=(5, 10, 12, 3)).astype(np.float32) def graph_fn(inputs): target_norm_value = 4.0 dim = 3 output = ops.normalize_to_target(inputs, target_norm_value, dim) return output # Executing on CPU since creating a variable inside a conditional is not # supported. outputs = self.execute_cpu(graph_fn, [inputs]) self.assertEqual(outputs.shape, inputs.shape) def test_correct_initial_output_values(self): if self.is_tf2(): self.skipTest('normalize_to_target not supported in eager mode because,' ' it requires creating variables.') def graph_fn(): inputs = tf.constant([[[[3, 4], [7, 24]], [[5, -12], [-1, 0]]]], tf.float32) target_norm_value = 10.0 dim = 3 normalized_inputs = ops.normalize_to_target(inputs, target_norm_value, dim) return normalized_inputs expected_output = [[[[30/5.0, 40/5.0], [70/25.0, 240/25.0]], [[50/13.0, -120/13.0], [-10, 0]]]] # Executing on CPU since creating a variable inside a conditional is not # supported. output = self.execute_cpu(graph_fn, []) self.assertAllClose(output, expected_output) def test_multiple_target_norm_values(self): if self.is_tf2(): self.skipTest('normalize_to_target not supported in eager mode because,' ' it requires creating variables.') def graph_fn(): inputs = tf.constant([[[[3, 4], [7, 24]], [[5, -12], [-1, 0]]]], tf.float32) target_norm_value = [10.0, 20.0] dim = 3 normalized_inputs = ops.normalize_to_target(inputs, target_norm_value, dim) return normalized_inputs expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]], [[50/13.0, -240/13.0], [-10, 0]]]] # Executing on CPU since creating a variable inside a conditional is not # supported. output = self.execute_cpu(graph_fn, []) self.assertAllClose(output, expected_output) class OpsTestPositionSensitiveCropRegions(test_case.TestCase): def test_position_sensitive(self): num_spatial_bins = [3, 2] image_shape = [3, 2, 6] # The result for both boxes should be [[1, 2], [3, 4], [5, 6]] # before averaging. expected_output = np.array([3.5, 3.5]).reshape([2, 1, 1, 1]) for crop_size_mult in range(1, 3): crop_size = [3 * crop_size_mult, 2 * crop_size_mult] def graph_fn(): # First channel is 1's, second channel is 2's, etc. image = tf.constant( list(range(1, 3 * 2 + 1)) * 6, dtype=tf.float32, shape=image_shape) boxes = tf.random_uniform((2, 4)) # pylint:disable=cell-var-from-loop ps_crop_and_pool = ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=True) return ps_crop_and_pool output = self.execute(graph_fn, []) self.assertAllClose(output, expected_output) def test_position_sensitive_with_equal_channels(self): num_spatial_bins = [2, 2] image_shape = [3, 3, 4] crop_size = [2, 2] def graph_fn(): image = tf.constant( list(range(1, 3 * 3 + 1)), dtype=tf.float32, shape=[3, 3, 1]) tiled_image = tf.tile(image, [1, 1, image_shape[2]]) boxes = tf.random_uniform((3, 4)) box_ind = tf.constant([0, 0, 0], dtype=tf.int32) # All channels are equal so position-sensitive crop and resize should # work as the usual crop and resize for just one channel. crop = tf.image.crop_and_resize(tf.expand_dims(image, axis=0), boxes, box_ind, crop_size) crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True) ps_crop_and_pool = ops.position_sensitive_crop_regions( tiled_image, boxes, crop_size, num_spatial_bins, global_pool=True) return crop_and_pool, ps_crop_and_pool # Crop and resize op is not supported in TPUs. expected_output, output = self.execute_cpu(graph_fn, []) self.assertAllClose(output, expected_output) def test_raise_value_error_on_num_bins_less_than_one(self): num_spatial_bins = [1, -1] image_shape = [1, 1, 2] crop_size = [2, 2] image = tf.constant(1, dtype=tf.float32, shape=image_shape) boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32) with self.assertRaisesRegex(ValueError, 'num_spatial_bins should be >= 1'): ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=True) def test_raise_value_error_on_non_divisible_crop_size(self): num_spatial_bins = [2, 3] image_shape = [1, 1, 6] crop_size = [3, 2] image = tf.constant(1, dtype=tf.float32, shape=image_shape) boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32) with self.assertRaisesRegex( ValueError, 'crop_size should be divisible by num_spatial_bins'): ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=True) def test_raise_value_error_on_non_divisible_num_channels(self): num_spatial_bins = [2, 2] image_shape = [1, 1, 5] crop_size = [2, 2] def graph_fn(): image = tf.constant(1, dtype=tf.float32, shape=image_shape) boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32) return ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=True) with self.assertRaisesRegex( ValueError, 'Dimension size must be evenly divisible by 4 but is 5'): self.execute(graph_fn, []) def test_position_sensitive_with_global_pool_false(self): num_spatial_bins = [3, 2] image_shape = [3, 2, 6] num_boxes = 2 expected_output = [] # Expected output, when crop_size = [3, 2]. expected_output.append(np.expand_dims( np.tile(np.array([[1, 2], [3, 4], [5, 6]]), (num_boxes, 1, 1)), axis=-1)) # Expected output, when crop_size = [6, 4]. expected_output.append(np.expand_dims( np.tile(np.array([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4], [5, 5, 6, 6], [5, 5, 6, 6]]), (num_boxes, 1, 1)), axis=-1)) for crop_size_mult in range(1, 3): crop_size = [3 * crop_size_mult, 2 * crop_size_mult] # First channel is 1's, second channel is 2's, etc. def graph_fn(): # pylint:disable=cell-var-from-loop image = tf.constant( list(range(1, 3 * 2 + 1)) * 6, dtype=tf.float32, shape=image_shape) boxes = tf.random_uniform((num_boxes, 4)) ps_crop = ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=False) return ps_crop output = self.execute(graph_fn, []) self.assertAllClose(output, expected_output[crop_size_mult - 1]) def test_position_sensitive_with_global_pool_false_and_do_global_pool(self): num_spatial_bins = [3, 2] image_shape = [3, 2, 6] num_boxes = 2 expected_output = [] # Expected output, when crop_size = [3, 2]. expected_output.append(np.mean( np.expand_dims( np.tile(np.array([[1, 2], [3, 4], [5, 6]]), (num_boxes, 1, 1)), axis=-1), axis=(1, 2), keepdims=True)) # Expected output, when crop_size = [6, 4]. expected_output.append(np.mean( np.expand_dims( np.tile(np.array([[1, 1, 2, 2], [1, 1, 2, 2], [3, 3, 4, 4], [3, 3, 4, 4], [5, 5, 6, 6], [5, 5, 6, 6]]), (num_boxes, 1, 1)), axis=-1), axis=(1, 2), keepdims=True)) for crop_size_mult in range(1, 3): crop_size = [3 * crop_size_mult, 2 * crop_size_mult] def graph_fn(): # pylint:disable=cell-var-from-loop # First channel is 1's, second channel is 2's, etc. image = tf.constant( list(range(1, 3 * 2 + 1)) * 6, dtype=tf.float32, shape=image_shape) boxes = tf.random_uniform((num_boxes, 4)) # Perform global_pooling after running the function with # global_pool=False. ps_crop = ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=False) ps_crop_and_pool = tf.reduce_mean( ps_crop, reduction_indices=(1, 2), keepdims=True) return ps_crop_and_pool output = self.execute(graph_fn, []) self.assertAllClose(output, expected_output[crop_size_mult - 1]) def test_raise_value_error_on_non_square_block_size(self): num_spatial_bins = [3, 2] image_shape = [3, 2, 6] crop_size = [6, 2] image = tf.constant(1, dtype=tf.float32, shape=image_shape) boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32) with self.assertRaisesRegex( ValueError, 'Only support square bin crop size for now.'): ops.position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=False) class OpsTestBatchPositionSensitiveCropRegions(test_case.TestCase): def test_position_sensitive_with_single_bin(self): num_spatial_bins = [1, 1] image_shape = [2, 3, 3, 4] crop_size = [2, 2] def graph_fn(): image = tf.random_uniform(image_shape) boxes = tf.random_uniform((2, 3, 4)) box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32) # When a