# 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_v4.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from nets import inception class InceptionTest(tf.test.TestCase): def testBuildLogits(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_v4(inputs, num_classes) auxlogits = end_points['AuxLogits'] predictions = end_points['Predictions'] self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits')) self.assertListEqual(auxlogits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue(predictions.op.name.startswith( 'InceptionV4/Logits/Predictions')) self.assertListEqual(predictions.get_shape().as_list(), [batch_size, num_classes]) def testBuildWithoutAuxLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) logits, endpoints = inception.inception_v4(inputs, num_classes, create_aux_logits=False) self.assertFalse('AuxLogits' in endpoints) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) def testAllEndPointsShapes(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v4(inputs, num_classes) 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], 'Mixed_3a': [batch_size, 73, 73, 160], 'Mixed_4a': [batch_size, 71, 71, 192], 'Mixed_5a': [batch_size, 35, 35, 384], # 4 x Inception-A blocks 'Mixed_5b': [batch_size, 35, 35, 384], 'Mixed_5c': [batch_size, 35, 35, 384], 'Mixed_5d': [batch_size, 35, 35, 384], 'Mixed_5e': [batch_size, 35, 35, 384], # Reduction-A block 'Mixed_6a': [batch_size, 17, 17, 1024], # 7 x Inception-B blocks 'Mixed_6b': [batch_size, 17, 17, 1024], 'Mixed_6c': [batch_size, 17, 17, 1024], 'Mixed_6d': [batch_size, 17, 17, 1024], 'Mixed_6e': [batch_size, 17, 17, 1024], 'Mixed_6f': [batch_size, 17, 17, 1024], 'Mixed_6g': [batch_size, 17, 17, 1024], 'Mixed_6h': [batch_size, 17, 17, 1024], # Reduction-A block 'Mixed_7a': [batch_size, 8, 8, 1536], # 3 x Inception-C blocks 'Mixed_7b': [batch_size, 8, 8, 1536], 'Mixed_7c': [batch_size, 8, 8, 1536], 'Mixed_7d': [batch_size, 8, 8, 1536], # Logits and predictions 'AuxLogits': [batch_size, num_classes], 'PreLogitsFlatten': [batch_size, 1536], 'Logits': [batch_size, num_classes], 'Predictions': [batch_size, num_classes]} 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 testBuildBaseNetwork(self): batch_size = 5 height, width = 299, 299 inputs = tf.random_uniform((batch_size, height, width, 3)) net, end_points = inception.inception_v4_base(inputs) self.assertTrue(net.op.name.startswith( 'InceptionV4/Mixed_7d')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536]) expected_endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] self.assertItemsEqual(end_points.keys(), expected_endpoints) for name, op in end_points.iteritems(): self.assertTrue(op.name.startswith('InceptionV4/' + name)) def testBuildOnlyUpToFinalEndpoint(self): batch_size = 5 height, width = 299, 299 all_endpoints = [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] for index, endpoint in enumerate(all_endpoints): with tf.Graph().as_default(): inputs = tf.random_uniform((batch_size, height, width, 3)) out_tensor, end_points = inception.inception_v4_base( inputs, final_endpoint=endpoint) self.assertTrue(out_tensor.op.name.startswith( 'InceptionV4/' + endpoint)) self.assertItemsEqual(all_endpoints[:index+1], end_points) def testVariablesSetDevice(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 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_v4(inputs, num_classes) with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): inception.inception_v4(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 inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v4(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['Mixed_7d'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 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_v4(inputs, num_classes) self.assertTrue(logits.op.name.startswith('InceptionV4/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_v4(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 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_v4(train_inputs, num_classes) eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) logits, _ = inception.inception_v4(eval_inputs, num_classes, is_training=False, reuse=True) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size,)) if __name__ == '__main__': tf.test.main()