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1479 lines
62 KiB
1479 lines
62 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.ops."""
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import numpy as np
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import tensorflow as tf
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from object_detection.core import standard_fields as fields
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from object_detection.utils import ops
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from object_detection.utils import test_case
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slim = tf.contrib.slim
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class NormalizedToImageCoordinatesTest(tf.test.TestCase):
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def test_normalized_to_image_coordinates(self):
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normalized_boxes = tf.placeholder(tf.float32, shape=(None, 1, 4))
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normalized_boxes_np = np.array([[[0.0, 0.0, 1.0, 1.0]],
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[[0.5, 0.5, 1.0, 1.0]]])
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image_shape = tf.convert_to_tensor([1, 4, 4, 3], dtype=tf.int32)
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absolute_boxes = ops.normalized_to_image_coordinates(normalized_boxes,
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image_shape,
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parallel_iterations=2)
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expected_boxes = np.array([[[0, 0, 4, 4]],
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[[2, 2, 4, 4]]])
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with self.test_session() as sess:
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absolute_boxes = sess.run(absolute_boxes,
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feed_dict={normalized_boxes:
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normalized_boxes_np})
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self.assertAllEqual(absolute_boxes, expected_boxes)
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class ReduceSumTrailingDimensions(tf.test.TestCase):
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def test_reduce_sum_trailing_dimensions(self):
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input_tensor = tf.placeholder(tf.float32, shape=[None, None, None])
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reduced_tensor = ops.reduce_sum_trailing_dimensions(input_tensor, ndims=2)
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with self.test_session() as sess:
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reduced_np = sess.run(reduced_tensor,
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feed_dict={input_tensor: np.ones((2, 2, 2),
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np.float32)})
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self.assertAllClose(reduced_np, 2 * np.ones((2, 2), np.float32))
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class MeshgridTest(tf.test.TestCase):
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def test_meshgrid_numpy_comparison(self):
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"""Tests meshgrid op with vectors, for which it should match numpy."""
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x = np.arange(4)
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y = np.arange(6)
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exp_xgrid, exp_ygrid = np.meshgrid(x, y)
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xgrid, ygrid = ops.meshgrid(x, y)
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with self.test_session() as sess:
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xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
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self.assertAllEqual(xgrid_output, exp_xgrid)
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self.assertAllEqual(ygrid_output, exp_ygrid)
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def test_meshgrid_multidimensional(self):
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np.random.seed(18)
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x = np.random.rand(4, 1, 2).astype(np.float32)
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y = np.random.rand(2, 3).astype(np.float32)
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xgrid, ygrid = ops.meshgrid(x, y)
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grid_shape = list(y.shape) + list(x.shape)
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self.assertEqual(xgrid.get_shape().as_list(), grid_shape)
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self.assertEqual(ygrid.get_shape().as_list(), grid_shape)
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with self.test_session() as sess:
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xgrid_output, ygrid_output = sess.run([xgrid, ygrid])
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# Check the shape of the output grids
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self.assertEqual(xgrid_output.shape, tuple(grid_shape))
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self.assertEqual(ygrid_output.shape, tuple(grid_shape))
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# Check a few elements
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test_elements = [((3, 0, 0), (1, 2)),
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((2, 0, 1), (0, 0)),
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((0, 0, 0), (1, 1))]
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for xind, yind in test_elements:
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# These are float equality tests, but the meshgrid op should not introduce
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# rounding.
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self.assertEqual(xgrid_output[yind + xind], x[xind])
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self.assertEqual(ygrid_output[yind + xind], y[yind])
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class OpsTestFixedPadding(tf.test.TestCase):
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def test_3x3_kernel(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.fixed_padding(tensor, 3)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
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def test_5x5_kernel(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.fixed_padding(tensor, 5)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
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def test_3x3_atrous_kernel(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.fixed_padding(tensor, 3, 2)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 6, 6, 1), padded_tensor_out.shape)
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class OpsTestPadToMultiple(tf.test.TestCase):
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def test_zero_padding(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.pad_to_multiple(tensor, 1)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
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def test_no_padding(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.pad_to_multiple(tensor, 2)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
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def test_non_square_padding(self):
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tensor = tf.constant([[[[0.], [0.]]]])
