X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Foverfeat_test.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Foverfeat_test.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=446f9ac4d8446ff0eeb5ed9a8d21fa0eb7fabf0b;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py deleted file mode 100755 index 446f9ac..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/overfeat_test.py +++ /dev/null @@ -1,145 +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.slim_nets.overfeat.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import overfeat - -slim = tf.contrib.slim - - -class OverFeatTest(tf.test.TestCase): - - def testBuild(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs, num_classes) - self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - - def testFullyConvolutional(self): - batch_size = 1 - height, width = 281, 281 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False) - self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd') - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, 2, 2, num_classes]) - - def testEndPoints(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - _, end_points = overfeat.overfeat(inputs, num_classes) - expected_names = ['overfeat/conv1', - 'overfeat/pool1', - 'overfeat/conv2', - 'overfeat/pool2', - 'overfeat/conv3', - 'overfeat/conv4', - 'overfeat/conv5', - 'overfeat/pool5', - 'overfeat/fc6', - 'overfeat/fc7', - 'overfeat/fc8' - ] - self.assertSetEqual(set(end_points.keys()), set(expected_names)) - - def testModelVariables(self): - batch_size = 5 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - overfeat.overfeat(inputs, num_classes) - expected_names = ['overfeat/conv1/weights', - 'overfeat/conv1/biases', - 'overfeat/conv2/weights', - 'overfeat/conv2/biases', - 'overfeat/conv3/weights', - 'overfeat/conv3/biases', - 'overfeat/conv4/weights', - 'overfeat/conv4/biases', - 'overfeat/conv5/weights', - 'overfeat/conv5/biases', - 'overfeat/fc6/weights', - 'overfeat/fc6/biases', - 'overfeat/fc7/weights', - 'overfeat/fc7/biases', - 'overfeat/fc8/weights', - 'overfeat/fc8/biases', - ] - model_variables = [v.op.name for v in slim.get_model_variables()] - self.assertSetEqual(set(model_variables), set(expected_names)) - - def testEvaluation(self): - batch_size = 2 - height, width = 231, 231 - num_classes = 1000 - with self.test_session(): - eval_inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(eval_inputs, is_training=False) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - predictions = tf.argmax(logits, 1) - self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) - - def testTrainEvalWithReuse(self): - train_batch_size = 2 - eval_batch_size = 1 - train_height, train_width = 231, 231 - eval_height, eval_width = 281, 281 - num_classes = 1000 - with self.test_session(): - train_inputs = tf.random_uniform( - (train_batch_size, train_height, train_width, 3)) - logits, _ = overfeat.overfeat(train_inputs) - self.assertListEqual(logits.get_shape().as_list(), - [train_batch_size, num_classes]) - tf.get_variable_scope().reuse_variables() - eval_inputs = tf.random_uniform( - (eval_batch_size, eval_height, eval_width, 3)) - logits, _ = overfeat.overfeat(eval_inputs, is_training=False, - spatial_squeeze=False) - self.assertListEqual(logits.get_shape().as_list(), - [eval_batch_size, 2, 2, num_classes]) - logits = tf.reduce_mean(logits, [1, 2]) - predictions = tf.argmax(logits, 1) - self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) - - def testForward(self): - batch_size = 1 - height, width = 231, 231 - with self.test_session() as sess: - inputs = tf.random_uniform((batch_size, height, width, 3)) - logits, _ = overfeat.overfeat(inputs) - sess.run(tf.global_variables_initializer()) - output = sess.run(logits) - self.assertTrue(output.any()) - -if __name__ == '__main__': - tf.test.main()