single bin is used, position-sensitive crop and pool should be # the same as non-position sensitive crop and pool. crop = tf.image.crop_and_resize(image, tf.reshape(boxes, [-1, 4]), box_ind, crop_size) crop_and_pool = tf.reduce_mean(crop, [1, 2], keepdims=True) crop_and_pool = tf.reshape(crop_and_pool, [2, 3, 1, 1, 4]) ps_crop_and_pool = ops.batch_position_sensitive_crop_regions( image, boxes, crop_size, num_spatial_bins, global_pool=True) return crop_and_pool, ps_crop_and_pool # Crop and resize is not supported on TPUs. expected_output, output = self.execute_cpu(graph_fn, []) self.assertAllClose(output, expected_output) def test_position_sensitive_with_global_pool_false_and_known_boxes(self): num_spatial_bins = [2, 2] image_shape = [2, 2, 2, 4] crop_size = [2, 2] # box_ind = tf.constant([0, 1], dtype=tf.int32) expected_output = [] # Expected output, when the box containing whole image. expected_output.append( np.reshape(np.array([[4, 7], [10, 13]]), (1, 2, 2, 1)) ) # Expected output, when the box containing only first row. expected_output.append( np.reshape(np.array([[3, 6], [7, 10]]), (1, 2, 2, 1)) ) expected_output = np.stack(expected_output, axis=0) def graph_fn(): images = tf.constant( list(range(1, 2 * 2 * 4 + 1)) * 2, dtype=tf.float32, shape=image_shape) # First box contains whole image, and second box contains only first row. boxes = tf.constant(np.array([[[0., 0., 1., 1.]], [[0., 0., 0.5, 1.]]]), dtype=tf.float32) ps_crop = ops.batch_position_sensitive_crop_regions( images, boxes, crop_size, num_spatial_bins, global_pool=False) return ps_crop output = self.execute(graph_fn, []) self.assertAllEqual(output, expected_output) def test_position_sensitive_with_global_pool_false_and_single_bin(self): num_spatial_bins = [1, 1] image_shape = [2, 3, 3, 4] crop_size = [1, 1] def graph_fn(): images = tf.random_uniform(image_shape) boxes = tf.random_uniform((2, 3, 4)) # box_ind = tf.constant([0, 0, 0, 1, 1, 1], dtype=tf.int32) # Since single_bin is used and crop_size = [1, 1] (i.e., no crop resize), # the outputs are the same whatever the global_pool value is. ps_crop_and_pool = ops.batch_position_sensitive_crop_regions( images, boxes, crop_size, num_spatial_bins, global_pool=True) ps_crop = ops.batch_position_sensitive_crop_regions( images, boxes, crop_size, num_spatial_bins, global_pool=False) return ps_crop_and_pool, ps_crop pooled_output, unpooled_output = self.execute(graph_fn, []) self.assertAllClose(pooled_output, unpooled_output) # The following tests are only executed on CPU because the output # shape is not constant. class ReframeBoxMasksToImageMasksTest(test_case.TestCase, parameterized.TestCase): def test_reframe_image_corners_relative_to_boxes(self): def graph_fn(): return ops.reframe_image_corners_relative_to_boxes( tf.constant([[0.1, 0.2, 0.3, 0.4]])) np_boxes = self.execute_cpu(graph_fn, []) self.assertAllClose(np_boxes, [[-0.5, -1, 4.5, 4.]]) @parameterized.parameters( {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'bilinear'}, {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'nearest'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'bilinear'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'nearest'}, ) def testZeroImageOnEmptyMask(self, mask_dtype, mask_dtype_np, resize_method): np_expected_image_masks = np.array([[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]) def graph_fn(): box_masks = tf.constant([[[0, 0], [0, 0]]], dtype=mask_dtype) boxes = tf.constant([[0.0, 0.0, 1.0, 1.0]], dtype=tf.float32) image_masks = ops.reframe_box_masks_to_image_masks( box_masks, boxes, image_height=4, image_width=4, resize_method=resize_method) return image_masks np_image_masks = self.execute_cpu(graph_fn, []) self.assertEqual(np_image_masks.dtype, mask_dtype_np) self.assertAllClose(np_image_masks, np_expected_image_masks) @parameterized.parameters( {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'bilinear'}, {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'nearest'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'bilinear'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'nearest'}, ) def testZeroBoxMasks(self, mask_dtype, mask_dtype_np, resize_method): def graph_fn(): box_masks = tf.zeros([0, 3, 3], dtype=mask_dtype) boxes = tf.zeros([0, 4], dtype=tf.float32) image_masks = ops.reframe_box_masks_to_image_masks( box_masks, boxes, image_height=4, image_width=4, resize_method=resize_method) return image_masks np_image_masks = self.execute_cpu(graph_fn, []) self.assertEqual(np_image_masks.dtype, mask_dtype_np) self.assertAllEqual(np_image_masks.shape, np.array([0, 4, 4])) def testBoxWithZeroArea(self): def graph_fn(): box_masks = tf.zeros([1, 3, 3], dtype=tf.float32) boxes = tf.constant([[0.1, 0.2, 0.1, 0.7]], dtype=tf.float32) image_masks = ops.reframe_box_masks_to_image_masks(box_masks, boxes, image_height=4, image_width=4) return image_masks np_image_masks = self.execute_cpu(graph_fn, []) self.assertAllEqual(np_image_masks.shape, np.array([1, 4, 4])) @parameterized.parameters( {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'bilinear'}, {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'nearest'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'bilinear'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'nearest'}, ) def testMaskIsCenteredInImageWhenBoxIsCentered(self, mask_dtype, mask_dtype_np, resize_method): def graph_fn(): box_masks = tf.constant([[[4, 4], [4, 4]]], dtype=mask_dtype) boxes = tf.constant([[0.25, 0.25, 0.75, 0.75]], dtype=tf.float32) image_masks = ops.reframe_box_masks_to_image_masks( box_masks, boxes, image_height=4, image_width=4, resize_method=resize_method) return image_masks np_expected_image_masks = np.array([[[0, 0, 0, 0], [0, 4, 4, 0], [0, 4, 4, 0], [0, 0, 0, 0]]], dtype=mask_dtype_np) np_image_masks = self.execute_cpu(graph_fn, []) self.assertEqual(np_image_masks.dtype, mask_dtype_np) self.assertAllClose(np_image_masks, np_expected_image_masks) @parameterized.parameters( {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'bilinear'}, {'mask_dtype': tf.float32, 'mask_dtype_np': np.float32, 'resize_method': 'nearest'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'bilinear'}, {'mask_dtype': tf.uint8, 'mask_dtype_np': np.uint8, 'resize_method': 'nearest'}, ) def testMaskOffCenterRemainsOffCenterInImage(self, mask_dtype, mask_dtype_np, resize_method): def graph_fn(): box_masks = tf.constant([[[1, 0], [0, 1]]], dtype=mask_dtype) boxes = tf.constant([[0.25, 0.5, 0.75, 1.0]], dtype=tf.float32) image_masks = ops.reframe_box_masks_to_image_masks( box_masks, boxes, image_height=4, image_width=4, resize_method=resize_method) return image_masks if mask_dtype == tf.float32 and resize_method == 'bilinear': np_expected_image_masks = np.array([[[0, 0, 0, 0], [0, 0, 0.6111111, 0.16666669], [0, 0, 0.3888889, 0.83333337], [0, 0, 0, 0]]], dtype=np.float32) else: np_expected_image_masks = np.array([[[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]], dtype=mask_dtype_np) np_image_masks = self.execute_cpu(graph_fn, []) self.assertEqual(np_image_masks.dtype, mask_dtype_np) self.assertAllClose(np_image_masks, np_expected_image_masks) class MergeBoxesWithMultipleLabelsTest(test_case.TestCase): def testMergeBoxesWithMultipleLabels(self): def graph_fn(): boxes = tf.constant( [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75], [0.25, 0.25, 0.75, 0.75]], dtype=tf.float32) class_indices = tf.constant([0, 4, 2], dtype=tf.int32) class_confidences = tf.constant([0.8, 0.2, 0.1], dtype=tf.float32) num_classes = 5 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes)) return (merged_boxes, merged_classes, merged_confidences, merged_box_indices) expected_merged_boxes = np.array( [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75]], dtype=np.float32) expected_merged_classes = np.array( [[1, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=np.int32) expected_merged_confidences = np.array( [[0.8, 0, 0.1, 0, 0], [0, 0, 0, 0, 0.2]], dtype=np.float32) expected_merged_box_indices = np.array([0, 1], dtype=np.int32) # Running on CPU only as tf.unique is not supported on TPU. (np_merged_boxes, np_merged_classes, np_merged_confidences, np_merged_box_indices) = self.execute_cpu(graph_fn, []) self.assertAllClose(np_merged_boxes, expected_merged_boxes) self.assertAllClose(np_merged_classes, expected_merged_classes) self.assertAllClose(np_merged_confidences, expected_merged_confidences) self.assertAllClose(np_merged_box_indices, expected_merged_box_indices) def testMergeBoxesWithMultipleLabelsCornerCase(self): def graph_fn(): boxes = tf.constant( [[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 0, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1]], dtype=tf.float32) class_indices = tf.constant([0, 1, 2, 3, 2, 1, 0, 3], dtype=tf.int32) class_confidences = tf.constant([0.1, 0.9, 0.2, 0.8, 0.3, 0.7, 0.4, 0.6], dtype=tf.float32) num_classes = 4 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes)) return (merged_boxes, merged_classes, merged_confidences, merged_box_indices) expected_merged_boxes = np.array( [[0, 0, 1, 1], [0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 1, 1]], dtype=np.float32) expected_merged_classes = np.array( [[1, 0, 0, 1], [1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1]], dtype=np.int32) expected_merged_confidences = np.array( [[0.1, 0, 0, 0.6], [0.4, 0.9, 0, 0], [0, 0.7, 0.2, 0], [0, 0, 0.3, 0.8]], dtype=np.float32) expected_merged_box_indices = np.array([0, 1, 2, 3], dtype=np.int32) # Running on CPU only as tf.unique is not supported on TPU. (np_merged_boxes, np_merged_classes, np_merged_confidences, np_merged_box_indices) = self.execute_cpu(graph_fn, []) self.assertAllClose(np_merged_boxes, expected_merged_boxes) self.assertAllClose(np_merged_classes, expected_merged_classes) self.assertAllClose(np_merged_confidences, expected_merged_confidences) self.assertAllClose(np_merged_box_indices, expected_merged_box_indices) def testMergeBoxesWithEmptyInputs(self): def graph_fn(): boxes = tf.zeros([0, 4], dtype=tf.float32) class_indices = tf.constant([], dtype=tf.int32) class_confidences = tf.constant([], dtype=tf.float32) num_classes = 5 merged_boxes, merged_classes, merged_confidences, merged_box_indices = ( ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes)) return (merged_boxes, merged_classes, merged_confidences, merged_box_indices) # Running on CPU only as tf.unique is not supported on TPU. (np_merged_boxes, np_merged_classes, np_merged_confidences, np_merged_box_indices) = self.execute_cpu(graph_fn, []) self.assertAllEqual(np_merged_boxes.shape, [0, 4]) self.assertAllEqual(np_merged_classes.shape, [0, 5]) self.assertAllEqual(np_merged_confidences.shape, [0, 5]) self.assertAllEqual(np_merged_box_indices.shape, [0]) def testMergeBoxesWithMultipleLabelsUsesInt64(self): if self.is_tf2(): self.skipTest('Getting op names is not supported in eager mode.') boxes = tf.constant( [[0.25, 0.25, 0.75, 0.75], [0.0, 0.0, 0.5, 0.75], [0.25, 0.25, 0.75, 0.75]], dtype=tf.float32) class_indices = tf.constant([0, 4, 2], dtype=tf.int32) class_confidences = tf.constant([0.8, 0.2, 0.1], dtype=tf.float32) num_classes = 5 ops.merge_boxes_with_multiple_labels( boxes, class_indices, class_confidences, num_classes) graph = tf.get_default_graph() def assert_dtype_is_int64(op_name): op = graph.get_operation_by_name(op_name) self.assertEqual(op.get_attr('dtype'), tf.int64) def assert_t_is_int64(op_name): op = graph.get_operation_by_name(op_name) self.assertEqual(op.get_attr('T'), tf.int64) assert_dtype_is_int64('map/TensorArray') assert_dtype_is_int64('map/TensorArray_1') assert_dtype_is_int64('map/while/TensorArrayReadV3') assert_t_is_int64('map/while/TensorArrayWrite/TensorArrayWriteV3') assert_t_is_int64( 