Python pnasnet.pnasnet_large_arg_scope方法代码示例

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testBuildLogitsLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
    auxlogits = end_points['AuxLogits']
    predictions = end_points['Predictions']
    self.assertListEqual(auxlogits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertListEqual(predictions.get_shape().as_list(),
                         [batch_size, num_classes]) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
  """Defines the default arg scope for the PNASNet Large for object detection.

  This provides a small edit to switch batch norm training on and off.

  Args:
    is_batch_norm_training: Boolean indicating whether to train with batch norm.
    Default is False.

  Returns:
    An `arg_scope` to use for the PNASNet Large Model.
  """
  imagenet_scope = pnasnet.pnasnet_large_arg_scope()
  with slim.arg_scope(imagenet_scope):
    with slim.arg_scope([slim.batch_norm],
                        is_training=is_batch_norm_training) as sc:
      return sc 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testBuildLogitsLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = 1000
    inputs = tf.random.uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
    auxlogits = end_points['AuxLogits']
    predictions = end_points['Predictions']
    self.assertListEqual(auxlogits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertListEqual(predictions.get_shape().as_list(),
                         [batch_size, num_classes]) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testBuildNonExistingLayerLargeModel(self):
    """Tests that the model is built correctly without unnecessary layers."""
    inputs = tf.random_uniform((5, 331, 331, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      pnasnet.build_pnasnet_large(inputs, 1000)
    vars_names = [x.op.name for x in tf.trainable_variables()]
    self.assertIn('cell_stem_0/1x1/weights', vars_names)
    self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testBuildPreLogitsLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = None
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      net, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
    self.assertFalse('AuxLogits' in end_points)
    self.assertFalse('Predictions' in end_points)
    self.assertTrue(net.op.name.startswith('final_layer/Mean'))
    self.assertListEqual(net.get_shape().as_list(), [batch_size, 4320]) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testAllEndPointsShapesLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      _, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)

    endpoints_shapes = {'Stem': [batch_size, 42, 42, 540],
                        'Cell_0': [batch_size, 42, 42, 1080],
                        'Cell_1': [batch_size, 42, 42, 1080],
                        'Cell_2': [batch_size, 42, 42, 1080],
                        'Cell_3': [batch_size, 42, 42, 1080],
                        'Cell_4': [batch_size, 21, 21, 2160],
                        'Cell_5': [batch_size, 21, 21, 2160],
                        'Cell_6': [batch_size, 21, 21, 2160],
                        'Cell_7': [batch_size, 21, 21, 2160],
                        'Cell_8': [batch_size, 11, 11, 4320],
                        'Cell_9': [batch_size, 11, 11, 4320],
                        'Cell_10': [batch_size, 11, 11, 4320],
                        'Cell_11': [batch_size, 11, 11, 4320],
                        'global_pool': [batch_size, 4320],
                        # Logits and predictions
                        'AuxLogits': [batch_size, 1000],
                        'Predictions': [batch_size, 1000],
                        'Logits': [batch_size, 1000],
                       }
    self.assertEqual(len(end_points), 17)
    self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
    for endpoint_name in endpoints_shapes:
      tf.logging.info('Endpoint name: {}'.format(endpoint_name))
      expected_shape = endpoints_shapes[endpoint_name]
      self.assertIn(endpoint_name, end_points)
      self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
                           expected_shape) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testNoAuxHeadLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = 1000
    for use_aux_head in (True, False):
      tf.reset_default_graph()
      inputs = tf.random_uniform((batch_size, height, width, 3))
      tf.train.create_global_step()
      config = pnasnet.large_imagenet_config()
      config.set_hparam('use_aux_head', int(use_aux_head))
      with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
        _, end_points = pnasnet.build_pnasnet_large(inputs, num_classes,
                                                    config=config)
      self.assertEqual('AuxLogits' in end_points, use_aux_head) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
  """Defines the default arg scope for the PNASNet Large for object detection.

  This provides a small edit to switch batch norm training on and off.

  Args:
    is_batch_norm_training: Boolean indicating whether to train with batch norm.

  Returns:
    An `arg_scope` to use for the PNASNet Large Model.
  """
  imagenet_scope = pnasnet.pnasnet_large_arg_scope()
  with arg_scope(imagenet_scope):
    with arg_scope([slim.batch_norm], is_training=is_batch_norm_training) as sc:
      return sc 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_large_arg_scope [as 别名]
def testOverrideHParamsLargeModel(self):
    batch_size = 5
    height, width = 331, 331
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    config = pnasnet.large_imagenet_config()
    config.set_hparam('data_format', 'NCHW')
    with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
      _, end_points = pnasnet.build_pnasnet_large(
          inputs, num_classes, config=config)
    self.assertListEqual(
        end_points['Stem'].shape.as_list(), [batch_size, 540, 42, 42])