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padded_tensor = ops.pad_to_multiple(tensor, 2)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 2, 2, 1), padded_tensor_out.shape)
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def test_padding(self):
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tensor = tf.constant([[[[0.], [0.]], [[0.], [0.]]]])
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padded_tensor = ops.pad_to_multiple(tensor, 4)
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with self.test_session() as sess:
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padded_tensor_out = sess.run(padded_tensor)
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self.assertEqual((1, 4, 4, 1), padded_tensor_out.shape)
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class OpsTestPaddedOneHotEncoding(tf.test.TestCase):
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def test_correct_one_hot_tensor_with_no_pad(self):
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indices = tf.constant([1, 2, 3, 5])
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one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=0)
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expected_tensor = np.array([[0, 1, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 1]], np.float32)
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with self.test_session() as sess:
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out_one_hot_tensor = sess.run(one_hot_tensor)
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self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
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atol=1e-10)
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def test_correct_one_hot_tensor_with_pad_one(self):
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indices = tf.constant([1, 2, 3, 5])
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one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=1)
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expected_tensor = np.array([[0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 1]], np.float32)
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with self.test_session() as sess:
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out_one_hot_tensor = sess.run(one_hot_tensor)
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self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
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atol=1e-10)
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def test_correct_one_hot_tensor_with_pad_three(self):
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indices = tf.constant([1, 2, 3, 5])
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one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=6, left_pad=3)
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expected_tensor = np.array([[0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 1]], np.float32)
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with self.test_session() as sess:
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out_one_hot_tensor = sess.run(one_hot_tensor)
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self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
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atol=1e-10)
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def test_correct_padded_one_hot_tensor_with_empty_indices(self):
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depth = 6
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pad = 2
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indices = tf.constant([])
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one_hot_tensor = ops.padded_one_hot_encoding(
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indices, depth=depth, left_pad=pad)
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expected_tensor = np.zeros((0, depth + pad))
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with self.test_session() as sess:
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out_one_hot_tensor = sess.run(one_hot_tensor)
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self.assertAllClose(out_one_hot_tensor, expected_tensor, rtol=1e-10,
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atol=1e-10)
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def test_return_none_on_zero_depth(self):
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indices = tf.constant([1, 2, 3, 4, 5])
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one_hot_tensor = ops.padded_one_hot_encoding(indices, depth=0, left_pad=2)
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self.assertEqual(one_hot_tensor, None)
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def test_raise_value_error_on_rank_two_input(self):
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indices = tf.constant(1.0, shape=(2, 3))
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with self.assertRaises(ValueError):
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ops.padded_one_hot_encoding(indices, depth=6, left_pad=2)
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def test_raise_value_error_on_negative_pad(self):
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indices = tf.constant(1.0, shape=(2, 3))
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with self.assertRaises(ValueError):
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ops.padded_one_hot_encoding(indices, depth=6, left_pad=-1)
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def test_raise_value_error_on_float_pad(self):
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indices = tf.constant(1.0, shape=(2, 3))
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with self.assertRaises(ValueError):
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ops.padded_one_hot_encoding(indices, depth=6, left_pad=0.1)
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def test_raise_value_error_on_float_depth(self):
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indices = tf.constant(1.0, shape=(2, 3))
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with self.assertRaises(ValueError):
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ops.padded_one_hot_encoding(indices, depth=0.1, left_pad=2)
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class OpsDenseToSparseBoxesTest(tf.test.TestCase):
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def test_return_all_boxes_when_all_input_boxes_are_valid(self):
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num_classes = 4
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num_valid_boxes = 3
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code_size = 4
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dense_location_placeholder = tf.placeholder(tf.float32,
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shape=(num_valid_boxes,
