!4582 Mod_callback_serial
Merge pull request !4582 from wanyiming/mod_callback_serial
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465390e580
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@ -75,9 +75,9 @@ class Callback:
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"""
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Abstract base class used to build a callback class. Callbacks are context managers
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which will be entered and exited when passing into the Model.
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You can leverage this mechanism to init and release resources automatically.
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You can use this mechanism to initialize and release resources automatically.
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Callback function will execution some operating to the current step or epoch.
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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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>>> class Print_info(Callback):
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@ -229,11 +229,11 @@ class RunContext:
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"""
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Provides information about the model.
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Run call being made. Provides information about original request to model function.
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callback objects can stop the loop by calling request_stop() of run_context.
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Provides information about original request to model function.
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Callback objects can stop the loop by calling request_stop() of run_context.
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Args:
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original_args (dict): Holding the related information of model etc.
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original_args (dict): Holding the related information of model.
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"""
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def __init__(self, original_args):
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if not isinstance(original_args, dict):
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@ -246,13 +246,13 @@ class RunContext:
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Get the _original_args object.
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Returns:
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Dict, a object holding the original arguments of model.
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Dict, an object that holds the original arguments of model.
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"""
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return self._original_args
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def request_stop(self):
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"""
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Sets stop requested during training.
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Sets stop requirement during training.
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Callbacks can use this function to request stop of iterations.
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model.train() checks whether this is called or not.
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@ -70,23 +70,24 @@ def _chg_ckpt_file_name_if_same_exist(directory, prefix):
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class CheckpointConfig:
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"""
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The config for model checkpoint.
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The configuration of model checkpoint.
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Note:
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During the training process, if dataset is transmitted through the data channel,
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suggest set save_checkpoint_steps be an integer multiple of loop_size.
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Otherwise there may be deviation in the timing of saving checkpoint.
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It is suggested to set 'save_checkpoint_steps' to an integer multiple of loop_size.
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Otherwise, the time to save the checkpoint may be biased.
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Args:
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save_checkpoint_steps (int): Steps to save checkpoint. Default: 1.
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save_checkpoint_seconds (int): Seconds to save checkpoint. Default: 0.
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Can't be used with save_checkpoint_steps at the same time.
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keep_checkpoint_max (int): Maximum step to save checkpoint. Default: 5.
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keep_checkpoint_max (int): Maximum number of checkpoint files can be saved. Default: 5.
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keep_checkpoint_per_n_minutes (int): Keep one checkpoint every n minutes. Default: 0.
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Can't be used with keep_checkpoint_max at the same time.
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integrated_save (bool): Whether to intergrated save in automatic model parallel scene. Default: True.
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Integrated save function is only supported in automatic parallel scene, not supported in manual parallel.
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async_save (bool): Whether asynchronous execute save checkpoint into file. Default: False
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integrated_save (bool): Whether to perform integrated save function in automatic model parallel scene.
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Default: True. Integrated save function is only supported in automatic parallel scene, not supported
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in manual parallel.
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async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False
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Raises:
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ValueError: If the input_param is None or 0.
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@ -180,9 +181,9 @@ class ModelCheckpoint(Callback):
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It is called to combine with train process and save the model and network parameters after traning.
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Args:
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prefix (str): Checkpoint files names prefix. Default: "CKP".
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directory (str): Folder path into which checkpoint files will be saved. Default: None.
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config (CheckpointConfig): Checkpoint strategy config. Default: None.
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prefix (str): The prefix name of checkpoint files. Default: "CKP".
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directory (str): The path of the folder which will be saved in the checkpoint file. Default: None.
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config (CheckpointConfig): Checkpoint strategy configuration. Default: None.
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Raises:
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ValueError: If the prefix is invalid.
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@ -27,13 +27,13 @@ class LossMonitor(Callback):
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If the loss is NAN or INF, it will terminate training.
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Note:
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If per_print_times is 0 do not print loss.
