X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v1_test.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v1_test.py;h=11eb14ee933607fe2ccb8bcb6244a837652b59b3;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py new file mode 100755 index 0000000..11eb14e --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v1_test.py @@ -0,0 +1,210 @@ +# 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_v1.""" + +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 InceptionV1Test(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_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/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_6c, end_points = inception.inception_v1_base(inputs) + self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c')) + self.assertListEqual(mixed_6c.get_shape().as_list(), + [batch_size, 7, 7, 1024]) + expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', + 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', + 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', + 'Mixed_5b', 'Mixed_5c'] + 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', + 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', + 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', '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_v1_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionV1/' + 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_v1_base(inputs, + final_endpoint='Mixed_5c') + endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64], + 'MaxPool_2a_3x3': [5, 56, 56, 64], + 'Conv2d_2b_1x1': [5, 56, 56, 64], + 'Conv2d_2c_3x3': [5, 56, 56, 192], + 'MaxPool_3a_3x3': [5, 28, 28, 192], + 'Mixed_3b': [5, 28, 28, 256], + 'Mixed_3c': [5, 28, 28, 480], + 'MaxPool_4a_3x3': [5, 14, 14, 480], + 'Mixed_4b': [5, 14, 14, 512], + 'Mixed_4c': [5, 14, 14, 512], + 'Mixed_4d': [5, 14, 14, 512], + 'Mixed_4e': [5, 14, 14, 528], + 'Mixed_4f': [5, 14, 14, 832], + 'MaxPool_5a_2x2': [5, 7, 7, 832], + 'Mixed_5b': [5, 7, 7, 832], + 'Mixed_5c': [5, 7, 7, 1024]} + + 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_v1_arg_scope()): + inception.inception_v1_base(inputs) + total_params, _ = slim.model_analyzer.analyze_vars( + slim.get_model_variables()) + self.assertAlmostEqual(5607184, total_params) + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 112, 112 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + mixed_5c, _ = inception.inception_v1_base(inputs) + self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c')) + self.assertListEqual(mixed_5c.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_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/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_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/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_v1(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 = 224, 224 + num_classes = 1000 + + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_v1(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_v1(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_v1(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()