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337 lines
14 KiB
337 lines
14 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for object_detection.utils.shape_utils."""
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import numpy as np
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import tensorflow as tf
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from object_detection.utils import shape_utils
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class UtilTest(tf.test.TestCase):
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def test_pad_tensor_using_integer_input(self):
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t1 = tf.constant([1], dtype=tf.int32)
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pad_t1 = shape_utils.pad_tensor(t1, 2)
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t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
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pad_t2 = shape_utils.pad_tensor(t2, 2)
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self.assertEqual(2, pad_t1.get_shape()[0])
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self.assertEqual(2, pad_t2.get_shape()[0])
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with self.test_session() as sess:
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pad_t1_result, pad_t2_result = sess.run([pad_t1, pad_t2])
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self.assertAllEqual([1, 0], pad_t1_result)
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self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
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def test_pad_tensor_using_tensor_input(self):
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t1 = tf.constant([1], dtype=tf.int32)
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pad_t1 = shape_utils.pad_tensor(t1, tf.constant(2))
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t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
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pad_t2 = shape_utils.pad_tensor(t2, tf.constant(2))
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with self.test_session() as sess:
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pad_t1_result, pad_t2_result = sess.run([pad_t1, pad_t2])
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self.assertAllEqual([1, 0], pad_t1_result)
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self.assertAllClose([[0.1, 0.2], [0, 0]], pad_t2_result)
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def test_clip_tensor_using_integer_input(self):
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t1 = tf.constant([1, 2, 3], dtype=tf.int32)
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clip_t1 = shape_utils.clip_tensor(t1, 2)
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t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
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clip_t2 = shape_utils.clip_tensor(t2, 2)
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self.assertEqual(2, clip_t1.get_shape()[0])
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self.assertEqual(2, clip_t2.get_shape()[0])
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with self.test_session() as sess:
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clip_t1_result, clip_t2_result = sess.run([clip_t1, clip_t2])
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self.assertAllEqual([1, 2], clip_t1_result)
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self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
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def test_clip_tensor_using_tensor_input(self):
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t1 = tf.constant([1, 2, 3], dtype=tf.int32)
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clip_t1 = shape_utils.clip_tensor(t1, tf.constant(2))
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t2 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
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clip_t2 = shape_utils.clip_tensor(t2, tf.constant(2))
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with self.test_session() as sess:
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clip_t1_result, clip_t2_result = sess.run([clip_t1, clip_t2])
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self.assertAllEqual([1, 2], clip_t1_result)
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self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], clip_t2_result)
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def test_pad_or_clip_tensor_using_integer_input(self):
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t1 = tf.constant([1], dtype=tf.int32)
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tt1 = shape_utils.pad_or_clip_tensor(t1, 2)
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t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
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tt2 = shape_utils.pad_or_clip_tensor(t2, 2)
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t3 = tf.constant([1, 2, 3], dtype=tf.int32)
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tt3 = shape_utils.clip_tensor(t3, 2)
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t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
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tt4 = shape_utils.clip_tensor(t4, 2)
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self.assertEqual(2, tt1.get_shape()[0])
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self.assertEqual(2, tt2.get_shape()[0])
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self.assertEqual(2, tt3.get_shape()[0])
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self.assertEqual(2, tt4.get_shape()[0])
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with self.test_session() as sess:
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tt1_result, tt2_result, tt3_result, tt4_result = sess.run(
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[tt1, tt2, tt3, tt4])
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self.assertAllEqual([1, 0], tt1_result)
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self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
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self.assertAllEqual([1, 2], tt3_result)
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self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
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def test_pad_or_clip_tensor_using_tensor_input(self):
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t1 = tf.constant([1], dtype=tf.int32)
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tt1 = shape_utils.pad_or_clip_tensor(t1, tf.constant(2))
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t2 = tf.constant([[0.1, 0.2]], dtype=tf.float32)
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tt2 = shape_utils.pad_or_clip_tensor(t2, tf.constant(2))
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t3 = tf.constant([1, 2, 3], dtype=tf.int32)
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tt3 = shape_utils.clip_tensor(t3, tf.constant(2))
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t4 = tf.constant([[0.1, 0.2], [0.2, 0.4], [0.5, 0.8]], dtype=tf.float32)
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tt4 = shape_utils.clip_tensor(t4, tf.constant(2))
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with self.test_session() as sess:
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tt1_result, tt2_result, tt3_result, tt4_result = sess.run(
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[tt1, tt2, tt3, tt4])
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self.assertAllEqual([1, 0], tt1_result)
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self.assertAllClose([[0.1, 0.2], [0, 0]], tt2_result)
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self.assertAllEqual([1, 2], tt3_result)
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self.assertAllClose([[0.1, 0.2], [0.2, 0.4]], tt4_result)
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def test_combines_static_dynamic_shape(self):
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tensor = tf.placeholder(tf.float32, shape=(None, 2, 3))
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combined_shape = shape_utils.combined_static_and_dynamic_shape(
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tensor)
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self.assertTrue(tf.contrib.framework.is_tensor(combined_shape[0]))