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If per_print_times is 0, do not print loss.
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Args:
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per_print_times (int): Print loss every times. Default: 1.
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per_print_times (int): Print the loss each every time. Default: 1.
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Raises:
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ValueError: If print_step is not int or less than zero.
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ValueError: If print_step is not an integer or less than zero.
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"""
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def __init__(self, per_print_times=1):
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@ -62,7 +62,7 @@ class SummaryCollector(Callback):
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SummaryCollector can help you to collect some common information.
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It can help you to collect loss, learning late, computational graph and so on.
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SummaryCollector also persists data collected by the summary operator into a summary file.
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SummaryCollector also enables the summary operator to collect data from a summary file.
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Note:
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1. Multiple SummaryCollector instances in callback list are not allowed.
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@ -74,51 +74,51 @@ class SummaryCollector(Callback):
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If the directory does not exist, it will be created automatically.
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collect_freq (int): Set the frequency of data collection, it should be greater then zero,
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and the unit is `step`. Default: 10. If a frequency is set, we will collect data
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at (current steps % freq) == 0, and the first step will be collected at any time.
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when (current steps % freq) equals to 0, and the first step will be collected at any time.
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It is important to note that if the data sink mode is used, the unit will become the `epoch`.
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It is not recommended to collect data too frequently, which can affect performance.
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collect_specified_data (Union[None, dict]): Perform custom operations on the collected data. Default: None.
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By default, if set to None, all data is collected as the default behavior.
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If you want to customize the data collected, you can do so with a dictionary.
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Examples,you can set {'collect_metric': False} to control not collecting metrics.
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You can customize the collected data with a dictionary.
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For example, you can set {'collect_metric': False} to control not collecting metrics.
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The data that supports control is shown below.
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- collect_metric: Whether to collect training metrics, currently only loss is collected.
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The first output will be treated as loss, and it will be averaged.
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- collect_metric: Whether to collect training metrics, currently only the loss is collected.
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The first output will be treated as the loss and it will be averaged.
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Optional: True/False. Default: True.
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- collect_graph: Whether to collect computational graph, currently only
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- collect_graph: Whether to collect the computational graph. Currently, only
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training computational graph is collected. Optional: True/False. Default: True.
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- collect_train_lineage: Whether to collect lineage data for the training phase,
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this field will be displayed on the lineage page of Mindinsight. Optional: True/False. Default: True.
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- collect_eval_lineage: Whether to collect lineage data for the eval phase,
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- collect_eval_lineage: Whether to collect lineage data for the evaluation phase,
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this field will be displayed on the lineage page of Mindinsight. Optional: True/False. Default: True.
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- collect_input_data: Whether to collect dataset for each training. Currently only image data is supported.
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Optional: True/False. Default: True.
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- collect_dataset_graph: Whether to collect dataset graph for the training phase.
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Optional: True/False. Default: True.
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- histogram_regular: Collect weight and bias for parameter distribution page display in MindInsight.
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- histogram_regular: Collect weight and bias for parameter distribution page and displayed in MindInsight.
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This field allows regular strings to control which parameters to collect.
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Default: None, it means only the first five parameters are collected.
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It is not recommended to collect too many parameters at once, as it can affect performance.
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Note that if you collect too many parameters and run out of memory, the training will fail.
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keep_default_action (bool): This field affects the collection behavior of the 'collect_specified_data' field.
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Optional: True/False, Default: True.
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True: means that after specified data is set, non-specified data is collected as the default behavior.
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False: means that after specified data is set, only the specified data is collected,
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True: it means that after specified data is set, non-specified data is collected as the default behavior.
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False: it means that after specified data is set, only the specified data is collected,
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and the others are not collected.
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custom_lineage_data (Union[dict, None]): Allows you to customize the data and present it on the MingInsight
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lineage page. In the custom data, the key type support str, and the value type support str/int/float.
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Default: None, it means there is no custom data.
