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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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 utils."""
import itertools
import numpy as np
import tensorflow as tf
from deeplab2.model import utils
class UtilsTest(tf.test.TestCase):
def test_resize_logits_graph_mode(self):
@tf.function
def graph_mode_wrapper(*args):
return utils.resize_and_rescale_offsets(*args)
resized_logits = graph_mode_wrapper(tf.ones((2, 33, 33, 2)), [65, 65])
resized_logits_2 = graph_mode_wrapper(tf.ones((2, 33, 33, 2)), [33, 33])
self.assertListEqual(resized_logits.shape.as_list(), [2, 65, 65, 2])
self.assertListEqual(resized_logits_2.shape.as_list(), [2, 33, 33, 2])
def test_resize_logits(self):
offset_logits = tf.convert_to_tensor([[[[2, 2], [2, 1], [2, 0]],
[[1, 2], [1, 1], [1, 0]],
[[0, 2], [0, 1], [0, 0]]]],
dtype=tf.float32)
target_size = [5, 5]
resized_logits = utils.resize_and_rescale_offsets(offset_logits,
target_size)
self.assertListEqual(resized_logits.shape.as_list(), [1, 5, 5, 2])
for i in range(5):
for j in range(5):
np.testing.assert_array_almost_equal(resized_logits.numpy()[0, i, j, :],
[4 - i, 4 - j])
def test_zero_padding(self):
input_tensor = tf.ones(shape=(2, 5, 5, 2))
input_tensor_2 = tf.ones(shape=(5, 5, 2))
padded_tensor = utils.add_zero_padding(input_tensor, kernel_size=5, rank=4)
padded_tensor_2 = utils.add_zero_padding(
input_tensor_2, kernel_size=5, rank=3)
self.assertEqual(tf.reduce_sum(padded_tensor), 100)
self.assertEqual(tf.reduce_sum(padded_tensor_2), 50)
self.assertListEqual(padded_tensor.shape.as_list(), [2, 9, 9, 2])
self.assertListEqual(padded_tensor_2.shape.as_list(), [9, 9, 2])
# Count zero elements.
self.assertEqual(tf.reduce_sum(padded_tensor-1), -224)
self.assertEqual(tf.reduce_sum(padded_tensor_2-1), -112)
def test_resize_function_error(self):
input_tensor = tf.random.uniform(shape=(2, 10, 10, 2))
with self.assertRaises(ValueError):
_ = utils.resize_align_corners(input_tensor, [19, 19],
method='not_a_valid_method')
def test_resize_function_shape(self):
input_tensor = tf.random.uniform(shape=(2, 10, 10, 2))
result_tensor = utils.resize_align_corners(input_tensor, [19, 19])
self.assertListEqual(result_tensor.shape.as_list(), [2, 19, 19, 2])
def test_resize_graph_mode(self):
@tf.function
def graph_mode_wrapper(*args):
return utils.resize_align_corners(*args)
result_tensor = graph_mode_wrapper(tf.ones((2, 33, 33, 2)), [65, 65])
result_tensor_2 = graph_mode_wrapper(tf.ones((2, 33, 33, 2)), [33, 33])
self.assertListEqual(result_tensor.shape.as_list(), [2, 65, 65, 2])
self.assertListEqual(result_tensor_2.shape.as_list(), [2, 33, 33, 2])
def test_resize_function_constant_input(self):
input_tensor = tf.ones(shape=(2, 10, 10, 2))
result_tensor = utils.resize_align_corners(input_tensor, [19, 19])
self.assertTrue(tf.keras.backend.all(result_tensor == 1))
def test_resize_function_invalid_rank(self):
input_tensor = tf.keras.Input(shape=(None, 2))
with self.assertRaisesRegex(
ValueError, 'should have rank of 4'):
_ = utils.resize_align_corners(input_tensor, [19, 19])
def test_resize_function_v1_compatibility(self):
