TypeError: data type not understood

[RitikaGupta8734]

# adjust this to point to your downloaded/trained model
# models can be downloaded here: https://github.com/fizyr/keras-retinanet/releases
model_path = os.path.join('/home/ritika/Downloads/new_downloads/1/resnet152_oid_v1.0.0.h5')

# load retinanet model
model = models.load_model(model_path, backbone_name='resnet50')

# if the model is not converted to an inference model, use the line below
# see: https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model
#model = models.convert_model(model)

# print(model.summary())

# load label to names mapping for visualization purposes
labels_to_names = {0: 'Biker', 1: 'Car', 2: 'Bus', 3: 'Cart', 4: 'Skater', 5: 'Pedestrian'}
# labels_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}

Running this part of code in ResNet50RetinaNet-Video is giving me an error.

TypeError Traceback (most recent call last)
in
4
5 # load retinanet model
----> 6 model = models.load_model(model_path, backbone_name='resnet50')
7
8 # if the model is not converted to an inference model, use the line below

~/Downloads/new_downloads/1/keras_retinanet/models/init.py in load_model(filepath, backbone_name)
81 """
82 import keras.models
---> 83 return keras.models.load_model(filepath, custom_objects=backbone(backbone_name).custom_objects)
84
85

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile)
182 if (h5py is not None and (
183 isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
--> 184 return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
185
186 if sys.version_info >= (3, 4) and isinstance(filepath, pathlib.Path):

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py in load_model_from_hdf5(filepath, custom_objects, compile)
176 model_config = json.loads(model_config.decode('utf-8'))
177 model = model_config_lib.model_from_config(model_config,
--> 178 custom_objects=custom_objects)
179
180 # set weights

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/saving/model_config.py in model_from_config(config, custom_objects)
53 'Sequential.from_config(config)?')
54 from tensorflow.python.keras.layers import deserialize # pylint: disable=g-import-not-at-top
---> 55 return deserialize(config, custom_objects=custom_objects)
56
57

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
107 module_objects=globs,
108 custom_objects=custom_objects,
--> 109 printable_module_name='layer')

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
371 custom_objects=dict(
372 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 373 list(custom_objects.items())))
374 with CustomObjectScope(custom_objects):
375 return cls.from_config(cls_config)

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in from_config(cls, config, custom_objects)
985 """
986 input_tensors, output_tensors, created_layers = reconstruct_from_config(
--> 987 config, custom_objects)
988 model = cls(inputs=input_tensors, outputs=output_tensors,
989 name=config.get('name'))

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in reconstruct_from_config(config, custom_objects, created_layers)
2017 # First, we create all layers and enqueue nodes to be processed
2018 for layer_data in config['layers']:
-> 2019 process_layer(layer_data)
2020 # Then we process nodes in order of layer depth.
2021 # Nodes that cannot yet be processed (if the inbound node

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in process_layer(layer_data)
1999 from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
2000
-> 2001 layer = deserialize_layer(layer_data, custom_objects=custom_objects)
2002 created_layers[layer_name] = layer
2003

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
107 module_objects=globs,
108 custom_objects=custom_objects,
--> 109 printable_module_name='layer')

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
371 custom_objects=dict(
372 list(_GLOBAL_CUSTOM_OBJECTS.items()) +
--> 373 list(custom_objects.items())))
374 with CustomObjectScope(custom_objects):
375 return cls.from_config(cls_config)

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in from_config(cls, config, custom_objects)
985 """
986 input_tensors, output_tensors, created_layers = reconstruct_from_config(
--> 987 config, custom_objects)
988 model = cls(inputs=input_tensors, outputs=output_tensors,
989 name=config.get('name'))

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in reconstruct_from_config(config, custom_objects, created_layers)
2027 if layer in unprocessed_nodes:
2028 for node_data in unprocessed_nodes.pop(layer):
-> 2029 process_node(layer, node_data)
2030
2031 input_tensors = []

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py in process_node(layer, node_data)
1975 if input_tensors is not None:
1976 input_tensors = base_layer_utils.unnest_if_single_tensor(input_tensors)
-> 1977 output_tensors = layer(input_tensors, **kwargs)
1978
1979 # Update node index map.