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collect_tensor_freq (Optional[int]): Same semantic as the `collect_freq`, but controls TensorSummary only.
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Because TensorSummary data is too large compared to other summary data, this parameter is used to reduce
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its collection. By default, TensorSummary data will be collected at most 20 steps, but not more than how
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many steps other summary data will be collected.
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lineage page. In the custom data, the type of the key supports str, and the type of value supports str, int
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and float. Default: None, it means there is no custom data.
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collect_tensor_freq (Optional[int]): The same semantics as the `collect_freq`, but controls TensorSummary only.
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Because TensorSummary data is too large to be compared with other summary data, this parameter is used to
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reduce its collection. By default, The maximum number of steps for collecting TensorSummary data is 21,
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but it will not exceed the number of steps for collecting other summary data.
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Default: None, which means to follow the behavior as described above. For example, given `collect_freq=10`,
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when the total steps is 600, TensorSummary will be collected 20 steps, while other summary data 61 steps,
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but when the total steps is 20, both TensorSummary and other summary will be collected 3 steps.
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Also note that when in parallel mode, the total steps will be splitted evenly, which will
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affect how many steps TensorSummary will be collected.
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max_file_size (Optional[int]): The maximum size in bytes each file can be written to the disk.
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affect the number of steps TensorSummary will be collected.
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max_file_size (Optional[int]): The maximum size in bytes of each file that can be written to the disk.
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Default: None, which means no limit. For example, to write not larger than 4GB,
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specify `max_file_size=4 * 1024**3`.
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@ -41,7 +41,7 @@ class FixedLossScaleManager(LossScaleManager):
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Args:
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loss_scale (float): Loss scale. Default: 128.0.
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drop_overflow_update (bool): whether to do optimizer if there is overflow. Default: True.
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drop_overflow_update (bool): whether to execute optimizer if there is an overflow. Default: True.
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Examples:
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>>> loss_scale_manager = FixedLossScaleManager()
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@ -59,7 +59,7 @@ class FixedLossScaleManager(LossScaleManager):
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return self._loss_scale
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def get_drop_overflow_update(self):
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"""Get the flag whether to drop optimizer update when there is overflow happened"""
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"""Get the flag whether to drop optimizer update when there is an overflow."""
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return self._drop_overflow_update
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def update_loss_scale(self, overflow):
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@ -82,7 +82,7 @@ class DynamicLossScaleManager(LossScaleManager):
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Dynamic loss-scale manager.
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Args:
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init_loss_scale (float): Init loss scale. Default: 2**24.
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init_loss_scale (float): Initialize loss scale. Default: 2**24.
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scale_factor (int): Coefficient of increase and decrease. Default: 2.
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scale_window (int): Maximum continuous normal steps when there is no overflow. Default: 2000.
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@ -135,7 +135,7 @@ class DynamicLossScaleManager(LossScaleManager):
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self.cur_iter += 1
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def get_drop_overflow_update(self):
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"""Get the flag whether to drop optimizer update when there is overflow happened"""
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"""Get the flag whether to drop optimizer update when there is an overflow."""
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return True
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def get_update_cell(self):
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@ -13,11 +13,11 @@
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# limitations under the License.
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# ============================================================================
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"""
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quantization.
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Quantization.
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User can use quantization aware to train a model. MindSpore supports quantization aware training,
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which models quantization errors in both the forward and backward passes using fake-quantization
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ops. Note that the entire computation is carried out in floating point. At the end of quantization
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operations. Note that the entire computation is carried out in floating point. At the end of quantization
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aware training, MindSpore provides conversion functions to convert the trained model into lower precision.
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"""
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@ -145,10 +145,10 @@ def save_checkpoint(parameter_list, ckpt_file_name, async_save=False):
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Saves checkpoint info to a specified file.
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Args:
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parameter_list (list): Parameters list, each element is a dict
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parameter_list (list): Parameters list, each element is a dictionary
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like {"name":xx, "type":xx, "shape":xx, "data":xx}.