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code_size))
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dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
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box_locations, box_classes = ops.dense_to_sparse_boxes(
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dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
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feed_dict = {dense_location_placeholder: np.random.uniform(
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size=[num_valid_boxes, code_size]),
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dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
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dtype=np.int32)}
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expected_box_locations = feed_dict[dense_location_placeholder]
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expected_box_classses = np.array([0, 3, 3])
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with self.test_session() as sess:
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box_locations, box_classes = sess.run([box_locations, box_classes],
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feed_dict=feed_dict)
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self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
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atol=1e-6)
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self.assertAllEqual(box_classes, expected_box_classses)
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def test_return_only_valid_boxes_when_input_contains_invalid_boxes(self):
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num_classes = 4
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num_valid_boxes = 3
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num_boxes = 10
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code_size = 4
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dense_location_placeholder = tf.placeholder(tf.float32, shape=(num_boxes,
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code_size))
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dense_num_boxes_placeholder = tf.placeholder(tf.int32, shape=(num_classes))
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box_locations, box_classes = ops.dense_to_sparse_boxes(
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dense_location_placeholder, dense_num_boxes_placeholder, num_classes)
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feed_dict = {dense_location_placeholder: np.random.uniform(
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size=[num_boxes, code_size]),
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dense_num_boxes_placeholder: np.array([1, 0, 0, 2],
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dtype=np.int32)}
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expected_box_locations = (feed_dict[dense_location_placeholder]
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[:num_valid_boxes])
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expected_box_classses = np.array([0, 3, 3])
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with self.test_session() as sess:
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box_locations, box_classes = sess.run([box_locations, box_classes],
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feed_dict=feed_dict)
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self.assertAllClose(box_locations, expected_box_locations, rtol=1e-6,
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atol=1e-6)
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self.assertAllEqual(box_classes, expected_box_classses)
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class OpsTestIndicesToDenseVector(tf.test.TestCase):
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def test_indices_to_dense_vector(self):
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size = 10000
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num_indices = np.random.randint(size)
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rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
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expected_output = np.zeros(size, dtype=np.float32)
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expected_output[rand_indices] = 1.
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
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with self.test_session() as sess:
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output = sess.run(indicator)
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self.assertAllEqual(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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def test_indices_to_dense_vector_size_at_inference(self):
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size = 5000
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num_indices = 250
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all_indices = np.arange(size)
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rand_indices = np.random.permutation(all_indices)[0:num_indices]
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expected_output = np.zeros(size, dtype=np.float32)
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expected_output[rand_indices] = 1.
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tf_all_indices = tf.placeholder(tf.int32)
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(tf_rand_indices,
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tf.shape(tf_all_indices)[0])
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feed_dict = {tf_all_indices: all_indices}
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with self.test_session() as sess:
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output = sess.run(indicator, feed_dict=feed_dict)
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self.assertAllEqual(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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def test_indices_to_dense_vector_int(self):
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size = 500
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num_indices = 25
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rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
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expected_output = np.zeros(size, dtype=np.int64)
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expected_output[rand_indices] = 1
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(
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tf_rand_indices, size, 1, dtype=tf.int64)
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with self.test_session() as sess:
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output = sess.run(indicator)