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in call(self, *args, **kwargs)
895 # Build layer if applicable (if the build method has been
896 # overridden).
--> 897 self._maybe_build(inputs)
898 cast_inputs = self._maybe_cast_inputs(inputs)
899

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self, inputs)
2414 # operations.
2415 with tf_utils.maybe_init_scope(self):
-> 2416 self.build(input_shapes) # pylint:disable=not-callable
2417 # We must set also ensure that the layer is marked as built, and the build
2418 # shape is stored since user defined build functions may not be calling

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py in build(self, input_shape)
170 constraint=self.bias_constraint,
171 trainable=True,
--> 172 dtype=self.dtype)
173 else:
174 self.bias = None

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, synchronization, aggregation, **kwargs)
575 synchronization=synchronization,
576 aggregation=aggregation,
--> 577 caching_device=caching_device)
578 if regularizer is not None:
579 # TODO(fchollet): in the future, this should be handled at the

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/training/tracking/base.py in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
741 dtype=dtype,
742 initializer=initializer,
--> 743 **kwargs_for_getter)
744
745 # If we set an initializer and the variable processed it, tracking will not

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer_utils.py in make_variable(name, shape, dtype, initializer, trainable, caching_device, validate_shape, constraint, use_resource, collections, synchronization, aggregation, partitioner)
139 synchronization=synchronization,
140 aggregation=aggregation,
--> 141 shape=variable_shape if variable_shape else None)
142
143

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in call(cls, *args, **kwargs)
257 def call(cls, *args, **kwargs):
258 if cls is VariableV1:
--> 259 return cls._variable_v1_call(*args, **kwargs)
260 elif cls is Variable:
261 return cls._variable_v2_call(*args, **kwargs)

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in _variable_v1_call(cls, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope, constraint, use_resource, synchronization, aggregation, shape)
218 synchronization=synchronization,
219 aggregation=aggregation,
--> 220 shape=shape)
221
222 def _variable_v2_call(cls,

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in (**kwargs)
196 shape=None):
197 """Call on Variable class. Useful to force the signature."""
--> 198 previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
199 for _, getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
200 previous_getter = _make_getter(getter, previous_getter)

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py in default_variable_creator(next_creator, **kwargs)
2596 synchronization=synchronization,
2597 aggregation=aggregation,
-> 2598 shape=shape)
2599 else:
2600 return variables.RefVariable(

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/variables.py in call(cls, *args, **kwargs)
261 return cls._variable_v2_call(*args, **kwargs)
262 else:
--> 263 return super(VariableMetaclass, cls).call(*args, **kwargs)
264
265

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py in init(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)
1432 aggregation=aggregation,
1433 shape=shape,
-> 1434 distribute_strategy=distribute_strategy)
1435
1436 def _init_from_args(self,

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)
1565 with ops.name_scope("Initializer"), device_context_manager(None):
1566 initial_value = ops.convert_to_tensor(
-> 1567 initial_value() if init_from_fn else initial_value,
1568 name="initial_value", dtype=dtype)
1569 if shape is not None:

~/anaconda3/envs/myenv/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer_utils.py in ()
119 (type(init_ops.Initializer), type(init_ops_v2.Initializer))):
120 initializer = initializer()
--> 121 init_val = lambda: initializer(shape, dtype=dtype)
122 variable_dtype = dtype.base_dtype
123 if use_resource is None:

~/Downloads/new_downloads/1/keras_retinanet/initializers.py in call(self, shape, dtype)
35 def call(self, shape, dtype=None):
36 # set bias to -log((1 - p)/p) for foreground
---> 37 result = np.ones(shape, dtype=dtype) * -math.log((1 - self.probability) / self.probability)
38
39 return result

~/.local/lib/python3.6/site-packages/numpy/core/numeric.py in ones(shape, dtype, order)
205
206 """
--> 207 a = empty(shape, dtype, order)
208 multiarray.copyto(a, 1, casting='unsafe')
209 return a

TypeError: data type not understood