Python nets.nets_factory.get_network_fn() Examples

The following are 30 code examples of nets.nets_factory.get_network_fn(). 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 nets.nets_factory , or try the search function .
Example #1
Source File: image_generation.py    From TwinGAN with Apache License 2.0 6 votes vote down vote up 
def prepare_inception_score_classifier(classifier_name, num_classes, images, return_saver=True):
    network_fn = nets_factory.get_network_fn(
      classifier_name,
      num_classes=num_classes,
      weight_decay=0.0,
      is_training=False,
    )
    # Note: you may need to change the prediction_fn here.
    try:
      logits, end_points = network_fn(images, prediction_fn=tf.sigmoid, create_aux_logits=False)
    except TypeError:
      tf.logging.warning('Cannot specify prediction_fn=tf.sigmoid, create_aux_logits=False.')
      logits, end_points = network_fn(images, )

    variables_to_restore = slim.get_model_variables(scope=nets_factory.scopes_map[classifier_name])
    predictions = end_points['Predictions']
    if return_saver:
      saver = tf.train.Saver(variables_to_restore)
      return predictions, end_points, saver
    else:
      return predictions, end_points
Example #2
Source File: export_inference_graph.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #3
Source File: export_inference_graph.py    From edafa with MIT License 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)

    if FLAGS.quantize:
      tf.contrib.quantize.create_eval_graph()

    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #4
Source File: nets_factory_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testGetNetworkFnFirstHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[:10]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        if net not in ['i3d', 's3dg']:
          inputs = tf.random_uniform(
              (batch_size, image_size, image_size, 3))
          logits, end_points = net_fn(inputs)
          self.assertTrue(isinstance(logits, tf.Tensor))
          self.assertTrue(isinstance(end_points, dict))
          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #5
Source File: nets_factory_test.py    From MAX-Image-Segmenter with Apache License 2.0 6 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        if net not in ['i3d', 's3dg']:
          inputs = tf.random_uniform(
              (batch_size, image_size, image_size, 3))
          logits, end_points = net_fn(inputs)
          self.assertTrue(isinstance(logits, tf.Tensor))
          self.assertTrue(isinstance(end_points, dict))
          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #6
Source File: export_inference_graph.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #7
Source File: nets_factory_test.py    From morph-net with Apache License 2.0 6 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        if net not in ['i3d', 's3dg']:
          inputs = tf.random_uniform(
              (batch_size, image_size, image_size, 3))
          logits, end_points = net_fn(inputs)
          self.assertTrue(isinstance(logits, tf.Tensor))
          self.assertTrue(isinstance(end_points, dict))
          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #8
Source File: nets_factory_test.py    From morph-net with Apache License 2.0 6 votes vote down vote up 
def testGetNetworkFnFirstHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[:10]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes=num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        if net not in ['i3d', 's3dg']:
          inputs = tf.random_uniform(
              (batch_size, image_size, image_size, 3))
          logits, end_points = net_fn(inputs)
          self.assertTrue(isinstance(logits, tf.Tensor))
          self.assertTrue(isinstance(end_points, dict))
          self.assertEqual(logits.get_shape().as_list()[0], batch_size)
          self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #9
Source File: export_inference_graph.py    From CVTron with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #10
Source File: export_inference_graph.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #11
Source File: export_inference_graph.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #12
Source File: export_inference_graph.py    From tumblr-emotions with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'validation',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    if hasattr(network_fn, 'default_image_size'):
      image_size = network_fn.default_image_size
    else:
      image_size = FLAGS.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[1, image_size, image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #13
Source File: export_inference_graph.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    if hasattr(network_fn, 'default_image_size'):
      image_size = network_fn.default_image_size
    else:
      image_size = FLAGS.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[1, image_size, image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #14
Source File: style_swap_model.py    From style_swap_tensorflow with Apache License 2.0 6 votes vote down vote up 
def _build_evaluate_model(self):
        self.input_image = tf.placeholder(tf.float32, shape=[None, None, 3])
        self.style_image = tf.placeholder(tf.float32, shape=[None, None, 3])
        preprocess_fn = preprocessing_factory.get_preprocessing(self.config.net_name, is_training=False)

        height = self.evaluate_height if self.evaluate_height else self.PREPROCESS_SIZE
        width = self.evaluate_width if self.evaluate_width else self.PREPROCESS_SIZE

        preprocessed_image = preprocess_fn(self.input_image, height, width, resize_side_min=min(height, width))
        images = tf.expand_dims(preprocessed_image, axis=0)

        style_images = tf.expand_dims(preprocess_fn(self.style_image, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE), axis=0)

        self.swaped_tensor = self._swap_net(images, style_images)