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self.assertListEqual(combined_shape[1:], [2, 3])
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def test_pad_or_clip_nd_tensor(self):
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tensor_placeholder = tf.placeholder(tf.float32, [None, 5, 4, 7])
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output_tensor = shape_utils.pad_or_clip_nd(
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tensor_placeholder, [None, 3, 5, tf.constant(6)])
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self.assertAllEqual(output_tensor.shape.as_list(), [None, 3, 5, None])
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with self.test_session() as sess:
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output_tensor_np = sess.run(
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output_tensor,
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feed_dict={
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tensor_placeholder: np.random.rand(2, 5, 4, 7),
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})
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self.assertAllEqual(output_tensor_np.shape, [2, 3, 5, 6])
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class StaticOrDynamicMapFnTest(tf.test.TestCase):
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def test_with_dynamic_shape(self):
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def fn(input_tensor):
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return tf.reduce_sum(input_tensor)
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input_tensor = tf.placeholder(tf.float32, shape=(None, 2))
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map_fn_output = shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
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op_names = [op.name for op in tf.get_default_graph().get_operations()]
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self.assertTrue(any(['map' == op_name[:3] for op_name in op_names]))
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with self.test_session() as sess:
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result1 = sess.run(
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map_fn_output, feed_dict={
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input_tensor: [[1, 2], [3, 1], [0, 4]]})
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result2 = sess.run(
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map_fn_output, feed_dict={
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input_tensor: [[-1, 1], [0, 9]]})
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self.assertAllEqual(result1, [3, 4, 4])
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self.assertAllEqual(result2, [0, 9])
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def test_with_static_shape(self):
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def fn(input_tensor):
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return tf.reduce_sum(input_tensor)
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input_tensor = tf.constant([[1, 2], [3, 1], [0, 4]], dtype=tf.float32)
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map_fn_output = shape_utils.static_or_dynamic_map_fn(fn, input_tensor)
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op_names = [op.name for op in tf.get_default_graph().get_operations()]
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self.assertTrue(all(['map' != op_name[:3] for op_name in op_names]))
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with self.test_session() as sess:
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result = sess.run(map_fn_output)
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self.assertAllEqual(result, [3, 4, 4])
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def test_with_multiple_dynamic_shapes(self):
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def fn(elems):
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input_tensor, scalar_index_tensor = elems
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return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
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input_tensor = tf.placeholder(tf.float32, shape=(None, 3))
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scalar_index_tensor = tf.placeholder(tf.int32, shape=(None, 1))
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map_fn_output = shape_utils.static_or_dynamic_map_fn(
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fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
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op_names = [op.name for op in tf.get_default_graph().get_operations()]
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self.assertTrue(any(['map' == op_name[:3] for op_name in op_names]))
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with self.test_session() as sess:
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result1 = sess.run(
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map_fn_output, feed_dict={
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input_tensor: [[1, 2, 3], [4, 5, -1], [0, 6, 9]],
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scalar_index_tensor: [[0], [2], [1]],
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})
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result2 = sess.run(
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map_fn_output, feed_dict={
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input_tensor: [[-1, 1, 0], [3, 9, 30]],
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scalar_index_tensor: [[1], [0]]
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})
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self.assertAllEqual(result1, [1, -1, 6])
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self.assertAllEqual(result2, [1, 3])
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def test_with_multiple_static_shapes(self):
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def fn(elems):
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input_tensor, scalar_index_tensor = elems
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return tf.reshape(tf.slice(input_tensor, scalar_index_tensor, [1]), [])
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input_tensor = tf.constant([[1, 2, 3], [4, 5, -1], [0, 6, 9]],
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dtype=tf.float32)
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scalar_index_tensor = tf.constant([[0], [2], [1]], dtype=tf.int32)
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map_fn_output = shape_utils.static_or_dynamic_map_fn(
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fn, [input_tensor, scalar_index_tensor], dtype=tf.float32)
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op_names = [op.name for op in tf.get_default_graph().get_operations()]
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self.assertTrue(all(['map' != op_name[:3] for op_name in op_names]))
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with self.test_session() as sess:
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result = sess.run(map_fn_output)
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self.assertAllEqual(result, [1, -1, 6])
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def test_fails_with_nested_input(self):
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def fn(input_tensor):
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return input_tensor
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input_tensor1 = tf.constant([1])
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input_tensor2 = tf.constant([2])
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with self.assertRaisesRegexp(
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ValueError, '`elems` must be a Tensor or list of Tensors.'):
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shape_utils.static_or_dynamic_map_fn(
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fn, [input_tensor1, [input_tensor2]], dtype=tf.float32)
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class CheckMinImageShapeTest(tf.test.TestCase):
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def test_check_min_image_dim_static_shape(self):
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input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
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_ = shape_utils.check_min_image_dim(33, input_tensor)
