X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_resnet_v2_test.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_resnet_v2_test.py;h=c369ed9f74b1bf14fb2d45b2210df6239d943177;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py new file mode 100755 index 0000000..c369ed9 --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2_test.py @@ -0,0 +1,265 @@ +# 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.inception_resnet_v2.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception + + +class InceptionTest(tf.test.TestCase): + + def testBuildLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, endpoints = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue('AuxLogits' in endpoints) + auxlogits = endpoints['AuxLogits'] + self.assertTrue( + auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits')) + self.assertListEqual(auxlogits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildWithoutAuxLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, endpoints = inception.inception_resnet_v2(inputs, num_classes, + create_aux_logits=False) + self.assertTrue('AuxLogits' not in endpoints) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildEndPoints(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2(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]) + pre_pool = end_points['Conv2d_7b_1x1'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 8, 8, 1536]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + net, end_points = inception.inception_resnet_v2_base(inputs) + self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1')) + self.assertListEqual(net.get_shape().as_list(), + [batch_size, 8, 8, 1536]) + 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_6a', + 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + 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_6a', + 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + 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_resnet_v2_base( + inputs, final_endpoint=endpoint) + if endpoint != 'PreAuxLogits': + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionResnetV2/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits') + endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], + 'Conv2d_2a_3x3': [5, 147, 147, 32], + 'Conv2d_2b_3x3': [5, 147, 147, 64], + 'MaxPool_3a_3x3': [5, 73, 73, 64], + 'Conv2d_3b_1x1': [5, 73, 73, 80], + 'Conv2d_4a_3x3': [5, 71, 71, 192], + 'MaxPool_5a_3x3': [5, 35, 35, 192], + 'Mixed_5b': [5, 35, 35, 320], + 'Mixed_6a': [5, 17, 17, 1088], + 'PreAuxLogits': [5, 17, 17, 1088] + } + + 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 testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits', align_feature_maps=True) + endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32], + 'Conv2d_2a_3x3': [5, 150, 150, 32], + 'Conv2d_2b_3x3': [5, 150, 150, 64], + 'MaxPool_3a_3x3': [5, 75, 75, 64], + 'Conv2d_3b_1x1': [5, 75, 75, 80], + 'Conv2d_4a_3x3': [5, 75, 75, 192], + 'MaxPool_5a_3x3': [5, 38, 38, 192], + 'Mixed_5b': [5, 38, 38, 320], + 'Mixed_6a': [5, 19, 19, 1088], + 'PreAuxLogits': [5, 19, 19, 1088] + } + + 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 testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits', output_stride=8) + endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], + 'Conv2d_2a_3x3': [5, 147, 147, 32], + 'Conv2d_2b_3x3': [5, 147, 147, 64], + 'MaxPool_3a_3x3': [5, 73, 73, 64], + 'Conv2d_3b_1x1': [5, 73, 73, 80], + 'Conv2d_4a_3x3': [5, 71, 71, 192], + 'MaxPool_5a_3x3': [5, 35, 35, 192], + 'Mixed_5b': [5, 35, 35, 320], + 'Mixed_6a': [5, 33, 33, 1088], + 'PreAuxLogits': [5, 33, 33, 1088] + } + + 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 testVariablesSetDevice(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + # Force all Variables to reside on the device. + with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): + inception.inception_resnet_v2(inputs, num_classes) + with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): + inception.inception_resnet_v2(inputs, num_classes) + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): + self.assertDeviceEqual(v.device, '/cpu:0') + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): + self.assertDeviceEqual(v.device, '/gpu:0') + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 150, 150 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Conv2d_7b_1x1'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 3, 3, 1536]) + + def testUnknownBatchSize(self): + batch_size = 1 + height, width = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + 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 = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_resnet_v2(eval_inputs, + num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + 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 + with self.test_session() as sess: + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_resnet_v2(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_resnet_v2(eval_inputs, + num_classes, + is_training=False, + reuse=True) + predictions = tf.argmax(logits, 1) + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + +if __name__ == '__main__': + tf.test.main()