Python tensorflow.contrib.slim.nets.resnet_v1.resnet_v1_50() Examples

The following are 6 code examples of tensorflow.contrib.slim.nets.resnet_v1.resnet_v1_50(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.contrib.slim.nets.resnet_v1 , or try the search function .
Example #1
Source File: test_gradcam_dangling.py    From darkon with Apache License 2.0 6 votes vote down vote up 
def setUp(self):
        tf.reset_default_graph()

        self.nbclasses = 1000
        inputs = tf.placeholder(tf.float32, [1, 224, 224, 3])
        with slim.arg_scope(resnet_v1.resnet_arg_scope()):
            net, end_points = resnet_v1.resnet_v1_50(inputs, self.nbclasses, is_training=False)
        saver = tf.train.Saver(tf.global_variables())
        check_point = 'test/data/resnet_v1_50.ckpt'

        sess = tf.InteractiveSession()
        saver.restore(sess, check_point)

        conv_name = 'resnet_v1_50/block4/unit_3/bottleneck_v1/Relu'
        
        self.graph_origin = tf.get_default_graph().as_graph_def()
        self.insp = darkon.Gradcam(inputs, self.nbclasses, conv_name)
        self.sess = sess
Example #2
Source File: models.py    From Multimodal-Emotion-Recognition with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def video_model(video_frames=None, audio_frames=None):
    """Creates the video model.
    
    Args:
        video_frames: A tensor that contains the video input.
        audio_frames: not needed (leave None).
    Returns:
        The video model.
    """

    with tf.variable_scope("video_model"):
        batch_size, seq_length, height, width, channels = video_frames.get_shape().as_list()

        video_input = tf.reshape(video_frames, (batch_size * seq_length, height, width, channels))
        video_input = tf.cast(video_input, tf.float32)

        features, end_points = resnet_v1.resnet_v1_50(video_input, None)
        features = tf.reshape(features, (batch_size, seq_length, int(features.get_shape()[3])))

    return features
Example #3
Source File: det_lesion.py    From liverseg-2017-nipsws with MIT License 6 votes vote down vote up 
def det_lesion_resnet(inputs, is_training_option=False, scope='det_lesion'):
    """Defines the network
    Args:
    inputs: Tensorflow placeholder that contains the input image
    scope: Scope name for the network
    Returns:
    net: Output Tensor of the network
    end_points: Dictionary with all Tensors of the network
    """

    with tf.variable_scope(scope, 'det_lesion', [inputs]) as sc:
        end_points_collection = sc.name + '_end_points'
        with slim.arg_scope(resnet_v1.resnet_arg_scope()):

            net, end_points = resnet_v1.resnet_v1_50(inputs, is_training=is_training_option)
            net = slim.flatten(net, scope='flatten5')
            net = slim.fully_connected(net, 1, activation_fn=tf.nn.sigmoid,
                                       weights_initializer=initializers.xavier_initializer(), scope='output')
            utils.collect_named_outputs(end_points_collection, 'det_lesion/output', net)

    end_points = slim.utils.convert_collection_to_dict(end_points_collection)
    return net, end_points
Example #4
Source File: det_lesion.py    From liverseg-2017-nipsws with MIT License 6 votes vote down vote up 
def load_resnet_imagenet(ckpt_path):
    """Initialize the network parameters from the Resnet-50 pre-trained model provided by TF-SLIM
    Args:
    Path to the checkpoint
    Returns:
    Function that takes a session and initializes the network
    """
    reader = tf.train.NewCheckpointReader(ckpt_path)
    var_to_shape_map = reader.get_variable_to_shape_map()
    vars_corresp = dict()
    
    for v in var_to_shape_map:
        if "bottleneck_v1" in v or "conv1" in v:
            vars_corresp[v] = slim.get_model_variables(v.replace("resnet_v1_50", "det_lesion/resnet_v1_50"))[0]
    init_fn = slim.assign_from_checkpoint_fn(ckpt_path, vars_corresp)
    return init_fn
Example #5
Source File: test_gradcam.py    From darkon with Apache License 2.0 5 votes vote down vote up 
def test_resnet(self):
        with slim.arg_scope(resnet_v1.resnet_arg_scope()):
            net, end_points = resnet_v1.resnet_v1_50(self.inputs, self.nbclasses, is_training=False)
        saver = tf.train.Saver(tf.global_variables())
        check_point = 'test/data/resnet_v1_50.ckpt'
        sess = tf.InteractiveSession()
        saver.restore(sess, check_point)

        self.sess = sess
        self.graph_origin = tf.get_default_graph()
        self.target_op_name = darkon.Gradcam.candidate_featuremap_op_names(sess, self.graph_origin)[-1]
        self.model_name = 'resnet'
        
        self.assertEqual('resnet_v1_50/block4/unit_3/bottleneck_v1/Relu', self.target_op_name)
Example #6
Source File: saliency-maps.py    From tensorpack with Apache License 2.0 5 votes vote down vote up 
def build_graph(self, orig_image):
        mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
        with guided_relu():
            with slim.arg_scope(resnet_v1.resnet_arg_scope()):
                image = tf.expand_dims(orig_image - mean, 0)
                logits, _ = resnet_v1.resnet_v1_50(image, 1000, is_training=False)
            saliency_map(logits, orig_image, name="saliency")