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self.assertAllEqual(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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def test_indices_to_dense_vector_custom_values(self):
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size = 100
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num_indices = 10
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rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
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indices_value = np.random.rand(1)
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default_value = np.random.rand(1)
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expected_output = np.float32(np.ones(size) * default_value)
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expected_output[rand_indices] = indices_value
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(
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tf_rand_indices,
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size,
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indices_value=indices_value,
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default_value=default_value)
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with self.test_session() as sess:
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output = sess.run(indicator)
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self.assertAllClose(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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def test_indices_to_dense_vector_all_indices_as_input(self):
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size = 500
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num_indices = 500
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rand_indices = np.random.permutation(np.arange(size))[0:num_indices]
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expected_output = np.ones(size, dtype=np.float32)
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
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with self.test_session() as sess:
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output = sess.run(indicator)
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self.assertAllEqual(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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def test_indices_to_dense_vector_empty_indices_as_input(self):
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size = 500
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rand_indices = []
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expected_output = np.zeros(size, dtype=np.float32)
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tf_rand_indices = tf.constant(rand_indices)
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indicator = ops.indices_to_dense_vector(tf_rand_indices, size)
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with self.test_session() as sess:
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output = sess.run(indicator)
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self.assertAllEqual(output, expected_output)
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self.assertEqual(output.dtype, expected_output.dtype)
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class GroundtruthFilterTest(tf.test.TestCase):
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def test_filter_groundtruth(self):
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input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
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input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
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input_classes = tf.placeholder(tf.int32, shape=(None,))
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input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
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input_area = tf.placeholder(tf.float32, shape=(None,))
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input_difficult = tf.placeholder(tf.float32, shape=(None,))
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input_label_types = tf.placeholder(tf.string, shape=(None,))
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input_confidences = tf.placeholder(tf.float32, shape=(None,))
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valid_indices = tf.placeholder(tf.int32, shape=(None,))
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input_tensors = {
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fields.InputDataFields.image: input_image,
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fields.InputDataFields.groundtruth_boxes: input_boxes,
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fields.InputDataFields.groundtruth_classes: input_classes,
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fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
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fields.InputDataFields.groundtruth_area: input_area,
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fields.InputDataFields.groundtruth_difficult: input_difficult,
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fields.InputDataFields.groundtruth_label_types: input_label_types,
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fields.InputDataFields.groundtruth_confidences: input_confidences,
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}
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output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
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image_tensor = np.random.rand(224, 224, 3)
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feed_dict = {
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input_image: image_tensor,
|
|
input_boxes:
|
|
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
|
|
input_classes: np.array([1, 2], dtype=np.int32),
|
|
input_is_crowd: np.array([False, True], dtype=np.bool),
|
|
input_area: np.array([32, 48], dtype=np.float32),
|
|
input_difficult: np.array([True, False], dtype=np.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),
|
|
}
|
|
expected_tensors = {
|
|
fields.InputDataFields.image: image_tensor,
|
|
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: ['APPROPRIATE'],
|
|
fields.InputDataFields.groundtruth_confidences: [0.99],
|
|
}
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
|
|
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 = tf.placeholder(tf.float32, shape=(None, 4))
|
|
input_classes = tf.placeholder(tf.int32, shape=(None,))
|
|
input_tensors = {
|
|
fields.InputDataFields.groundtruth_boxes: input_boxes,
|
|
fields.InputDataFields.groundtruth_classes: input_classes
|
|
}
|
|
valid_indices = tf.placeholder(tf.int32, shape=(None,))
|
|
|
|
feed_dict = {
|
|
input_boxes:
|
|
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.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]
