Spaces:
Running
Running
| # Copyright 2019 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 lstm_object_detection.lstm.utils.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow.compat.v1 as tf | |
| from lstm_object_detection.lstm import utils | |
| class QuantizableUtilsTest(tf.test.TestCase): | |
| def test_quantizable_concat_is_training(self): | |
| inputs_1 = tf.zeros([4, 10, 10, 1], dtype=tf.float32) | |
| inputs_2 = tf.ones([4, 10, 10, 2], dtype=tf.float32) | |
| concat_in_train = utils.quantizable_concat([inputs_1, inputs_2], | |
| axis=3, | |
| is_training=True) | |
| self.assertAllEqual([4, 10, 10, 3], concat_in_train.shape.as_list()) | |
| self._check_min_max_ema(tf.get_default_graph()) | |
| self._check_min_max_vars(tf.get_default_graph()) | |
| def test_quantizable_concat_inference(self): | |
| inputs_1 = tf.zeros([4, 10, 10, 1], dtype=tf.float32) | |
| inputs_2 = tf.ones([4, 10, 10, 2], dtype=tf.float32) | |
| concat_in_train = utils.quantizable_concat([inputs_1, inputs_2], | |
| axis=3, | |
| is_training=False) | |
| self.assertAllEqual([4, 10, 10, 3], concat_in_train.shape.as_list()) | |
| self._check_no_min_max_ema(tf.get_default_graph()) | |
| self._check_min_max_vars(tf.get_default_graph()) | |
| def test_quantizable_concat_not_quantized_is_training(self): | |
| inputs_1 = tf.zeros([4, 10, 10, 1], dtype=tf.float32) | |
| inputs_2 = tf.ones([4, 10, 10, 2], dtype=tf.float32) | |
| concat_in_train = utils.quantizable_concat([inputs_1, inputs_2], | |
| axis=3, | |
| is_training=True, | |
| is_quantized=False) | |
| self.assertAllEqual([4, 10, 10, 3], concat_in_train.shape.as_list()) | |
| self._check_no_min_max_ema(tf.get_default_graph()) | |
| self._check_no_min_max_vars(tf.get_default_graph()) | |
| def test_quantizable_concat_not_quantized_inference(self): | |
| inputs_1 = tf.zeros([4, 10, 10, 1], dtype=tf.float32) | |
| inputs_2 = tf.ones([4, 10, 10, 2], dtype=tf.float32) | |
| concat_in_train = utils.quantizable_concat([inputs_1, inputs_2], | |
| axis=3, | |
| is_training=False, | |
| is_quantized=False) | |
| self.assertAllEqual([4, 10, 10, 3], concat_in_train.shape.as_list()) | |
| self._check_no_min_max_ema(tf.get_default_graph()) | |
| self._check_no_min_max_vars(tf.get_default_graph()) | |
| def test_quantize_op_is_training(self): | |
| inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32) | |
| outputs = utils.quantize_op(inputs) | |
| self.assertAllEqual(inputs.shape.as_list(), outputs.shape.as_list()) | |
| self._check_min_max_ema(tf.get_default_graph()) | |
| self._check_min_max_vars(tf.get_default_graph()) | |
| def test_quantize_op_inference(self): | |
| inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32) | |
| outputs = utils.quantize_op(inputs, is_training=False) | |
| self.assertAllEqual(inputs.shape.as_list(), outputs.shape.as_list()) | |
| self._check_no_min_max_ema(tf.get_default_graph()) | |
| self._check_min_max_vars(tf.get_default_graph()) | |
| def test_fixed_quantize_op(self): | |
| inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32) | |
| outputs = utils.fixed_quantize_op(inputs) | |
| self.assertAllEqual(inputs.shape.as_list(), outputs.shape.as_list()) | |
| self._check_no_min_max_ema(tf.get_default_graph()) | |
| self._check_no_min_max_vars(tf.get_default_graph()) | |
| def _check_min_max_vars(self, graph): | |
| op_types = [op.type for op in graph.get_operations()] | |
| self.assertTrue( | |
| any('FakeQuantWithMinMaxVars' in op_type for op_type in op_types)) | |
| def _check_min_max_ema(self, graph): | |
| op_names = [op.name for op in graph.get_operations()] | |
| self.assertTrue(any('AssignMinEma' in name for name in op_names)) | |
| self.assertTrue(any('AssignMaxEma' in name for name in op_names)) | |
| self.assertTrue(any('SafeQuantRangeMin' in name for name in op_names)) | |
| self.assertTrue(any('SafeQuantRangeMax' in name for name in op_names)) | |
| def _check_no_min_max_vars(self, graph): | |
| op_types = [op.type for op in graph.get_operations()] | |
| self.assertFalse( | |
| any('FakeQuantWithMinMaxVars' in op_type for op_type in op_types)) | |
| def _check_no_min_max_ema(self, graph): | |
| op_names = [op.name for op in graph.get_operations()] | |
| self.assertFalse(any('AssignMinEma' in name for name in op_names)) | |
| self.assertFalse(any('AssignMaxEma' in name for name in op_names)) | |
| self.assertFalse(any('SafeQuantRangeMin' in name for name in op_names)) | |
| self.assertFalse(any('SafeQuantRangeMax' in name for name in op_names)) | |
| class QuantizableSeparableConv2dTest(tf.test.TestCase): | |
| def test_quantizable_separable_conv2d(self): | |
| inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32) | |
| num_outputs = 64 | |
| kernel_size = [3, 3] | |
| scope = 'QuantSeparable' | |
| outputs = utils.quantizable_separable_conv2d( | |
| inputs, num_outputs, kernel_size, scope=scope) | |
| self.assertAllEqual([4, 10, 10, num_outputs], outputs.shape.as_list()) | |
| self._check_depthwise_bias_add(tf.get_default_graph(), scope) | |
| def test_quantizable_separable_conv2d_not_quantized(self): | |
| inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32) | |
| num_outputs = 64 | |
| kernel_size = [3, 3] | |
| scope = 'QuantSeparable' | |
| outputs = utils.quantizable_separable_conv2d( | |
| inputs, num_outputs, kernel_size, is_quantized=False, scope=scope) | |
| self.assertAllEqual([4, 10, 10, num_outputs], outputs.shape.as_list()) | |
| self._check_no_depthwise_bias_add(tf.get_default_graph(), scope) | |
| def _check_depthwise_bias_add(self, graph, scope): | |
| op_names = [op.name for op in graph.get_operations()] | |
| self.assertTrue( | |
| any('%s_bias/BiasAdd' % scope in name for name in op_names)) | |
| def _check_no_depthwise_bias_add(self, graph, scope): | |
| op_names = [op.name for op in graph.get_operations()] | |
| self.assertFalse( | |
| any('%s_bias/BiasAdd' % scope in name for name in op_names)) | |
| if __name__ == '__main__': | |
| tf.test.main() | |