research/object_detection/utils/ops_test.py
# 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.string_)
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.string_)
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.assertRaisesRegexp(
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.assertRaisesRegexp(
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.assertRaisesRegexp(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.assertRaisesRegexp(
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.assertRaisesRegexp(
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.assertRaisesRegexp(
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()