|
|
}
|
|
|
|
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
|
|
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):
|
|
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
|
|
input_classes = tf.placeholder(tf.int32, shape=(None,))
|
|
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
|
|
input_area = tf.placeholder(tf.float32, shape=(None,))
|
|
input_difficult = tf.placeholder(tf.float32, shape=(None,))
|
|
input_confidences = tf.placeholder(tf.float32, shape=(None,))
|
|
valid_indices = tf.placeholder(tf.int32, shape=(None,))
|
|
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)
|
|
|
|
feed_dict = {
|
|
input_boxes:
|
|
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
|
|
input_classes: np.array([1, 2], dtype=np.int32),
|
|
input_is_crowd: np.array([False, True], dtype=np.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],
|
|
}
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
|
|
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):
|
|
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
|
|
input_classes = tf.placeholder(tf.int32, shape=(None,))
|
|
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
|
|
input_area = tf.placeholder(tf.float32, shape=(None,))
|
|
input_difficult = tf.placeholder(tf.float32, shape=(None,))
|
|
input_confidences = tf.placeholder(tf.float32, shape=(None,))
|
|
valid_indices = tf.placeholder(tf.int32, shape=(None,))
|
|
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)
|
|
|
|
feed_dict = {
|
|
input_boxes: np.array([], dtype=np.float).reshape(0, 4),
|
|
input_classes: np.array([], dtype=np.int32),
|
|
input_is_crowd: np.array([], dtype=np.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),
|
|
}
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
|
|
for key in input_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(tf.test.TestCase):
|
|
|
|
def test_filter_groundtruth_with_positive_classes(self):
|
|
input_image = tf.placeholder(tf.float32, shape=(None, None, 3))
|
|
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
|
|
input_classes = tf.placeholder(tf.int32, shape=(None,))
|
|
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
|
|
input_area = tf.placeholder(tf.float32, shape=(None,))
|
|
input_difficult = tf.placeholder(tf.float32, shape=(None,))
|
|
input_label_types = tf.placeholder(tf.string, shape=(None,))
|
|
input_confidences = tf.placeholder(tf.float32, shape=(None,))
|
|
valid_indices = tf.placeholder(tf.int32, shape=(None,))
|
|
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)
|
|
|
|
image_tensor = np.random.rand(224, 224, 3)
|
|
feed_dict = {
|
|
input_image: image_tensor,
|
|
input_boxes:
|
|
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
|
|
input_classes: np.array([1, 0], dtype=np.int32),
|
|
input_is_crowd: np.array([False, True], dtype=np.bool),
|
|
input_area: np.array([32, 48], dtype=np.float32),
|
|
input_difficult: np.array([True, False], dtype=np.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),
|
|
}
|
|
expected_tensors = {
|
|
fields.InputDataFields.image: image_tensor,
|
|
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: ['APPROPRIATE'],
|
|
fields.InputDataFields.groundtruth_confidences: [0.99],
|
|
}
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
|
|
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(tf.test.TestCase):
|
|
|
|
def test_replace_nan_groundtruth_label_scores_with_ones(self):
|
|
label_scores = tf.constant([np.nan, 1.0, np.nan])
|
|
output_tensor = ops.replace_nan_groundtruth_label_scores_with_ones(
|
|
label_scores)
|
|
expected_tensor = [1.0, 1.0, 1.0]
|
|
with self.test_session():
|
|
output_tensor = output_tensor.eval()
|
|
self.assertAllClose(expected_tensor, output_tensor)
|
|
|
|
def test_input_equals_output_when_no_nans(self):
|
|
input_label_scores = [0.5, 1.0, 1.0]
|
|
label_scores_tensor = tf.constant(input_label_scores)
|
|
output_label_scores = ops.replace_nan_groundtruth_label_scores_with_ones(
|
|
label_scores_tensor)
|
|
with self.test_session():
|
|
output_label_scores = output_label_scores.eval()
|
|
self.assertAllClose(input_label_scores, output_label_scores)
|
|
|
|
|
|
class GroundtruthFilterWithCrowdBoxesTest(tf.test.TestCase):
|
|
|
|
def test_filter_groundtruth_with_crowd_boxes(self):
|
|
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],
|
|
}
|
|
|
|
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 = ops.filter_groundtruth_with_crowd_boxes(
|
|
input_tensors)
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors)
|
|
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(tf.test.TestCase):
|
|
|
|
def test_filter_groundtruth_with_nan_box_coordinates(self):
|
|
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],
|
|
}
|
|
|
|
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 = ops.filter_groundtruth_with_nan_box_coordinates(
|
|
input_tensors)
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors)
|
|
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(tf.test.TestCase):
|
|
|
|
def test_filter_unrecognized_classes(self):
|
|
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],
|
|
}
|
|
|
|
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 = ops.filter_unrecognized_classes(input_tensors)
|
|
with self.test_session() as sess:
|
|
output_tensors = sess.run(output_tensors)
|
|
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(tf.test.TestCase):
|
|
|
|
def test_create_normalize_to_target(self):
|
|
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 = tf.contrib.framework.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):
|
|
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.get_shape().as_list(),
|
|
inputs.get_shape().as_list())
|
|
|
|
def test_correct_initial_output_values(self):
|
|
inputs = tf.constant([[[[3, 4], [7, 24]],
|
|
[[5, -12], [-1, 0]]]], tf.float32)
|
|
target_norm_value = 10.0
|
|
dim = 3
|
|
expected_output = [[[[30/5.0, 40/5.0], [70/25.0, 240/25.0]],
|
|
[[50/13.0, -120/13.0], [-10, 0]]]]
|
|
with self.test_session() as sess:
|
|
normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
|
|
dim)
|
|
sess.run(tf.global_variables_initializer())
|
|
output = normalized_inputs.eval()
|
|
self.assertAllClose(output, expected_output)
|
|
|
|
def test_multiple_target_norm_values(self):
|
|
inputs = tf.constant([[[[3, 4], [7, 24]],
|
|
[[5, -12], [-1, 0]]]], tf.float32)
|
|
target_norm_value = [10.0, 20.0]
|
|
dim = 3
|
|
expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]],
|
|
[[50/13.0, -240/13.0], [-10, 0]]]]
|
|
with self.test_session() as sess:
|
|
normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
|
|
dim)
|
|
sess.run(tf.global_variables_initializer())
|
|
output = normalized_inputs.eval()
|
|
self.assertAllClose(output, expected_output)
|
|
|
|
|
|
class OpsTestPositionSensitiveCropRegions(tf.test.TestCase):
|
|
|
|
def test_position_sensitive(self):
|
|
num_spatial_bins = [3, 2]
|
|
image_shape = [3, 2, 6]
