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 nets.inception_v1."""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport numpy as npimport tensorflow.compat.v1 as tfimport tf_slim as slimfrom nets import inceptionclass InceptionV3Test(tf.test.TestCase): def testBuildClassificationNetwork(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith( 'InceptionV3/Logits/SpatialSqueeze')) 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 testBuildPreLogitsNetwork(self): batch_size = 5 height, width = 299, 299 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) net, end_points = inception.inception_v3(inputs, num_classes) self.assertTrue(net.op.name.startswith('InceptionV3/Logits/AvgPool')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 2048]) self.assertFalse('Logits' in end_points) self.assertFalse('Predictions' in end_points) def testBuildBaseNetwork(self): batch_size = 5 height, width = 299, 299 inputs = tf.random.uniform((batch_size, height, width, 3)) final_endpoint, end_points = inception.inception_v3_base(inputs) self.assertTrue(final_endpoint.op.name.startswith( 'InceptionV3/Mixed_7c')) self.assertListEqual(final_endpoint.get_shape().as_list(), [batch_size, 8, 8, 2048]) 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_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] 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_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] 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_v3_base( inputs, final_endpoint=endpoint) self.assertTrue(out_tensor.op.name.startswith( 'InceptionV3/' + endpoint)) self.assertItemsEqual(endpoints[:index + 1], end_points.keys()) def testBuildAndCheckAllEndPointsUptoMixed7c(self): batch_size = 5 height, width = 299, 299 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v3_base( inputs, final_endpoint='Mixed_7c') endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32], 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], 'MaxPool_3a_3x3': [batch_size, 73, 73, 64], 'Conv2d_3b_1x1': [batch_size, 73, 73, 80], 'Conv2d_4a_3x3': [batch_size, 71, 71, 192], 'MaxPool_5a_3x3': [batch_size, 35, 35, 192], 'Mixed_5b': [batch_size, 35, 35, 256], 'Mixed_5c': [batch_size, 35, 35, 288], 'Mixed_5d': [batch_size, 35, 35, 288], 'Mixed_6a': [batch_size, 17, 17, 768], 'Mixed_6b': [batch_size, 17, 17, 768], 'Mixed_6c': [batch_size, 17, 17, 768], 'Mixed_6d': [batch_size, 17, 17, 768], 'Mixed_6e': [batch_size, 17, 17, 768], 'Mixed_7a': [batch_size, 8, 8, 1280], 'Mixed_7b': [batch_size, 8, 8, 2048], 'Mixed_7c': [batch_size, 8, 8, 2048]} 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 testModelHasExpectedNumberOfParameters(self): batch_size = 5 height, width = 299, 299 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope(inception.inception_v3_arg_scope()): inception.inception_v3_base(inputs) total_params, _ = slim.model_analyzer.analyze_vars( slim.get_model_variables()) self.assertAlmostEqual(21802784, total_params) def testBuildEndPoints(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v3(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]) self.assertTrue('Mixed_7c' in end_points) pre_pool = end_points['Mixed_7c'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 8, 2048]) self.assertTrue('PreLogits' in end_points) pre_logits = end_points['PreLogits'] self.assertListEqual(pre_logits.get_shape().as_list(), [batch_size, 1, 1, 2048]) def testBuildEndPointsWithDepthMultiplierLessThanOne(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v3(inputs, num_classes) endpoint_keys = [key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv')] _, end_points_with_multiplier = inception.inception_v3( 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 = 299, 299 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v3(inputs, num_classes) endpoint_keys = [key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv')] _, end_points_with_multiplier = inception.inception_v3( 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 = 299, 299 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) with self.assertRaises(ValueError): _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1) with self.assertRaises(ValueError): _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0) def testHalfSizeImages(self): batch_size = 5 height, width = 150, 150 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 2048]) def testUnknownImageShape(self): tf.reset_default_graph() batch_size = 2 height, width = 299, 299 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 = inception.inception_v3(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] 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, 8, 8, 2048]) def testGlobalPoolUnknownImageShape(self): tf.reset_default_graph() batch_size = 1 height, width = 330, 400 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 = inception.inception_v3(inputs, num_classes, global_pool=True) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7c'] 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, 8, 11, 2048]) def testUnknowBatchSize(self): batch_size = 1 height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV3/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 = 299, 299 num_classes = 1000 eval_inputs = tf.random.uniform((batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(input=logits, axis=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)) inception.inception_v3(train_inputs, num_classes) eval_inputs = tf.random.uniform((eval_batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False, reuse=True) predictions = tf.argmax(input=logits, axis=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, 299, 299, 3]) logits, _ = inception.inception_v3(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]) 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_v3_arg_scope()): inception.inception_v3(inputs, num_classes, is_training=False) self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), []) def testBatchNormScale(self): height, width = 299, 299 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with slim.arg_scope( inception.inception_v3_arg_scope(batch_norm_scale=True)): inception.inception_v3(inputs, num_classes, is_training=False) gamma_names = set( v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$')) 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()