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=44e66446baa42f49e164131eb4c1a97b46a9918d;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;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 new file mode 100755 index 0000000..44e6644 --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/mobilenet_v1_test.py @@ -0,0 +1,450 @@ +# 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()