|
|
|
|
# First channel is 1's, second channel is 2's, etc.
|
|
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
|
|
shape=image_shape)
|
|
boxes = tf.random_uniform((2, 4))
|
|
|
|
# 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]
|
|
ps_crop_and_pool = ops.position_sensitive_crop_regions(
|
|
image, boxes, crop_size, num_spatial_bins, global_pool=True)
|
|
|
|
with self.test_session() as sess:
|
|
output = sess.run(ps_crop_and_pool)
|
|
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]
|
|
|
|
image = tf.constant(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], keep_dims=True)
|
|
|
|
ps_crop_and_pool = ops.position_sensitive_crop_regions(
|
|
tiled_image,
|
|
boxes,
|
|
crop_size,
|
|
num_spatial_bins,
|
|
global_pool=True)
|
|
|
|
with self.test_session() as sess:
|
|
expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
|
|
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]
|
|
|
|
image = tf.constant(1, dtype=tf.float32, shape=image_shape)
|
|
boxes = tf.constant([[0, 0, 1, 1]], dtype=tf.float32)
|
|
|
|
with self.assertRaisesRegexp(
|
|
ValueError, 'Dimension size must be evenly divisible by 4 but is 5'):
|
|
ops.position_sensitive_crop_regions(
|
|
image, boxes, crop_size, num_spatial_bins, global_pool=True)
|
|
|
|
def test_position_sensitive_with_global_pool_false(self):
|
|
num_spatial_bins = [3, 2]
|
|
image_shape = [3, 2, 6]
|
|
num_boxes = 2
|
|
|
|
# First channel is 1's, second channel is 2's, etc.
|
|
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
|
|
shape=image_shape)
|
|
boxes = tf.random_uniform((num_boxes, 4))
|
|
|
|
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]
|
|
ps_crop = ops.position_sensitive_crop_regions(
|
|
image, boxes, crop_size, num_spatial_bins, global_pool=False)
|
|
with self.test_session() as sess:
|
|
output = sess.run(ps_crop)
|
|
|
|
self.assertAllEqual(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
|
|
|
|
# First channel is 1's, second channel is 2's, etc.
|
|
image = tf.constant(range(1, 3 * 2 + 1) * 6, dtype=tf.float32,
|
|
shape=image_shape)
|
|
boxes = tf.random_uniform((num_boxes, 4))
|
|
|
|
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]
|
|
|
|
# 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), keep_dims=True)
|
|
|
|
with self.test_session() as sess:
|
|
output = sess.run(ps_crop_and_pool)
|
|
|
|
self.assertAllEqual(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(tf.test.TestCase):
|
|
|
|
def test_position_sensitive_with_single_bin(self):
|
|
num_spatial_bins = [1, 1]
|
|
image_shape = [2, 3, 3, 4]
|
|
crop_size = [2, 2]
|
|
|
|
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)
|
|
|
|
with self.test_session() as sess:
|
|
expected_output, output = sess.run((crop_and_pool, ps_crop_and_pool))
|
|
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]
|
|
|
|
images = tf.constant(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)
|
|
# 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)
|
|
|
|
ps_crop = ops.batch_position_sensitive_crop_regions(
|
|
images, boxes, crop_size, num_spatial_bins, global_pool=False)
|
|
|
|
with self.test_session() as sess:
|
|
output = sess.run(ps_crop)
|
|
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]
|
|
|
|
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)
|
|
|
|
with self.test_session() as sess:
|
|
pooled_output, unpooled_output = sess.run((ps_crop_and_pool, ps_crop))
|
|
self.assertAllClose(pooled_output, unpooled_output)
|
|
|
|
|
|
class ReframeBoxMasksToImageMasksTest(tf.test.TestCase):
|
|
|
|
def testZeroImageOnEmptyMask(self):
|
|
box_masks = tf.constant([[[0, 0],
|
|
[0, 0]]], dtype=tf.float32)
|
|
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)
|
|
np_expected_image_masks = np.array([[[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0]]], dtype=np.float32)
