Python pnasnet.pnasnet_mobile_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_mobile_arg_scope [as 别名]
def testBuildLogitsMobileModel(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
      logits, end_points = pnasnet.build_pnasnet_mobile(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_mobile_arg_scope [as 别名]
def testBuildLogitsMobileModel(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    inputs = tf.random.uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
      logits, end_points = pnasnet.build_pnasnet_mobile(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_mobile_arg_scope [as 别名]
def testBuildNonExistingLayerMobileModel(self):
    """Tests that the model is built correctly without unnecessary layers."""
    inputs = tf.random_uniform((5, 224, 224, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
      pnasnet.build_pnasnet_mobile(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_mobile_arg_scope [as 别名]
def testBuildPreLogitsMobileModel(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = None
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
      net, end_points = pnasnet.build_pnasnet_mobile(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, 1080]) 

# 需要导入模块: from nets.nasnet import pnasnet [as 别名]
# 或者: from nets.nasnet.pnasnet import pnasnet_mobile_arg_scope [as 别名]
def testAllEndPointsShapesMobileModel(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000
    inputs = tf.random_uniform((batch_size, height, width, 3))
    tf.train.create_global_step()
    with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
      _, end_points = pnasnet.build_pnasnet_mobile(inputs, num_classes)

    endpoints_shapes = {
        'Stem': [batch_size, 28, 28, 135],
        'Cell_0': [batch_size, 28, 28, 270],
        'Cell_1': [batch_size, 28, 28, 270],
        'Cell_2': [batch_size, 28, 28, 270],
        'Cell_3': [batch_size, 14, 14, 540],
        'Cell_4': [batch_size, 14, 14, 540],
        'Cell_5': [batch_size, 14, 14, 540],
        'Cell_6': [batch_size, 7, 7, 1080],
        'Cell_7': [batch_size, 7, 7, 1080],
        'Cell_8': [batch_size, 7, 7, 1080],
        'global_pool': [batch_size, 1080],
        # Logits and predictions
        'AuxLogits': [batch_size, num_classes],
        'Predictions': [batch_size, num_classes],
        'Logits': [batch_size, num_classes],
    }
    self.assertEqual(len(end_points), 14)
    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_mobile_arg_scope [as 别名]
def testNoAuxHeadMobileModel(self):
    batch_size = 5
    height, width = 224, 224
    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.mobile_imagenet_config()
      config.set_hparam('use_aux_head', int(use_aux_head))
      with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
        _, end_points = pnasnet.build_pnasnet_mobile(
            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_mobile_arg_scope [as 别名]
def testUseBoundedAcitvationMobileModel(self):
    batch_size = 1
    height, width = 224, 224
    num_classes = 1000
    for use_bounded_activation in (True, False):
      tf.reset_default_graph()
      inputs = tf.random_uniform((batch_size, height, width, 3))
      config = pnasnet.mobile_imagenet_config()
      config.set_hparam('use_bounded_activation', use_bounded_activation)
      with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()):
        _, _ = pnasnet.build_pnasnet_mobile(
            inputs, num_classes, config=config)
      for node in tf.get_default_graph().as_graph_def().node:
        if node.op.startswith('Relu'):
          self.assertEqual(node.op == 'Relu6', use_bounded_activation)