Example #1
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    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)
Example #2
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    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(logits, 1)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (eval_batch_size, ))
Example #3
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    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)
Example #4
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    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])
Example #5
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    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])
Example #6
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    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])
Example #7
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    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(logits, 1)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            output = sess.run(predictions)
            self.assertEquals(output.shape, (batch_size, ))
Example #8
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    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))
Example #9
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    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)
Example #10
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 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])