|
|
with self.test_session() as sess:
|
|
np_image_masks = sess.run(image_masks)
|
|
self.assertAllClose(np_image_masks, np_expected_image_masks)
|
|
|
|
def testZeroBoxMasks(self):
|
|
box_masks = tf.zeros([0, 3, 3], dtype=tf.float32)
|
|
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)
|
|
with self.test_session() as sess:
|
|
np_image_masks = sess.run(image_masks)
|
|
self.assertAllEqual(np_image_masks.shape, np.array([0, 4, 4]))
|
|
|
|
def testMaskIsCenteredInImageWhenBoxIsCentered(self):
|
|
box_masks = tf.constant([[[1, 1],
|
|
[1, 1]]], dtype=tf.float32)
|
|
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)
|
|
np_expected_image_masks = np.array([[[0, 0, 0, 0],
|
|
[0, 1, 1, 0],
|
|
[0, 1, 1, 0],
|
|
[0, 0, 0, 0]]], dtype=np.float32)
|
|
with self.test_session() as sess:
|
|
np_image_masks = sess.run(image_masks)
|
|
self.assertAllClose(np_image_masks, np_expected_image_masks)
|
|
|
|
def testMaskOffCenterRemainsOffCenterInImage(self):
|
|
box_masks = tf.constant([[[1, 0],
|
|
[0, 1]]], dtype=tf.float32)
|
|
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)
|
|
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)
|
|
with self.test_session() as sess:
|
|
np_image_masks = sess.run(image_masks)
|
|
self.assertAllClose(np_image_masks, np_expected_image_masks)
|
|
|
|
|
|
class MergeBoxesWithMultipleLabelsTest(tf.test.TestCase):
|
|
|
|
def testMergeBoxesWithMultipleLabels(self):
|
|
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))
|
|
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)
|
|
with self.test_session() as sess:
|
|
(np_merged_boxes, np_merged_classes, np_merged_confidences,
|
|
np_merged_box_indices) = sess.run(
|
|
[merged_boxes, merged_classes, merged_confidences,
|
|
merged_box_indices])
|
|
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):
|
|
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))
|
|
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)
|
|
with self.test_session() as sess:
|
|
(np_merged_boxes, np_merged_classes, np_merged_confidences,
|
|
np_merged_box_indices) = sess.run(
|
|
[merged_boxes, merged_classes, merged_confidences,
|
|
merged_box_indices])
|
|
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):
|
|
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))
|
|
with self.test_session() as sess:
|
|
(np_merged_boxes, np_merged_classes, np_merged_confidences,
|
|
np_merged_box_indices) = sess.run(
|
|
[merged_boxes, merged_classes, merged_confidences,
|
|
merged_box_indices])
|
|
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])
|
|
|
|
|
|
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):
|
|
params_placeholder = tf.placeholder(tf.float32, shape=[None, 4])
|
|
indices_placeholder = tf.placeholder(tf.int32, shape=[None])
|
|
gather_result = ops.matmul_gather_on_zeroth_axis(
|
|
params_placeholder, indices_placeholder)
|
|
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]])
|
|
with self.test_session() as sess:
|
|
gather_output = sess.run(gather_result, feed_dict={
|
|
params_placeholder: params, indices_placeholder: indices})
|
|
self.assertAllClose(gather_output, expected_output)
|
|
|
|
|
|
class OpsTestMatMulCropAndResize(test_case.TestCase):
|
|
|
|
def testMatMulCropAndResize2x2To1x1(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
|
|
|
|
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
|
|
expected_output = [[[[[2.5]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize2x2To1x1Flipped(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[1, 1])
|
|
|
|
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
|
|
boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
|
|