'map/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3') assert_dtype_is_int64('map/TensorArrayStack/TensorArrayGatherV3') class NearestNeighborUpsamplingTest(test_case.TestCase): def test_upsampling_with_single_scale(self): def graph_fn(inputs): custom_op_output = ops.nearest_neighbor_upsampling(inputs, scale=2) return custom_op_output inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1]) custom_op_output = self.execute(graph_fn, [inputs]) expected_output = [[[[0], [0], [1], [1]], [[0], [0], [1], [1]], [[2], [2], [3], [3]], [[2], [2], [3], [3]]]] self.assertAllClose(custom_op_output, expected_output) def test_upsampling_with_separate_height_width_scales(self): def graph_fn(inputs): custom_op_output = ops.nearest_neighbor_upsampling(inputs, height_scale=2, width_scale=3) return custom_op_output inputs = np.reshape(np.arange(4).astype(np.float32), [1, 2, 2, 1]) custom_op_output = self.execute(graph_fn, [inputs]) expected_output = [[[[0], [0], [0], [1], [1], [1]], [[0], [0], [0], [1], [1], [1]], [[2], [2], [2], [3], [3], [3]], [[2], [2], [2], [3], [3], [3]]]] self.assertAllClose(custom_op_output, expected_output) class MatmulGatherOnZerothAxis(test_case.TestCase): def test_gather_2d(self): def graph_fn(params, indices): return ops.matmul_gather_on_zeroth_axis(params, indices) params = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [0, 1, 0, 0]], dtype=np.float32) indices = np.array([2, 2, 1], dtype=np.int32) expected_output = np.array([[9, 10, 11, 12], [9, 10, 11, 12], [5, 6, 7, 8]]) gather_output = self.execute(graph_fn, [params, indices]) self.assertAllClose(gather_output, expected_output) def test_gather_3d(self): def graph_fn(params, indices): return ops.matmul_gather_on_zeroth_axis(params, indices) params = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]], [[0, 1], [0, 0]]], dtype=np.float32) indices = np.array([0, 3, 1], dtype=np.int32) expected_output = np.array([[[1, 2], [3, 4]], [[0, 1], [0, 0]], [[5, 6], [7, 8]]]) gather_output = self.execute(graph_fn, [params, indices]) self.assertAllClose(gather_output, expected_output) def test_gather_with_many_indices(self): def graph_fn(params, indices): return ops.matmul_gather_on_zeroth_axis(params, indices) params = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [0, 1, 0, 0]], dtype=np.float32) indices = np.array([0, 0, 0, 0, 0, 0], dtype=np.int32) expected_output = np.array(6*[[1, 2, 3, 4]]) gather_output = self.execute(graph_fn, [params, indices]) self.assertAllClose(gather_output, expected_output) def test_gather_with_dynamic_shape_input(self): def graph_fn(params, indices): return ops.matmul_gather_on_zeroth_axis(params, indices) params = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [0, 1, 0, 0]], dtype=np.float32) indices = np.array([0, 0, 0, 0, 0, 0]) expected_output = np.array(6*[[1, 2, 3, 4]]) gather_output = self.execute(graph_fn, [params, indices]) self.assertAllClose(gather_output, expected_output) class FpnFeatureLevelsTest(test_case.TestCase): def test_correct_fpn_levels(self): image_size = 640 pretraininig_image_size = 224 image_ratio = image_size * 1.0 / pretraininig_image_size boxes = np.array( [ [ [0, 0, 111, 111], # Level 0. [0, 0, 113, 113], # Level 1. [0, 0, 223, 223], # Level 1. [0, 0, 225, 225], # Level 2. [0, 0, 449, 449] # Level 3. ], ], dtype=np.float32) / image_size def graph_fn(boxes): return ops.fpn_feature_levels( num_levels=5, unit_scale_index=2, image_ratio=image_ratio, boxes=boxes) levels = self.execute(graph_fn, [boxes]) self.assertAllEqual([[0, 1, 1, 2, 3]], levels) class TestBfloat16ToFloat32(test_case.TestCase): def test_convert_list(self): var_list = [ tf.constant([1.], dtype=tf.bfloat16), tf.constant([2], dtype=tf.int32) ] casted_var_list = ops.bfloat16_to_float32_nested(var_list) self.assertEqual(casted_var_list[0].dtype, tf.float32) self.assertEqual(casted_var_list[1].dtype, tf.int32) def test_convert_tensor_dict(self): tensor_dict = { 'key1': tf.constant([1.], dtype=tf.bfloat16), 'key2': [ tf.constant([0.5], dtype=tf.bfloat16), tf.constant([7], dtype=tf.int32), ], 'key3': tf.constant([2], dtype=tf.uint8), } tensor_dict = ops.bfloat16_to_float32_nested(tensor_dict) self.assertEqual(tensor_dict['key1'].dtype, tf.float32) self.assertEqual(tensor_dict['key2'][0].dtype, tf.float32) self.assertEqual(tensor_dict['key2'][1].dtype, tf.int32) self.assertEqual(tensor_dict['key3'].dtype, tf.uint8) class TestGatherWithPaddingValues(test_case.TestCase): def test_gather_with_padding_values(self): expected_gathered_tensor = [ [0, 0, 0.2, 0.2], [0, 0, 0, 0], [0, 0, 0.1, 0.1], [0, 0, 0, 0], ] def graph_fn(): indices = tf.constant([1, -1, 0, -1]) input_tensor = tf.constant([[0, 0, 0.1, 0.1], [0, 0, 0.2, 0.2]], dtype=tf.float32) gathered_tensor = ops.gather_with_padding_values( input_tensor, indices=indices, padding_value=tf.zeros_like(input_tensor[0])) self.assertEqual(gathered_tensor.dtype, tf.float32) return gathered_tensor gathered_tensor_np = self.execute(graph_fn, []) self.assertAllClose(expected_gathered_tensor, gathered_tensor_np) class TestGIoU(test_case.TestCase): def test_giou_with_no_overlap(self): expected_giou_tensor = [ 0, -1/3, -3/4, 0, -98/100 ] def graph_fn(): boxes1 = tf.constant([[3, 4, 5, 6], [3, 3, 5, 5], [0, 0, 0, 0], [3, 3, 5, 5], [9, 9, 10, 10]], dtype=tf.float32) boxes2 = tf.constant([[3, 2, 5, 4], [3, 7, 5, 9], [5, 5, 10, 10], [3, 5, 5, 7], [0, 0, 1, 1]], dtype=tf.float32) giou = ops.giou(boxes1, boxes2) self.assertEqual(giou.dtype, tf.float32) return giou giou = self.execute(graph_fn, []) self.assertAllClose(expected_giou_tensor, giou) def test_giou_with_overlaps(self): expected_giou_tensor = [ 1/25, 1/4, 1/3, 1/7 - 2/9 ] def graph_fn(): boxes1 = tf.constant([[2, 1, 7, 6], [2, 2, 4, 4], [2, 2, 4, 4], [2, 2, 4, 4]], dtype=tf.float32) boxes2 = tf.constant([[4, 3, 5, 4], [3, 3, 4, 4], [2, 3, 4, 5], [3, 3, 5, 5]], dtype=tf.float32) giou = ops.giou(boxes1, boxes2) self.assertEqual(giou.dtype, tf.float32) return giou giou = self.execute(graph_fn, []) self.assertAllClose(expected_giou_tensor, giou) def test_giou_with_perfect_overlap(self): expected_giou_tensor = [1] def graph_fn(): boxes1 = tf.constant([[3, 3, 5, 5]], dtype=tf.float32) boxes2 = tf.constant([[3, 3, 5, 5]], dtype=tf.float32) giou = ops.giou(boxes1, boxes2) self.assertEqual(giou.dtype, tf.float32) return giou giou = self.execute(graph_fn, []) self.assertAllClose(expected_giou_tensor, giou) def test_giou_with_zero_area_boxes(self): expected_giou_tensor = [0] def graph_fn(): boxes1 = tf.constant([[1, 1, 1, 1]], dtype=tf.float32) boxes2 = tf.constant([[1, 1, 1, 1]], dtype=tf.float32) giou = ops.giou(boxes1, boxes2) self.assertEqual(giou.dtype, tf.float32) return giou giou = self.execute(graph_fn, []) self.assertAllClose(expected_giou_tensor, giou) def test_giou_different_with_l1_same(self): expected_giou_tensor = [ 2/3, 3/5 ] def graph_fn(): boxes1 = tf.constant([[3, 3, 5, 5], [3, 3, 5, 5]], dtype=tf.float32) boxes2 = tf.constant([[3, 2.5, 5, 5.5], [3, 2.5, 5, 4.5]], dtype=tf.float32) giou = ops.giou(boxes1, boxes2) self.assertEqual(giou.dtype, tf.float32) return giou giou = self.execute(graph_fn, []) self.assertAllClose(expected_giou_tensor, giou) class TestCoordinateConversion(test_case.TestCase): def test_coord_conv(self): expected_box_tensor = [ [0.5, 0.5, 5.5, 5.5], [2, 1, 4, 7], [0, 0, 0, 0] ] def graph_fn(): boxes = tf.constant([[3, 3, 5, 5], [3, 4, 2, 6], [0, 0, 0, 0]], dtype=tf.float32) converted = ops.center_to_corner_coordinate(boxes) self.assertEqual(converted.dtype, tf.float32) return converted converted = self.execute(graph_fn, []) self.assertAllClose(expected_box_tensor, converted) if __name__ == '__main__': tf.test.main()