# Test for odd and even input, and output shapes.
input_shapes = [(2, 10, 10, 3), (2, 11, 11, 3)]
target_sizes = [[19, 19], [20, 20]]
methods = ['bilinear', 'nearest']
for shape, target_size, method in itertools.product(input_shapes,
target_sizes, methods):
input_tensor = tf.random.uniform(shape=shape)
result_tensor = utils.resize_align_corners(input_tensor, target_size,
method)
if method == 'bilinear':
expected_tensor = tf.compat.v1.image.resize(
input_tensor,
target_size,
align_corners=True,
method=tf.compat.v1.image.ResizeMethod.BILINEAR)
else:
expected_tensor = tf.compat.v1.image.resize(
input_tensor,
target_size,
align_corners=True,
method=tf.compat.v1.image.ResizeMethod.NEAREST_NEIGHBOR)
np.testing.assert_equal(result_tensor.numpy(), expected_tensor.numpy())
def test_resize_bilinear_v1_compatibility(self):
# Test for odd and even input, and output shapes.
input_shapes = [(2, 10, 10, 3), (2, 11, 11, 3), (1, 11, 11, 64)]
target_sizes = [[19, 19], [20, 20], [10, 10]]
for shape, target_size in itertools.product(input_shapes, target_sizes):
input_tensor = tf.random.uniform(shape=shape)
result_tensor = utils.resize_bilinear(input_tensor, target_size)
expected_tensor = tf.compat.v1.image.resize(
input_tensor,
target_size,
align_corners=True,
method=tf.compat.v1.image.ResizeMethod.BILINEAR)
self.assertAllClose(result_tensor, expected_tensor)
def test_make_divisible(self):
value, divisor, min_value = 17, 2, 8
new_value = utils.make_divisible(value, divisor, min_value)
self.assertAllEqual(new_value, 18)
value, divisor, min_value = 17, 2, 22
new_value = utils.make_divisible(value, divisor, min_value)
self.assertAllEqual(new_value, 22)
def test_transpose_and_reshape_for_attention_operation(self):
images = tf.zeros([2, 8, 11, 2])
output = utils.transpose_and_reshape_for_attention_operation(images)
self.assertEqual(output.get_shape().as_list(), [2, 11, 16])
def test_reshape_and_transpose_for_attention_operation(self):
images = tf.zeros([2, 11, 16])
output = utils.reshape_and_transpose_for_attention_operation(images,
num_heads=8)
self.assertEqual(output.get_shape().as_list(), [2, 8, 11, 2])
def test_safe_setattr_raise_error(self):
layer = tf.keras.layers.Conv2D(1, 1)
with self.assertRaises(ValueError):
utils.safe_setattr(layer, 'filters', 3)
utils.safe_setattr(layer, 'another_conv', tf.keras.layers.Conv2D(1, 1))
with self.assertRaises(ValueError):
utils.safe_setattr(layer, 'another_conv', tf.keras.layers.Conv2D(1, 1))
def test_pad_sequence_with_none(self):
sequence = [1, 2]
output_2 = utils.pad_sequence_with_none(sequence, target_length=2)
self.assertEqual(output_2, [1, 2])
output_3 = utils.pad_sequence_with_none(sequence, target_length=3)
self.assertEqual(output_3, [1, 2, None])
def test_strided_downsample(self):
inputs = tf.zeros([2, 11, 11])
output = utils.strided_downsample(inputs, target_size=[6, 6])
self.assertEqual(output.get_shape().as_list(), [2, 6, 6])
def test_get_stuff_class_ids(self):
# num_thing_stuff_classes does not include `void` class.
num_thing_stuff_classes = 5
thing_class_ids = [3, 4]
void_label_list = [5, 0]
expected_stuff_class_ids_list = [
[0, 1, 2], [1, 2, 5]
]
for void_label, expected_stuff_class_ids in zip(
void_label_list, expected_stuff_class_ids_list):
stuff_class_ids = utils.get_stuff_class_ids(
num_thing_stuff_classes, thing_class_ids, void_label)
np.testing.assert_equal(stuff_class_ids,
expected_stuff_class_ids)
if __name__ == '__main__':
tf.test.main()
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