!19088 modify serialization comment
Merge pull request !19088 from changzherui/mod_serializer_comment
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@ -80,14 +80,21 @@ class Callback:
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Callback function will execute some operations in the current step or epoch.
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Examples:
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>>> from mindspore.train._callback import Callback
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>>> from mindspore import Model, nn
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>>> from mindspore.train.callback._callback import Callback
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>>> class Print_info(Callback):
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>>> def step_end(self, run_context):
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>>> cb_params = run_context.original_args()
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>>> print(cb_params.cur_epoch_num)
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>>> print(cb_params.cur_step_num)
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>>> print("step_num: ", cb_params.cur_step_num)
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>>>
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>>> print_cb = Print_info()
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>>> dataset = create_custom_dataset()
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>>> net = Net()
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>>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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>>> optim = nn.Momentum(net.trainable_params(), 0.01, 0.9)
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>>> model = Model(net, loss_fn=loss, optimizer=optim)
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>>> model.train(1, dataset, callbacks=print_cb)
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step_num: 1
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"""
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def __enter__(self):
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@ -199,7 +199,7 @@ def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True,
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save_obj (Union[Cell, list]): The cell object or data list(each element is a dictionary, like
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[{"name": param_name, "data": param_data},...], the type of
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param_name would be string, and the type of param_data would
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be parameter or `Tensor`).
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be parameter or Tensor).
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ckpt_file_name (str): Checkpoint file name. If the file name already exists, it will be overwritten.
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integrated_save (bool): Whether to integrated save in automatic model parallel scene. Default: True
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async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False
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@ -337,6 +337,10 @@ def load(file_name, **kwargs):
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>>> graph = load("net.mindir")
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>>> net = nn.GraphCell(graph)
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>>> output = net(input)
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>>> print(output)
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[[[[0.03204346 0.04455566 0.03509521]
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[0.02406311 0.04125977 0.02404785]
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[0.02018738 0.0292511 0.00889587]]]]
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"""
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if not isinstance(file_name, str):
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raise ValueError("The file name must be string.")
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@ -391,6 +395,8 @@ def load_checkpoint(ckpt_file_name, net=None, strict_load=False, filter_prefix=N
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>>>
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>>> ckpt_file_name = "./checkpoint/LeNet5-1_32.ckpt"
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>>> param_dict = load_checkpoint(ckpt_file_name, filter_prefix="conv1")
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>>> print(param_dict["conv2.weight]")
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Parameter (name=conv2.weight, shape=(16, 6, 5, 5), dtype=Float32, requires_grad=True
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"""
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ckpt_file_name, filter_prefix = _check_checkpoint_param(ckpt_file_name, filter_prefix)
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dec_key = Validator.check_isinstance('dec_key', dec_key, (type(None), bytes))
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@ -496,8 +502,12 @@ def load_param_into_net(net, parameter_dict, strict_load=False):
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Args:
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net (Cell): Cell network.
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parameter_dict (dict): Parameter dictionary.
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strict_load (bool): Whether to strict load the parameter into net. False: if some parameters in the net
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not loaded, it will remove some parameter's prefix name continue to load. Default: False
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strict_load (bool): Whether to strict load the parameter into net. If False, it will load parameter
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in the param_dict into net with the same suffix and load
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parameter with different accuracy. Default: False.
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Returns:
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List, parameters not loaded in the network.
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Raises:
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TypeError: Argument is not a Cell, or parameter_dict is not a Parameter dictionary.
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@ -695,15 +705,15 @@ def export(net, *inputs, file_name, file_format='AIR', **kwargs):
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kwargs (dict): Configuration options dictionary.
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- quant_mode: If the network is quantization aware training network, the quant_mode should
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- quant_mode (str): If the network is quantization aware training network, the quant_mode should
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be set to "QUANT", else the quant_mode should be set to "NONQUANT".
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- mean: The mean of input data after preprocessing, used for quantizing the first layer of network.
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- mean (float): The mean of input data after preprocessing, used for quantizing the first layer of network.
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Default: 127.5.
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- std_dev: The variance of input data after preprocessing, used for quantizing the first layer of network.
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Default: 127.5.
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- enc_key: Byte type key used for encryption. Tha valid length is 16, 24, or 32.
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- enc_mode: Specifies the encryption mode, take effect when enc_key is set. Option: 'AES-GCM' | 'AES-CBC'.
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Default: 'AES-GCM'.
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- std_dev (float): The variance of input data after preprocessing,
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used for quantizing the first layer of network. Default: 127.5.
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- enc_key (str): Byte type key used for encryption. Tha valid length is 16, 24, or 32.
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- enc_mode (str): Specifies the encryption mode, take effect when enc_key is set.
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Option: 'AES-GCM' | 'AES-CBC'. Default: 'AES-GCM'.
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Examples:
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>>> import numpy as np
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@ -1130,7 +1140,7 @@ def build_searched_strategy(strategy_filename):
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Raises:
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ValueError: Strategy file is incorrect.
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TypeError: Strategy_filename is not str.
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TypeError: strategy_filename is not str.
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"""
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if not isinstance(strategy_filename, str):
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raise TypeError(f"The strategy_filename should be str, but got {type(strategy_filename)}.")
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@ -1179,6 +1189,8 @@ def merge_sliced_parameter(sliced_parameters, strategy=None):
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KeyError: The parameter name is not in keys of strategy.
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Examples:
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>>> import numpy as np
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>>> from mindspore import Tensor
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>>> from mindspore.common.parameter import Parameter
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>>> from mindspore.train import merge_sliced_parameter
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>>>
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@ -1192,6 +1204,8 @@ def merge_sliced_parameter(sliced_parameters, strategy=None):
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... Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])),
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... "network.embedding_table")]
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>>> merged_parameter = merge_sliced_parameter(sliced_parameters)
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>>> print(merged_parameter)
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Parameter (name=network.embedding_table, shape=(12,), dtype=Float64, requires_grad=True)
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"""
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if not isinstance(sliced_parameters, list):
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raise TypeError(f"The sliced_parameters should be list, but got {type(sliced_parameters)}.")
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