remove unnecessary files

research/object_detection/meta_architectures/context_rcnn_lib_tf2_test.py

-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Tests for context_rcnn_lib."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import unittest
-from absl.testing import parameterized
-import tensorflow.compat.v1 as tf
-
-from object_detection.meta_architectures import context_rcnn_lib
-from object_detection.utils import test_case
-from object_detection.utils import tf_version
-
-_NEGATIVE_PADDING_VALUE = -100000
-
-class ContextRcnnLibTest(parameterized.TestCase, test_case.TestCase,
-                         tf.test.TestCase):
-  """Tests for the functions in context_rcnn_lib."""
-
-  def test_compute_valid_mask(self):
-    num_elements = tf.constant(3, tf.int32)
-    num_valid_elementss = tf.constant((1, 2), tf.int32)
-    valid_mask = context_rcnn_lib.compute_valid_mask(num_valid_elementss,
-                                                     num_elements)
-    expected_valid_mask = tf.constant([[1, 0, 0], [1, 1, 0]], tf.float32)
-    self.assertAllEqual(valid_mask, expected_valid_mask)
-
-  def test_filter_weight_value(self):
-    weights = tf.ones((2, 3, 2), tf.float32) * 4
-    values = tf.ones((2, 2, 4), tf.float32)
-    valid_mask = tf.constant([[True, True], [True, False]], tf.bool)
-
-    filtered_weights, filtered_values = context_rcnn_lib.filter_weight_value(
-        weights, values, valid_mask)
-    expected_weights = tf.constant([[[4, 4], [4, 4], [4, 4]],
-                                    [[4, _NEGATIVE_PADDING_VALUE + 4],
-                                     [4, _NEGATIVE_PADDING_VALUE + 4],
-                                     [4, _NEGATIVE_PADDING_VALUE + 4]]])
-
-    expected_values = tf.constant([[[1, 1, 1, 1], [1, 1, 1, 1]],
-                                   [[1, 1, 1, 1], [0, 0, 0, 0]]])
-    self.assertAllEqual(filtered_weights, expected_weights)
-    self.assertAllEqual(filtered_values, expected_values)
-
-    # Changes the valid_mask so the results will be different.
-    valid_mask = tf.constant([[True, True], [False, False]], tf.bool)
-
-    filtered_weights, filtered_values = context_rcnn_lib.filter_weight_value(
-        weights, values, valid_mask)
-    expected_weights = tf.constant(
-        [[[4, 4], [4, 4], [4, 4]],
-         [[_NEGATIVE_PADDING_VALUE + 4, _NEGATIVE_PADDING_VALUE + 4],
-          [_NEGATIVE_PADDING_VALUE + 4, _NEGATIVE_PADDING_VALUE + 4],
-          [_NEGATIVE_PADDING_VALUE + 4, _NEGATIVE_PADDING_VALUE + 4]]])
-
-    expected_values = tf.constant([[[1, 1, 1, 1], [1, 1, 1, 1]],
-                                   [[0, 0, 0, 0], [0, 0, 0, 0]]])
-    self.assertAllEqual(filtered_weights, expected_weights)
-    self.assertAllEqual(filtered_values, expected_values)
-
-  @parameterized.parameters((2, True, True), (2, False, True),
-                            (10, True, False), (10, False, False))
-  def test_project_features(self, projection_dimension, is_training, normalize):
-    features = tf.ones([2, 3, 4], tf.float32)
-    projected_features = context_rcnn_lib.project_features(
-        features,
-        projection_dimension,
-        is_training=is_training,
-        normalize=normalize,
-        node=context_rcnn_lib.ContextProjection(projection_dimension, False))
-
-    # Makes sure the shape is correct.
-    self.assertAllEqual(projected_features.shape, [2, 3, projection_dimension])
-
-  @parameterized.parameters(
-      (2, 10, 1),
-      (3, 10, 2),
-      (4, 20, 3),
-      (5, 20, 4),
-      (7, 20, 5),
-  )
-  def test_attention_block(self, bottleneck_dimension, output_dimension,
-                           attention_temperature):
-    input_features = tf.ones([2, 3, 4], tf.float32)
-    context_features = tf.ones([2, 2, 3], tf.float32)
-    valid_mask = tf.constant([[True, True], [False, False]], tf.bool)
-    is_training = False
-    projection_layers = {context_rcnn_lib.KEY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False), context_rcnn_lib.VALUE_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False),
-                         context_rcnn_lib.QUERY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False)}
-    
-    #Add in the feature layer because this is further down the pipeline and it isn't automatically injected.
-    projection_layers['feature'] = context_rcnn_lib.ContextProjection(output_dimension, False)
-
-    output_features = context_rcnn_lib.attention_block(
-        input_features, context_features, bottleneck_dimension,
-        output_dimension, attention_temperature, valid_mask, is_training, projection_layers)
-
-    # Makes sure the shape is correct.
-    self.assertAllEqual(output_features.shape, [2, 3, output_dimension])
-
-  @parameterized.parameters(True, False)
-  def test_compute_box_context_attention(self, is_training):
-    box_features = tf.ones([2, 3, 4, 4, 4], tf.float32)
-    context_features = tf.ones([2, 5, 6], tf.float32)
-    valid_context_size = tf.constant((2, 3), tf.int32)
-    bottleneck_dimension = 10
-    attention_temperature = 1
-    projection_layers = {context_rcnn_lib.KEY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False), context_rcnn_lib.VALUE_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False),
-                         context_rcnn_lib.QUERY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False)}
-    attention_features = context_rcnn_lib.compute_box_context_attention(
-        box_features, context_features, valid_context_size,
-        bottleneck_dimension, attention_temperature, is_training, 
-        False, projection_layers)
-    # Makes sure the shape is correct.
-    self.assertAllEqual(attention_features.shape, [2, 3, 1, 1, 4])
-
-
-if __name__ == '__main__':
-  tf.test.main()