Transfer learning on TensorFlow-Slim image classification model library
# 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_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tfimport tf_slim as slimfrom nets import inceptionclass 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 testBuildNoClasses(self): batch_size = 5 height, width = 299, 299 num_classes = None with self.test_session(): inputs = tf.random.uniform((batch_size, height, width, 3)) net, endpoints = inception.inception_resnet_v2(inputs, num_classes) self.assertTrue('AuxLogits' not in endpoints) self.assertTrue('Logits' not in endpoints) self.assertTrue( net.op.name.startswith('InceptionResnetV2/Logits/AvgPool')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1536]) 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.keys()) 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 testGlobalPool(self): batch_size = 1 height, width = 330, 400 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, 8, 11, 1536]) def testGlobalPoolUnknownImageShape(self): batch_size = 1 height, width = 330, 400 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3)) logits, end_points = inception.inception_resnet_v2( inputs, num_classes, create_aux_logits=False) 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'] images = tf.random.uniform((batch_size, height, width, 3)) sess.run(tf.global_variables_initializer()) logits_out, pre_pool_out = sess.run([logits, pre_pool], {inputs: images.eval()}) self.assertTupleEqual(logits_out.shape, (batch_size, num_classes)) self.assertTupleEqual(pre_pool_out.shape, (batch_size, 8, 11, 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(input=logits, axis=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(input=logits, axis=1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size,)) def testNoBatchNormScaleByDefault(self): height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with slim.arg_scope(inception.inception_resnet_v2_arg_scope()): inception.inception_resnet_v2(inputs, num_classes, is_training=False) self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), []) def testBatchNormScale(self): height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with slim.arg_scope( inception.inception_resnet_v2_arg_scope(batch_norm_scale=True)): inception.inception_resnet_v2(inputs, num_classes, is_training=False) gamma_names = set( v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$')) self.assertGreater(len(gamma_names), 0) for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'): self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)if __name__ == '__main__': tf.test.main()