expected_output = [[[[[2.5]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize2x2To3x3(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
|
|
|
|
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1]]], dtype=np.float32)
|
|
expected_output = [[[[[1.0], [1.5], [2.0]],
|
|
[[2.0], [2.5], [3.0]],
|
|
[[3.0], [3.5], [4.0]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize2x2To3x3Flipped(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[3, 3])
|
|
|
|
image = np.array([[[[1], [2]], [[3], [4]]]], dtype=np.float32)
|
|
boxes = np.array([[[1, 1, 0, 0]]], dtype=np.float32)
|
|
expected_output = [[[[[4.0], [3.5], [3.0]],
|
|
[[3.0], [2.5], [2.0]],
|
|
[[2.0], [1.5], [1.0]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize3x3To2x2(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
|
|
|
|
image = np.array([[[[1], [2], [3]],
|
|
[[4], [5], [6]],
|
|
[[7], [8], [9]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1],
|
|
[0, 0, .5, .5]]], dtype=np.float32)
|
|
expected_output = [[[[[1], [3]], [[7], [9]]],
|
|
[[[1], [2]], [[4], [5]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize3x3To2x2_2Channels(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
|
|
|
|
image = np.array([[[[1, 0], [2, 1], [3, 2]],
|
|
[[4, 3], [5, 4], [6, 5]],
|
|
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1],
|
|
[0, 0, .5, .5]]], dtype=np.float32)
|
|
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
|
|
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testBatchMatMulCropAndResize3x3To2x2_2Channels(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
|
|
|
|
image = np.array([[[[1, 0], [2, 1], [3, 2]],
|
|
[[4, 3], [5, 4], [6, 5]],
|
|
[[7, 6], [8, 7], [9, 8]]],
|
|
[[[1, 0], [2, 1], [3, 2]],
|
|
[[4, 3], [5, 4], [6, 5]],
|
|
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1],
|
|
[0, 0, .5, .5]],
|
|
[[1, 1, 0, 0],
|
|
[.5, .5, 0, 0]]], dtype=np.float32)
|
|
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
|
|
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
|
|
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
|
|
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testMatMulCropAndResize3x3To2x2Flipped(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.matmul_crop_and_resize(image, boxes, crop_size=[2, 2])
|
|
|
|
image = np.array([[[[1], [2], [3]],
|
|
[[4], [5], [6]],
|
|
[[7], [8], [9]]]], dtype=np.float32)
|
|
boxes = np.array([[[1, 1, 0, 0],
|
|
[.5, .5, 0, 0]]], dtype=np.float32)
|
|
expected_output = [[[[[9], [7]], [[3], [1]]],
|
|
[[[5], [4]], [[2], [1]]]]]
|
|
crop_output = self.execute(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
def testInvalidInputShape(self):
|
|
image = tf.constant([[[1], [2]], [[3], [4]]], dtype=tf.float32)
|
|
boxes = tf.constant([[-1, -1, 1, 1]], dtype=tf.float32)
|
|
crop_size = [4, 4]
|
|
with self.assertRaises(ValueError):
|
|
_ = ops.matmul_crop_and_resize(image, boxes, crop_size)
|
|
|
|
|
|
class OpsTestCropAndResize(test_case.TestCase):
|
|
|
|
def testBatchCropAndResize3x3To2x2_2Channels(self):
|
|
|
|
def graph_fn(image, boxes):
|
|
return ops.native_crop_and_resize(image, boxes, crop_size=[2, 2])
|
|
|
|
image = np.array([[[[1, 0], [2, 1], [3, 2]],
|
|
[[4, 3], [5, 4], [6, 5]],
|
|
[[7, 6], [8, 7], [9, 8]]],
|
|
[[[1, 0], [2, 1], [3, 2]],
|
|
[[4, 3], [5, 4], [6, 5]],
|
|
[[7, 6], [8, 7], [9, 8]]]], dtype=np.float32)
|
|
boxes = np.array([[[0, 0, 1, 1],
|
|
[0, 0, .5, .5]],
|
|
[[1, 1, 0, 0],
|
|
[.5, .5, 0, 0]]], dtype=np.float32)
|
|
expected_output = [[[[[1, 0], [3, 2]], [[7, 6], [9, 8]]],
|
|
[[[1, 0], [2, 1]], [[4, 3], [5, 4]]]],
|
|
[[[[9, 8], [7, 6]], [[3, 2], [1, 0]]],
|
|
[[[5, 4], [4, 3]], [[2, 1], [1, 0]]]]]
|
|
crop_output = self.execute_cpu(graph_fn, [image, boxes])
|
|
self.assertAllClose(crop_output, expected_output)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|