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217 lines
9.7 KiB
217 lines
9.7 KiB
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for slim.inception_v4."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from nets import inception
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class InceptionTest(tf.test.TestCase):
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def testBuildLogits(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, end_points = inception.inception_v4(inputs, num_classes)
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auxlogits = end_points['AuxLogits']
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predictions = end_points['Predictions']
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self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits'))
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self.assertListEqual(auxlogits.get_shape().as_list(),
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[batch_size, num_classes])
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self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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self.assertTrue(predictions.op.name.startswith(
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'InceptionV4/Logits/Predictions'))
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self.assertListEqual(predictions.get_shape().as_list(),
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[batch_size, num_classes])
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def testBuildWithoutAuxLogits(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, endpoints = inception.inception_v4(inputs, num_classes,
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create_aux_logits=False)
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self.assertFalse('AuxLogits' in endpoints)
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self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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def testAllEndPointsShapes(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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_, end_points = inception.inception_v4(inputs, num_classes)
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endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
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'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
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'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
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'Mixed_3a': [batch_size, 73, 73, 160],
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'Mixed_4a': [batch_size, 71, 71, 192],
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'Mixed_5a': [batch_size, 35, 35, 384],
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# 4 x Inception-A blocks
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'Mixed_5b': [batch_size, 35, 35, 384],
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'Mixed_5c': [batch_size, 35, 35, 384],
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'Mixed_5d': [batch_size, 35, 35, 384],
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'Mixed_5e': [batch_size, 35, 35, 384],
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# Reduction-A block
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'Mixed_6a': [batch_size, 17, 17, 1024],
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# 7 x Inception-B blocks
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'Mixed_6b': [batch_size, 17, 17, 1024],
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'Mixed_6c': [batch_size, 17, 17, 1024],
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'Mixed_6d': [batch_size, 17, 17, 1024],
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'Mixed_6e': [batch_size, 17, 17, 1024],
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'Mixed_6f': [batch_size, 17, 17, 1024],
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'Mixed_6g': [batch_size, 17, 17, 1024],
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'Mixed_6h': [batch_size, 17, 17, 1024],
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# Reduction-A block
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'Mixed_7a': [batch_size, 8, 8, 1536],
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# 3 x Inception-C blocks
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'Mixed_7b': [batch_size, 8, 8, 1536],
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'Mixed_7c': [batch_size, 8, 8, 1536],
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'Mixed_7d': [batch_size, 8, 8, 1536],
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# Logits and predictions
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'AuxLogits': [batch_size, num_classes],
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'PreLogitsFlatten': [batch_size, 1536],
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'Logits': [batch_size, num_classes],
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'Predictions': [batch_size, num_classes]}
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self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
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for endpoint_name in endpoints_shapes:
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expected_shape = endpoints_shapes[endpoint_name]
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self.assertTrue(endpoint_name in end_points)
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self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
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expected_shape)
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def testBuildBaseNetwork(self):
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batch_size = 5
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height, width = 299, 299
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inputs = tf.random_uniform((batch_size, height, width, 3))
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net, end_points = inception.inception_v4_base(inputs)
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self.assertTrue(net.op.name.startswith(
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'InceptionV4/Mixed_7d'))
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self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536])
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expected_endpoints = [
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'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
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'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
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'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
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'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
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'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
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self.assertItemsEqual(end_points.keys(), expected_endpoints)
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for name, op in end_points.iteritems():
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self.assertTrue(op.name.startswith('InceptionV4/' + name))
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def testBuildOnlyUpToFinalEndpoint(self):
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batch_size = 5
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height, width = 299, 299
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all_endpoints = [
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'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
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'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
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'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
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'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
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'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
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for index, endpoint in enumerate(all_endpoints):
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with tf.Graph().as_default():
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inputs = tf.random_uniform((batch_size, height, width, 3))
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out_tensor, end_points = inception.inception_v4_base(
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inputs, final_endpoint=endpoint)
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self.assertTrue(out_tensor.op.name.startswith(
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'InceptionV4/' + endpoint))
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self.assertItemsEqual(all_endpoints[:index+1], end_points)
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def testVariablesSetDevice(self):
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batch_size = 5
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height, width = 299, 299
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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# Force all Variables to reside on the device.
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with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
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inception.inception_v4(inputs, num_classes)
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with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
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inception.inception_v4(inputs, num_classes)
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for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_cpu'):
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self.assertDeviceEqual(v.device, '/cpu:0')
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for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_gpu'):
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self.assertDeviceEqual(v.device, '/gpu:0')
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def testHalfSizeImages(self):
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batch_size = 5
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height, width = 150, 150
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num_classes = 1000
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inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, end_points = inception.inception_v4(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[batch_size, num_classes])
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pre_pool = end_points['Mixed_7d']
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self.assertListEqual(pre_pool.get_shape().as_list(),
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[batch_size, 3, 3, 1536])
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def testUnknownBatchSize(self):
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batch_size = 1
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height, width = 299, 299
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num_classes = 1000
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with self.test_session() as sess:
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inputs = tf.placeholder(tf.float32, (None, height, width, 3))
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logits, _ = inception.inception_v4(inputs, num_classes)
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self.assertTrue(logits.op.name.startswith('InceptionV4/Logits'))
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self.assertListEqual(logits.get_shape().as_list(),
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[None, num_classes])
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images = tf.random_uniform((batch_size, height, width, 3))
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sess.run(tf.initialize_all_variables())
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output = sess.run(logits, {inputs: images.eval()})
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self.assertEquals(output.shape, (batch_size, num_classes))
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def testEvaluation(self):
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batch_size = 2
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height, width = 299, 299
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num_classes = 1000
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with self.test_session() as sess:
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eval_inputs = tf.random_uniform((batch_size, height, width, 3))
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logits, _ = inception.inception_v4(eval_inputs,
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num_classes,
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is_training=False)
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predictions = tf.argmax(logits, 1)
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sess.run(tf.initialize_all_variables())
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output = sess.run(predictions)
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self.assertEquals(output.shape, (batch_size,))
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def testTrainEvalWithReuse(self):
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train_batch_size = 5
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eval_batch_size = 2
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height, width = 150, 150
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num_classes = 1000
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with self.test_session() as sess:
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train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
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inception.inception_v4(train_inputs, num_classes)
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eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
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logits, _ = inception.inception_v4(eval_inputs,
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num_classes,
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is_training=False,
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reuse=True)
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predictions = tf.argmax(logits, 1)
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sess.run(tf.initialize_all_variables())
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output = sess.run(predictions)
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self.assertEquals(output.shape, (eval_batch_size,))
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if __name__ == '__main__':
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tf.test.main()
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