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_v2."""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 InceptionV2Test(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 = inception.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith( 'InceptionV2/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 = 224, 224 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) net, end_points = inception.inception_v2(inputs, num_classes) self.assertTrue(net.op.name.startswith('InceptionV2/Logits/AvgPool')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024]) self.assertFalse('Logits' in end_points) self.assertFalse('Predictions' in end_points) def testBuildBaseNetwork(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) mixed_5c, end_points = inception.inception_v2_base(inputs) self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c')) self.assertListEqual(mixed_5c.get_shape().as_list(), [batch_size, 7, 7, 1024]) expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', 'MaxPool_3a_3x3'] self.assertItemsEqual(list(end_points.keys()), expected_endpoints) def testBuildOnlyUptoFinalEndpoint(self): batch_size = 5 height, width = 224, 224 endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c'] 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_v2_base( inputs, final_endpoint=endpoint) self.assertTrue(out_tensor.op.name.startswith( 'InceptionV2/' + endpoint)) self.assertItemsEqual(endpoints[:index + 1], list(end_points.keys())) def testBuildAndCheckAllEndPointsUptoMixed5c(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs, final_endpoint='Mixed_5c') endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256], 'Mixed_3c': [batch_size, 28, 28, 320], 'Mixed_4a': [batch_size, 14, 14, 576], 'Mixed_4b': [batch_size, 14, 14, 576], 'Mixed_4c': [batch_size, 14, 14, 576], 'Mixed_4d': [batch_size, 14, 14, 576], 'Mixed_4e': [batch_size, 14, 14, 576], 'Mixed_5a': [batch_size, 7, 7, 1024], 'Mixed_5b': [batch_size, 7, 7, 1024], 'Mixed_5c': [batch_size, 7, 7, 1024], 'Conv2d_1a_7x7': [batch_size, 112, 112, 64], 'MaxPool_2a_3x3': [batch_size, 56, 56, 64], 'Conv2d_2b_1x1': [batch_size, 56, 56, 64], 'Conv2d_2c_3x3': [batch_size, 56, 56, 192], 'MaxPool_3a_3x3': [batch_size, 28, 28, 192]} self.assertItemsEqual( list(endpoints_shapes.keys()), list(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 = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) with slim.arg_scope(inception.inception_v2_arg_scope()): inception.inception_v2_base(inputs) total_params, _ = slim.model_analyzer.analyze_vars( slim.get_model_variables()) self.assertAlmostEqual(10173112, 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 = inception.inception_v2(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_v2( 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 = inception.inception_v2(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_v2( 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): _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1) with self.assertRaises(ValueError): _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0) def testBuildEndPointsWithUseSeparableConvolutionFalse(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] _, end_points_with_replacement = inception.inception_v2_base( inputs, use_separable_conv=False) # The endpoint shapes must be equal to the original shape even when the # separable convolution is replaced with a normal convolution. for key in endpoint_keys: original_shape = end_points[key].get_shape().as_list() self.assertTrue(key in end_points_with_replacement) new_shape = end_points_with_replacement[key].get_shape().as_list() self.assertListEqual(original_shape, new_shape) def testBuildEndPointsNCHWDataFormat(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v2_base(inputs) endpoint_keys = [ key for key in end_points.keys() if key.startswith('Mixed') or key.startswith('Conv') ] inputs_in_nchw = tf.random.uniform((batch_size, 3, height, width)) _, end_points_with_replacement = inception.inception_v2_base( inputs_in_nchw, use_separable_conv=False, data_format='NCHW') # With the 'NCHW' data format, all endpoint activations have a transposed # shape from the original shape with the 'NHWC' layout. for key in endpoint_keys: transposed_original_shape = tf.transpose( a=end_points[key], perm=[0, 3, 1, 2]).get_shape().as_list() self.assertTrue(key in end_points_with_replacement) new_shape = end_points_with_replacement[key].get_shape().as_list() self.assertListEqual(transposed_original_shape, new_shape) def testBuildErrorsForDataFormats(self): batch_size = 5 height, width = 224, 224 inputs = tf.random.uniform((batch_size, height, width, 3)) # 'NCWH' data format is not supported. with self.assertRaises(ValueError): _ = inception.inception_v2_base(inputs, data_format='NCWH') # 'NCHW' data format is not supported for separable convolution. with self.assertRaises(ValueError): _ = inception.inception_v2_base(inputs, data_format='NCHW') 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 = inception.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 4, 4, 1024]) def testBuildBaseNetworkWithoutRootBlock(self): batch_size = 5 height, width = 28, 28 channels = 192 inputs = tf.random.uniform((batch_size, height, width, channels)) _, end_points = inception.inception_v2_base( inputs, include_root_block=False) endpoints_shapes = { 'Mixed_3b': [batch_size, 28, 28, 256], 'Mixed_3c': [batch_size, 28, 28, 320], 'Mixed_4a': [batch_size, 14, 14, 576], 'Mixed_4b': [batch_size, 14, 14, 576], 'Mixed_4c': [batch_size, 14, 14, 576], 'Mixed_4d': [batch_size, 14, 14, 576], 'Mixed_4e': [batch_size, 14, 14, 576], 'Mixed_5a': [batch_size, 7, 7, 1024], 'Mixed_5b': [batch_size, 7, 7, 1024], 'Mixed_5c': [batch_size, 7, 7, 1024] } self.assertItemsEqual( list(endpoints_shapes.keys()), list(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 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 = inception.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] 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 testGlobalPoolUnknownImageShape(self): tf.reset_default_graph() batch_size = 1 height, width = 250, 300 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_v2(inputs, num_classes, global_pool=True) self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_5c'] 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, 10, 1024]) def testUnknowBatchSize(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_v2(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV2/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, _ = inception.inception_v2(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_v2(train_inputs, num_classes) eval_inputs = tf.random.uniform((eval_batch_size, height, width, 3)) logits, _ = inception.inception_v2(eval_inputs, num_classes, 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, 224, 224, 3]) logits, _ = inception.inception_v2(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 = 224, 224 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with slim.arg_scope(inception.inception_v2_arg_scope()): inception.inception_v2(inputs, num_classes, is_training=False) self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), []) def testBatchNormScale(self): height, width = 224, 224 num_classes = 1000 inputs = tf.placeholder(tf.float32, (1, height, width, 3)) with slim.arg_scope( inception.inception_v2_arg_scope(batch_norm_scale=True)): inception.inception_v2(inputs, num_classes, is_training=False) gamma_names = set( v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$')) 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()