Python inception_v3.inception_v3方法代碼示例

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testBuildBaseNetwork(self):
    batch_size = 5
    height, width = 299, 299

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    final_endpoint, end_points = inception_v3.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) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
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 ops.Graph().as_default():
        inputs = random_ops.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception_v3.inception_v3_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(
            out_tensor.op.name.startswith('InceptionV3/' + endpoint))
        self.assertItemsEqual(endpoints[:index + 1], end_points) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    _, end_points = inception_v3.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_v3.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) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    _, end_points = inception_v3.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_v3.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) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testUnknownImageShape(self):
    ops.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 = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v3.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}
      variables.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]) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = random_ops.random_uniform(
        (train_batch_size, height, width, 3))
    inception_v3.inception_v3(train_inputs, num_classes)
    eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception_v3.inception_v3(
        eval_inputs, num_classes, is_training=False, reuse=True)
    predictions = math_ops.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,)) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def build_graph(self):
    """Forms the core by building a wrapper around the inception graph.

      Here we add the necessary input & output tensors, to decode jpegs,
      serialize embeddings, restore from checkpoint etc.

      To use other Inception models modify this file. Note that to use other
      models beside Inception, you should make sure input_shape matches
      their input. Resizing or other modifications may be necessary as well.
      See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
      details about InceptionV3.

    Returns:
      input_jpeg: A tensor containing raw image bytes as the input layer.
      embedding: The embeddings tensor, that will be materialized later.
    """

    input_jpeg = tf.placeholder(tf.string, shape=None)
    image = tf.image.decode_jpeg(input_jpeg, channels=self.CHANNELS)

    # Note resize expects a batch_size, but we are feeding a single image.
    # So we have to expand then squeeze.  Resize returns float32 in the
    # range [0, uint8_max]
    image = tf.expand_dims(image, 0)

    # convert_image_dtype also scales [0, uint8_max] -> [0 ,1).
    image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    image = tf.image.resize_bilinear(
        image, [self.HEIGHT, self.WIDTH], align_corners=False)

    # Then rescale range to [-1, 1) for Inception.
    image = tf.subtract(image, 0.5)
    inception_input = tf.multiply(image, 2.0)

    # Build Inception layers, which expect a tensor of type float from [-1, 1)
    # and shape [batch_size, height, width, channels].
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      _, end_points = inception.inception_v3(inception_input, is_training=False)

    embedding = end_points['PreLogits']
    return input_jpeg, embedding 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testBuildClassificationNetwork(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    logits, end_points = inception_v3.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    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]) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testBuildAndCheckAllEndPointsUptoMixed7c(self):
    batch_size = 5
    height, width = 299, 299

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    _, end_points = inception_v3.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) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testModelHasExpectedNumberOfParameters(self):
    batch_size = 5
    height, width = 299, 299
    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    with arg_scope(inception_v3.inception_v3_arg_scope()):
      inception_v3.inception_v3_base(inputs)
    total_params, _ = model_analyzer.analyze_vars(
        variables_lib.get_model_variables())
    self.assertAlmostEqual(21802784, total_params) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testRaiseValueErrorWithInvalidDepthMultiplier(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    with self.assertRaises(ValueError):
      _ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
    with self.assertRaises(ValueError):
      _ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=0.0) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testHalfSizeImages(self):
    batch_size = 5
    height, width = 150, 150
    num_classes = 1000

    inputs = random_ops.random_uniform((batch_size, height, width, 3))
    logits, end_points = inception_v3.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]) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testEvaluation(self):
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000

    eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
    logits, _ = inception_v3.inception_v3(
        eval_inputs, num_classes, is_training=False)
    predictions = math_ops.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,)) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def testLogitsNotSqueezed(self):
    num_classes = 25
    images = random_ops.random_uniform([1, 299, 299, 3])
    logits, _ = inception_v3.inception_v3(
        images, num_classes=num_classes, spatial_squeeze=False)

    with self.test_session() as sess:
      variables.global_variables_initializer().run()
      logits_out = sess.run(logits)
      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 別名]
def network_inception_v3():
    input_shape = [1, 224, 224, 3]
    input_ = tf.placeholder(dtype=tf.float32, name='input', shape=input_shape)
    net, _end_points = inception_v3(input_, num_classes=1000, is_training=False)
    return net