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with self.assertRaisesRegexp(
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ValueError, 'image size must be >= 64 in both height and width.'):
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_ = shape_utils.check_min_image_dim(64, input_tensor)
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def test_check_min_image_dim_dynamic_shape(self):
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input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)
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with self.test_session() as sess:
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sess.run(image_tensor,
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feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
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with self.assertRaises(tf.errors.InvalidArgumentError):
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sess.run(image_tensor,
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feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])})
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class AssertShapeEqualTest(tf.test.TestCase):
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def test_unequal_static_shape_raises_exception(self):
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shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
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shape_b = tf.constant(np.zeros([4, 2, 3, 1]))
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with self.assertRaisesRegexp(
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ValueError, 'Unequal shapes'):
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shape_utils.assert_shape_equal(
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shape_utils.combined_static_and_dynamic_shape(shape_a),
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shape_utils.combined_static_and_dynamic_shape(shape_b))
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def test_equal_static_shape_succeeds(self):
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shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
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shape_b = tf.constant(np.zeros([4, 2, 2, 1]))
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with self.test_session() as sess:
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op = shape_utils.assert_shape_equal(
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shape_utils.combined_static_and_dynamic_shape(shape_a),
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shape_utils.combined_static_and_dynamic_shape(shape_b))
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sess.run(op)
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def test_unequal_dynamic_shape_raises_tf_assert(self):
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tensor_a = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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tensor_b = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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op = shape_utils.assert_shape_equal(
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shape_utils.combined_static_and_dynamic_shape(tensor_a),
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shape_utils.combined_static_and_dynamic_shape(tensor_b))
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with self.test_session() as sess:
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with self.assertRaises(tf.errors.InvalidArgumentError):
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sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
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tensor_b: np.zeros([1, 4, 4, 3])})
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def test_equal_dynamic_shape_succeeds(self):
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tensor_a = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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tensor_b = tf.placeholder(tf.float32, shape=[1, None, None, 3])
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op = shape_utils.assert_shape_equal(
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shape_utils.combined_static_and_dynamic_shape(tensor_a),
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shape_utils.combined_static_and_dynamic_shape(tensor_b))
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with self.test_session() as sess:
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sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
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tensor_b: np.zeros([1, 2, 2, 3])})
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def test_unequal_static_shape_along_first_dim_raises_exception(self):
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shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
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shape_b = tf.constant(np.zeros([6, 2, 3, 1]))
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with self.assertRaisesRegexp(
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ValueError, 'Unequal first dimension'):
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shape_utils.assert_shape_equal_along_first_dimension(
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shape_utils.combined_static_and_dynamic_shape(shape_a),
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shape_utils.combined_static_and_dynamic_shape(shape_b))
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def test_equal_static_shape_along_first_dim_succeeds(self):
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shape_a = tf.constant(np.zeros([4, 2, 2, 1]))
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shape_b = tf.constant(np.zeros([4, 7, 2]))
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with self.test_session() as sess:
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op = shape_utils.assert_shape_equal_along_first_dimension(
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shape_utils.combined_static_and_dynamic_shape(shape_a),
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shape_utils.combined_static_and_dynamic_shape(shape_b))
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sess.run(op)
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def test_unequal_dynamic_shape_along_first_dim_raises_tf_assert(self):
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tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
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tensor_b = tf.placeholder(tf.float32, shape=[None, None, 3])
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op = shape_utils.assert_shape_equal_along_first_dimension(
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shape_utils.combined_static_and_dynamic_shape(tensor_a),
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shape_utils.combined_static_and_dynamic_shape(tensor_b))
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with self.test_session() as sess:
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with self.assertRaises(tf.errors.InvalidArgumentError):
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sess.run(op, feed_dict={tensor_a: np.zeros([1, 2, 2, 3]),
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tensor_b: np.zeros([2, 4, 3])})
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def test_equal_dynamic_shape_along_first_dim_succeeds(self):
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tensor_a = tf.placeholder(tf.float32, shape=[None, None, None, 3])
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tensor_b = tf.placeholder(tf.float32, shape=[None])
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op = shape_utils.assert_shape_equal_along_first_dimension(
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shape_utils.combined_static_and_dynamic_shape(tensor_a),
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shape_utils.combined_static_and_dynamic_shape(tensor_b))
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with self.test_session() as sess:
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sess.run(op, feed_dict={tensor_a: np.zeros([5, 2, 2, 3]),
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tensor_b: np.zeros([5])})
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if __name__ == '__main__':
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tf.test.main()
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