Python tensorflow.contrib.slim.python.slim.nets.inception_v3.inception_v3_base() Examples

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) 

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) 

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) 

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) 

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) 

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) 

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) 

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) 

def inception_v3(nlabels, images, pkeep, is_training):

    batch_norm_params = {
        "is_training": is_training,
        "trainable": True,
        # Decay for the moving averages.
        "decay": 0.9997,
        # Epsilon to prevent 0s in variance.
        "epsilon": 0.001,
        # Collection containing the moving mean and moving variance.
        "variables_collections": {
            "beta": None,
            "gamma": None,
            "moving_mean": ["moving_vars"],
            "moving_variance": ["moving_vars"],
        }
    }
    weight_decay = 0.00004
    stddev=0.1
    weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
    with tf.variable_scope("InceptionV3", "InceptionV3", [images]) as scope:

        with tf.contrib.slim.arg_scope(
                [tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
                weights_regularizer=weights_regularizer,
                trainable=True):
            with tf.contrib.slim.arg_scope(
                    [tf.contrib.slim.conv2d],
                    weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                    activation_fn=tf.nn.relu,
                    normalizer_fn=batch_norm,
                    normalizer_params=batch_norm_params):
                net, end_points = inception_v3_base(images, scope=scope)
                with tf.variable_scope("logits"):
                    shape = net.get_shape()
                    net = avg_pool2d(net, shape[1:3], padding="VALID", scope="pool")
                    net = tf.nn.dropout(net, pkeep, name='droplast')
                    net = flatten(net, scope="flatten")
    
    with tf.variable_scope('output') as scope:
        
        weights = tf.Variable(tf.truncated_normal([2048, nlabels], mean=0.0, stddev=0.01), name='weights')
        biases = tf.Variable(tf.constant(0.0, shape=[nlabels], dtype=tf.float32), name='biases')
        output = tf.add(tf.matmul(net, weights), biases, name=scope.name)
        _activation_summary(output)
    return output 

def inception_v3_test(nlabels, images, pkeep, is_training):

    batch_norm_params = {
        "is_training": is_training,
        "trainable": True,
        # Decay for the moving averages.
        "decay": 0.9997,
        # Epsilon to prevent 0s in variance.
        "epsilon": 0.001,
        # Collection containing the moving mean and moving variance.
        "variables_collections": {
            "beta": None,
            "gamma": None,
            "moving_mean": ["moving_vars"],
            "moving_variance": ["moving_vars"],
        }
    }
    weight_decay = 0.00004
    stddev=0.1
    weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
    with tf.variable_scope("InceptionV3", "InceptionV3", [images]) as scope:

        with tf.contrib.slim.arg_scope(
                [tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected],
                weights_regularizer=weights_regularizer,
                trainable=True):
            with tf.contrib.slim.arg_scope(
                    [tf.contrib.slim.conv2d],
                    weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                    activation_fn=tf.nn.relu,
                    normalizer_fn=batch_norm,
                    normalizer_params=batch_norm_params):
                net, end_points = inception_v3_base(images, scope=scope)
                with tf.variable_scope("logits"):
                    shape = net.get_shape()
                    net = avg_pool2d(net, shape[1:3], padding="VALID", scope="pool")
                    net = tf.nn.dropout(net, pkeep, name='droplast')
                    net = flatten(net, scope="flatten")
    
    with tf.variable_scope('output') as scope:
        
        weights = tf.Variable(tf.truncated_normal([2048, nlabels], mean=0.0, stddev=0.01), name='weights')
        biases = tf.Variable(tf.constant(0.0, shape=[nlabels], dtype=tf.float32), name='biases')
        output = tf.add(tf.matmul(net, weights), biases, name=scope.name)
        _activation_summary(output)
    return output,net