        #
        # network_fn = nets_factory.get_network_fn(self.config.net_name, num_classes=1, is_training=False)
        # _, endpoints_dict = network_fn(images, spatial_squeeze=False)
        # self.swaped_tensor = endpoints_dict[self.config.net_name + self.style_layer]

        self.generated = self._inverse_net(self.swaped_tensor)

        self.evaluate_op = tf.squeeze(self.generated, axis=0)
        self.init_op = self._get_network_init_fn()
        self.save_variables = [var for var in tf.trainable_variables() if var.name.startswith("inverse_net")]
Example #15
Source File: export_inference_graph.py    From ctw-baseline with MIT License 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #16
Source File: export_inference_graph.py    From Creative-Adversarial-Networks with MIT License 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #17
Source File: style_swap_model.py    From style_swap_tensorflow with Apache License 2.0 6 votes vote down vote up 
def _train_inverse(self, generated, swaped_tensor):
        preprocess_fn = preprocessing_factory.get_preprocessing(self.config.net_name, is_training=False)
        network_fn = nets_factory.get_network_fn(self.config.net_name, num_classes=1, is_training=False)
        with tf.variable_scope("", reuse=True):
            preprocessed_image = tf.stack([preprocess_fn(img, self.PREPROCESS_SIZE, self.PREPROCESS_SIZE)
                                           for img in tf.unstack(generated, axis=0)])
            _, inversed_endpoints_dict = network_fn(preprocessed_image, spatial_squeeze=False)
            layer_names = list(inversed_endpoints_dict.keys())
            [layer_name] = [l_name for l_name in layer_names if self.style_layer in l_name]
            inversed_style_layer = inversed_endpoints_dict[layer_name]
        # print(inversed_style_layer.get_shape())
        tf.losses.add_loss(tf.nn.l2_loss(swaped_tensor - inversed_style_layer))
        self.loss_op = tf.losses.get_total_loss()

        train_vars = [var for var in tf.trainable_variables() if var.name.startswith("inverse_net")]
        slim.summarize_tensor(self.loss_op, "loss")
        slim.summarize_tensors(train_vars)
        # print(train_vars)
        self.save_variables = train_vars

        learning_rate = tf.train.exponential_decay(self.config.learning_rate, self.global_step, 1000, 0.66,
                                                   name="learning_rate")
        self.train_op = tf.train.AdamOptimizer(learning_rate).minimize(self.loss_op, self.global_step, train_vars)
Example #18
Source File: export_inference_graph.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    image_size = FLAGS.image_size or network_fn.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[FLAGS.batch_size, image_size,
                                        image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString())
Example #19
Source File: nets_factory_test.py    From Targeted-Adversarial-Attack with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFnArgScope(self):
    batch_size = 5
    num_classes = 10
    net = 'cifarnet'
    with self.test_session(use_gpu=True):
      net_fn = nets_factory.get_network_fn(net, num_classes)
      image_size = getattr(net_fn, 'default_image_size', 224)
      with slim.arg_scope([slim.model_variable, slim.variable],
                          device='/CPU:0'):
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        net_fn(inputs)
      weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0]
      self.assertDeviceEqual('/CPU:0', weights.device)
Example #20
Source File: nets_factory_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFnFirstHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[:10]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #21
Source File: nets_factory_test.py    From Creative-Adversarial-Networks with MIT License 5 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map.keys()[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #22
Source File: nets_factory_test.py    From hops-tensorflow with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFn(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map:
      with self.test_session():
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #23
Source File: nets_factory_test.py    From terngrad with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFn(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map:
      with self.test_session():
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #24
Source File: nets_factory_test.py    From CBAM-tensorflow-slim with MIT License 5 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #25
Source File: nets_factory_test.py    From ICPR_TextDection with GNU General Public License v3.0 5 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #26
Source File: nets_factory_test.py    From ICPR_TextDection with GNU General Public License v3.0 5 votes vote down vote up 
def testGetNetworkFnFirstHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[:10]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #27
Source File: nets_factory_test.py    From 3D-convolutional-speaker-recognition with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFn(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map:
      with self.test_session():
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #28
Source File: nets_factory_test.py    From 3D-convolutional-speaker-recognition with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFn(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map:
      with self.test_session():
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #29
Source File: nets_factory_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFnSecondHalf(self):
    batch_size = 5
    num_classes = 1000
    for net in list(nets_factory.networks_map.keys())[10:]:
      with tf.Graph().as_default() as g, self.test_session(g):
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)
Example #30
Source File: nets_factory_test.py    From 3D-convolutional-speaker-recognition with Apache License 2.0 5 votes vote down vote up 
def testGetNetworkFn(self):
    batch_size = 5
    num_classes = 1000
    for net in nets_factory.networks_map:
      with self.test_session():
        net_fn = nets_factory.get_network_fn(net, num_classes)
        # Most networks use 224 as their default_image_size
        image_size = getattr(net_fn, 'default_image_size', 224)
        inputs = tf.random_uniform((batch_size, image_size, image_size, 3))
        logits, end_points = net_fn(inputs)
        self.assertTrue(isinstance(logits, tf.Tensor))
        self.assertTrue(isinstance(end_points, dict))
        self.assertEqual(logits.get_shape().as_list()[0], batch_size)
        self.assertEqual(logits.get_shape().as_list()[-1], num_classes)