X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Fmobilenet_v1_test.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Fmobilenet_v1_test.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=44e66446baa42f49e164131eb4c1a97b46a9918d;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py deleted file mode 100755 index 44e6644..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py +++ /dev/null @@ -1,450 +0,0 @@ -# Copyright 2017 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 MobileNet 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 mobilenet_v1 - -slim = tf.contrib.slim - - -class MobilenetV1Test(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 = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/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)) - net, end_points = mobilenet_v1.mobilenet_v1_base(inputs) - self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13')) - self.assertListEqual(net.get_shape().as_list(), - [batch_size, 7, 7, 1024]) - expected_endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', - 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', - 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', - 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', - 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', - 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', - 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', - 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', - 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', - 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', - 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildOnlyUptoFinalEndpoint(self): - batch_size = 5 - height, width = 224, 224 - endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', - 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', - 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', - 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', - 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', - 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', - 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', - 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', - 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', - 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', - 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] - for index, endpoint in enumerate(endpoints): - with tf.Graph().as_default(): - inputs = tf.random_uniform((batch_size, height, width, 3)) - out_tensor, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint=endpoint) - self.assertTrue(out_tensor.op.name.startswith( - 'MobilenetV1/' + endpoint)) - self.assertItemsEqual(endpoints[:index+1], end_points) - - def testBuildCustomNetworkUsingConvDefs(self): - batch_size = 5 - height, width = 224, 224 - conv_defs = [ - mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128), - mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512) - ] - - inputs = tf.random_uniform((batch_size, height, width, 3)) - net, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs) - self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3')) - self.assertListEqual(net.get_shape().as_list(), - [batch_size, 56, 56, 512]) - expected_endpoints = ['Conv2d_0', - 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', - 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', - 'Conv2d_3_depthwise', 'Conv2d_3_pointwise'] - self.assertItemsEqual(end_points.keys(), expected_endpoints) - - def testBuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], - 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_12_depthwise': [batch_size, 7, 7, 512], - 'Conv2d_12_pointwise': [batch_size, 7, 7, 1024], - 'Conv2d_13_depthwise': [batch_size, 7, 7, 1024], - 'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - output_stride = 16 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, output_stride=output_stride, - final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], - 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], - 'Conv2d_12_depthwise': [batch_size, 14, 14, 512], - 'Conv2d_12_pointwise': [batch_size, 14, 14, 1024], - 'Conv2d_13_depthwise': [batch_size, 14, 14, 1024], - 'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self): - batch_size = 5 - height, width = 224, 224 - output_stride = 8 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, output_stride=output_stride, - final_endpoint='Conv2d_13_pointwise') - endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], - 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], - 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], - 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], - 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], - 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], - 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], - 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], - 'Conv2d_6_depthwise': [batch_size, 28, 28, 256], - 'Conv2d_6_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_7_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_7_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_8_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_8_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_9_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_9_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_10_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_10_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_11_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_11_pointwise': [batch_size, 28, 28, 512], - 'Conv2d_12_depthwise': [batch_size, 28, 28, 512], - 'Conv2d_12_pointwise': [batch_size, 28, 28, 1024], - 'Conv2d_13_depthwise': [batch_size, 28, 28, 1024], - 'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - self.assertTrue(endpoint_name in end_points) - self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), - expected_shape) - - def testBuildAndCheckAllEndPointsApproximateFaceNet(self): - batch_size = 5 - height, width = 128, 128 - - inputs = tf.random_uniform((batch_size, height, width, 3)) - with slim.arg_scope([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - _, end_points = mobilenet_v1.mobilenet_v1_base( - inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75) - # For the Conv2d_0 layer FaceNet has depth=16 - endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24], - 'Conv2d_1_depthwise': [batch_size, 64, 64, 24], - 'Conv2d_1_pointwise': [batch_size, 64, 64, 48], - 'Conv2d_2_depthwise': [batch_size, 32, 32, 48], - 'Conv2d_2_pointwise': [batch_size, 32, 32, 96], - 'Conv2d_3_depthwise': [batch_size, 32, 32, 96], - 'Conv2d_3_pointwise': [batch_size, 32, 32, 96], - 'Conv2d_4_depthwise': [batch_size, 16, 16, 96], - 'Conv2d_4_pointwise': [batch_size, 16, 16, 192], - 'Conv2d_5_depthwise': [batch_size, 16, 16, 192], - 'Conv2d_5_pointwise': [batch_size, 16, 16, 192], - 'Conv2d_6_depthwise': [batch_size, 8, 8, 192], - 'Conv2d_6_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_7_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_7_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_8_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_8_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_9_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_9_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_10_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_10_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_11_depthwise': [batch_size, 8, 8, 384], - 'Conv2d_11_pointwise': [batch_size, 8, 8, 384], - 'Conv2d_12_depthwise': [batch_size, 4, 4, 384], - 'Conv2d_12_pointwise': [batch_size, 4, 4, 768], - 'Conv2d_13_depthwise': [batch_size, 4, 4, 768], - 'Conv2d_13_pointwise': [batch_size, 4, 4, 768]} - self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) - for endpoint_name, expected_shape in endpoints_shapes.iteritems(): - 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([slim.conv2d, slim.separable_conv2d], - normalizer_fn=slim.batch_norm): - mobilenet_v1.mobilenet_v1_base(inputs) - total_params, _ = slim.model_analyzer.analyze_vars( - slim.get_model_variables()) - self.assertAlmostEqual(3217920L, 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 = mobilenet_v1.mobilenet_v1(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')] - - _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( - 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 = mobilenet_v1.mobilenet_v1(inputs, num_classes) - - endpoint_keys = [key for key in end_points.keys() - if key.startswith('Mixed') or key.startswith('Conv')] - - _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( - 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): - _ = mobilenet_v1.mobilenet_v1( - inputs, num_classes, depth_multiplier=-0.1) - with self.assertRaises(ValueError): - _ = mobilenet_v1.mobilenet_v1( - 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 = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_13_pointwise'] - 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 = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) - self.assertListEqual(logits.get_shape().as_list(), - [batch_size, num_classes]) - pre_pool = end_points['Conv2d_13_pointwise'] - 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, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes) - self.assertTrue(logits.op.name.startswith('MobilenetV1/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, _ = mobilenet_v1.mobilenet_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 = 150, 150 - num_classes = 1000 - - train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) - mobilenet_v1.mobilenet_v1(train_inputs, num_classes) - eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) - logits, _ = mobilenet_v1.mobilenet_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, _ = mobilenet_v1.mobilenet_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()