# coding=utf-8 # coding=utf-8 # Copyright 2016 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 nets.inception_v3.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v1 as tf from tf_slim import model_analyzer from tf_slim.nets import inception_v3 from tf_slim.ops import variables as variables_lib from tf_slim.ops.arg_scope import arg_scope # pylint:disable=g-direct-tensorflow-import from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test # pylint:enable=g-direct-tensorflow-import def setUpModule(): tf.disable_eager_execution() class InceptionV3Test(test.TestCase): def testBuildClassificationNetwork(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('Predictions' in end_points) self.assertListEqual(end_points['Predictions'].get_shape().as_list(), [batch_size, num_classes]) def testBuildBaseNetwork(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) final_endpoint, end_points = inception_v3.inception_v3_base(inputs) self.assertTrue(final_endpoint.op.name.startswith('InceptionV3/Mixed_7c')) self.assertListEqual(final_endpoint.get_shape().as_list(), [batch_size, 8, 8, 2048]) expected_endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c' ] self.assertItemsEqual(end_points.keys(), expected_endpoints) def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 299, 299 endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c' ] for index, endpoint in enumerate(endpoints): with ops.Graph().as_default(): inputs = random_ops.random_uniform((batch_size, height, width, 3)) out_tensor, end_points = inception_v3.inception_v3_base( inputs, final_endpoint=endpoint) self.assertTrue( out_tensor.op.name.startswith('InceptionV3/' + endpoint)) self.assertItemsEqual(endpoints[:index + 1], end_points) def testBuildAndCheckAllEndPointsUptoMixed7c(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) _, end_points = inception_v3.inception_v3_base( inputs, final_endpoint='Mixed_7c') endpoints_shapes = { 'Conv2d_1a_3x3': [batch_size, 149, 149, 32], 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], 'MaxPool_3a_3x3': [batch_size, 73, 73, 64], 'Conv2d_3b_1x1': [batch_size, 73, 73, 80], 'Conv2d_4a_3x3': [batch_size, 71, 71, 192], 'MaxPool_5a_3x3': [batch_size, 35, 35, 192], 'Mixed_5b': [batch_size, 35, 35, 256], 'Mixed_5c': [batch_size, 35, 35, 288], 'Mixed_5d': [batch_size, 35, 35, 288], 'Mixed_6a': [batch_size, 17, 17, 768], 'Mixed_6b': [batch_size, 17, 17, 768], 'Mixed_6c': [batch_size, 17, 17, 768], 'Mixed_6d': [batch_size, 17, 17, 768], 'Mixed_6e': [batch_size, 17, 17, 768], 'Mixed_7a': [batch_size, 8, 8, 1280], 'Mixed_7b': [batch_size, 8, 8, 2048], 'Mixed_7c': [batch_size, 8, 8, 2048] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), expected_shape) def testModelHasExpectedNumberOfParameters(self): batch_size = 5 height, width = 299, 299 inputs = random_ops.random_uniform((batch_size, height, width, 3)) with arg_scope(inception_v3.inception_v3_arg_scope()): inception_v3.inception_v3_base(inputs) total_params, _ = model_analyzer.analyze_vars( variables_lib.get_model_variables()) self.assertAlmostEqual(21802784, total_params) def testBuildEndPoints(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) _, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertTrue('Logits' in end_points) logits = end_points['Logits'] self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('AuxLogits' in end_points) aux_logits = end_points['AuxLogits'] self.assertListEqual(aux_logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('Mixed_7c' in end_points) pre_pool = end_points['Mixed_7c'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 8, 2048]) self.assertTrue('PreLogits' in end_points) pre_logits = end_points['PreLogits'] self.assertListEqual(pre_logits.get_shape().as_list(), [batch_size, 1, 1, 2048]) def testBuildEndPointsWithDepthMultiplierLessThanOne(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) _, end_points = inception_v3.inception_v3(inputs, num_classes) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] _, end_points_with_multiplier = inception_v3.inception_v3( inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=0.5) for key in endpoint_keys: original_depth = end_points[key].get_shape().as_list()[3] new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] self.assertEqual(0.5 * original_depth, new_depth) def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) _, end_points = inception_v3.inception_v3(inputs, num_classes) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] _, end_points_with_multiplier = inception_v3.inception_v3( inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=2.0) for key in endpoint_keys: original_depth = end_points[key].get_shape().as_list()[3] new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] self.assertEqual(2.0 * original_depth, new_depth) def testRaiseValueErrorWithInvalidDepthMultiplier(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) with self.assertRaises(ValueError): _ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=-0.1) with self.assertRaises(ValueError): _ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=0.0) def testHalfSizeImages(self): batch_size = 5 height, width = 150, 150 num_classes = 1000 inputs = random_ops.random_uniform((batch_size, height, width, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 2048]) def testUnknownImageShape(self): ops.reset_default_graph() batch_size = 2 height, width = 299, 299 num_classes = 1000 input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) with self.cached_session() as sess: inputs = array_ops.placeholder( dtypes.float32, shape=(batch_size, None, None, 3)) logits, end_points = inception_v3.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] feed_dict = {inputs: input_np} variables.global_variables_initializer().run() pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) def testUnknownBatchSize(self): batch_size = 1 height, width = 299, 299 num_classes = 1000 inputs = array_ops.placeholder(dtypes.float32, (None, height, width, 3)) logits, _ = inception_v3.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = random_ops.random_uniform((batch_size, height, width, 3)) with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEqual(output.shape, (batch_size, num_classes)) def testEvaluation(self): batch_size = 2 height, width = 299, 299 num_classes = 1000 eval_inputs = random_ops.random_uniform((batch_size, height, width, 3)) logits, _ = inception_v3.inception_v3( eval_inputs, num_classes, is_training=False) predictions = math_ops.argmax(logits, 1) with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) output = sess.run(predictions) self.assertEqual(output.shape, (batch_size,)) def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 train_inputs = random_ops.random_uniform( (train_batch_size, height, width, 3)) inception_v3.inception_v3(train_inputs, num_classes) eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3)) logits, _ = inception_v3.inception_v3( eval_inputs, num_classes, is_training=False, reuse=True) predictions = math_ops.argmax(logits, 1) with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) output = sess.run(predictions) self.assertEqual(output.shape, (eval_batch_size,)) def testLogitsNotSqueezed(self): num_classes = 25 images = random_ops.random_uniform([1, 299, 299, 3]) logits, _ = inception_v3.inception_v3( images, num_classes=num_classes, spatial_squeeze=False) with self.cached_session() as sess: variables.global_variables_initializer().run() logits_out = sess.run(logits) self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) if __name__ == '__main__': test.main()