X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v2_test.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v2_test.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=397aa50246cf038793c29a1640a8ab4ca07b41a9;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py deleted file mode 100755 index 397aa50..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2_test.py +++ /dev/null @@ -1,262 +0,0 @@ -# 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 slim_nets.inception_v2.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np -import tensorflow as tf - -from nets import inception - -slim = tf.contrib.slim - - -class InceptionV2Test(tf.test.TestCase): - - def testBuildClassificationNetwork(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/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 = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - mixed_5c, end_points = inception.inception_v2_base(inputs) - self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c')) - self.assertListEqual(mixed_5c.get_shape().as_list(), - [batch_size, 7, 7, 1024]) - expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', - 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', - 'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7', - 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', - 'MaxPool_3a_3x3'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 224, 224 - endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', - 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', - 'Mixed_5a', 'Mixed_5b', 'Mixed_5c'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = inception.inception_v2_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'InceptionV2/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildAndCheckAllEndPointsUptoMixed5c(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2_base(inputs, - final_endpoint='Mixed_5c') - endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256], - 'Mixed_3c': [batch_size, 28, 28, 320], - 'Mixed_4a': [batch_size, 14, 14, 576], - 'Mixed_4b': [batch_size, 14, 14, 576], - 'Mixed_4c': [batch_size, 14, 14, 576], - 'Mixed_4d': [batch_size, 14, 14, 576], - 'Mixed_4e': [batch_size, 14, 14, 576], - 'Mixed_5a': [batch_size, 7, 7, 1024], - 'Mixed_5b': [batch_size, 7, 7, 1024], - 'Mixed_5c': [batch_size, 7, 7, 1024], - 'Conv2d_1a_7x7': [batch_size, 112, 112, 64], - 'MaxPool_2a_3x3': [batch_size, 56, 56, 64], - 'Conv2d_2b_1x1': [batch_size, 56, 56, 64], - 'Conv2d_2c_3x3': [batch_size, 56, 56, 192], - 'MaxPool_3a_3x3': [batch_size, 28, 28, 192]} - 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 = 224, 224 - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope(inception.inception_v2_arg_scope()): - inception.inception_v2_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(10173112, total_params) - - def testBuildEndPointsWithDepthMultiplierLessThanOne(self): - batch_size = 5 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2(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.inception_v2( - 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 = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = inception.inception_v2(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.inception_v2( - 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 = 224, 224 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with self.assertRaises(ValueError): - _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1) - with self.assertRaises(ValueError): - _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0) - - def testHalfSizeImages(self): - batch_size = 5 - height, width = 112, 112 - num_classes = 1000 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_5c'] - self.assertListEqual(pre_pool.get_shape().as_list(), - [batch_size, 4, 4, 1024]) - - def testUnknownImageShape(self): - tf.reset_default_graph() - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) - with self.test_session() as sess: - inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) - logits, end_points = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Mixed_5c'] - feed_dict = {inputs: input_np} - tf.global_variables_initializer().run() - pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) - self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) - - def testUnknowBatchSize(self): - batch_size = 1 - height, width = 224, 224 - num_classes = 1000 - - inputs = tf.placeholder(tf.float32, (None, height, width, 3)) - logits, _ = inception.inception_v2(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [None, num_classes]) - images = tf.random_uniform((batch_size, height, width, 3)) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(logits, {inputs: images.eval()}) - self.assertEquals(output.shape, (batch_size, num_classes)) - - def testEvaluation(self): - batch_size = 2 - height, width = 224, 224 - num_classes = 1000 - - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = inception.inception_v2(eval_inputs, num_classes, - is_training=False) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (batch_size,)) - - def testTrainEvalWithReuse(self): - train_batch_size = 5 - eval_batch_size = 2 - height, width = 150, 150 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - inception.inception_v2(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True) - predictions = tf.argmax(logits, 1) - - with self.test_session() as sess: - sess.run(tf.global_variables_initializer()) - output = sess.run(predictions) - self.assertEquals(output.shape, (eval_batch_size,)) - - def testLogitsNotSqueezed(self): - num_classes = 25 - images = tf.random_uniform([1, 224, 224, 3]) - logits, _ = inception.inception_v2(images, - num_classes=num_classes, - spatial_squeeze=False) - - with self.test_session() as sess: - tf.global_variables_initializer().run() - logits_out = sess.run(logits) - self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) - - -if __name__ == '__main__': - tf.test.main()