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ckpt_file_name (str): Checkpoint file name.
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async_save (bool): Whether asynchronous execute save checkpoint into file. Default: False
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async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False
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Raises:
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RuntimeError: Failed to save the Checkpoint file.
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@ -271,10 +271,10 @@ def load_param_into_net(net, parameter_dict):
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Args:
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net (Cell): Cell network.
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parameter_dict (dict): Parameter dict.
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parameter_dict (dict): Parameter dictionary.
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Raises:
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TypeError: Argument is not a Cell, or parameter_dict is not a Parameter dict.
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TypeError: Argument is not a Cell, or parameter_dict is not a Parameter dictionary.
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"""
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if not isinstance(net, nn.Cell):
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logger.error("Failed to combine the net and the parameters.")
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@ -450,12 +450,12 @@ def _fill_param_into_net(net, parameter_list):
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def export(net, *inputs, file_name, file_format='AIR'):
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"""
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Exports MindSpore predict model to file in specified format.
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Export the MindSpore prediction model to a file in the specified format.
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Args:
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net (Cell): MindSpore network.
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inputs (Tensor): Inputs of the `net`.
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file_name (str): File name of model to export.
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file_name (str): File name of the model to be exported.
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file_format (str): MindSpore currently supports 'AIR', 'ONNX' and 'MINDIR' format for exported model.
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- AIR: Ascend Intermidiate Representation. An intermidiate representation format of Ascend model.
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@ -510,7 +510,7 @@ def parse_print(print_file_name):
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Loads Print data from a specified file.
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Args:
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print_file_name (str): The file name of save print data.
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print_file_name (str): The file name of saved print data.
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Returns:
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List, element of list is Tensor.
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@ -64,29 +64,29 @@ class SummaryRecord:
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SummaryRecord is used to record the summary data and lineage data.
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The API will create a summary file and lineage files lazily in a given directory and writes data to them.
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It writes the data to files by executing the 'record' method. In addition to record the data bubbled up from
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It writes the data to files by executing the 'record' method. In addition to recording the data bubbled up from
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the network by defining the summary operators, SummaryRecord also supports to record extra data which
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can be added by calling add_value.
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Note:
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1. Make sure to close the SummaryRecord at the end, or the process will not exit.
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Please see the Example section below on how to properly close with two ways.
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2. The SummaryRecord instance can only allow one at a time, otherwise it will cause problems with data writes.
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1. Make sure to close the SummaryRecord at the end, otherwise the process will not exit.
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Please see the Example section below to learn how to close properly in two ways.
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2. Only one SummaryRecord instance is allowed at a time, otherwise it will cause data writing problems.
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Args:
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log_dir (str): The log_dir is a directory location to save the summary.
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queue_max_size (int): Deprecated. The capacity of event queue.(reserved). Default: 0.
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flush_time (int): Deprecated. Frequency to flush the summaries to disk, the unit is second. Default: 120.
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flush_time (int): Deprecated. Frequency of flush the summary file to disk. The unit is second. Default: 120.
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file_prefix (str): The prefix of file. Default: "events".
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file_suffix (str): The suffix of file. Default: "_MS".
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network (Cell): Obtain a pipeline through network for saving graph summary. Default: None.
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max_file_size (Optional[int]): The maximum size in bytes each file can be written to the disk. \
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max_file_size (Optional[int]): The maximum size of each file that can be written to disk (in bytes). \
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Unlimited by default. For example, to write not larger than 4GB, specify `max_file_size=4 * 1024**3`.
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Raises:
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TypeError: If `max_file_size`, `queue_max_size` or `flush_time` is not int, \
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or `file_prefix` and `file_suffix` is not str.
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RuntimeError: If the log_dir can not be resolved to a canonicalized absolute pathname.
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TypeError: If the data type of `max_file_size`, `queue_max_size` or `flush_time` is not int, \
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or the data type of `file_prefix` and `file_suffix` is not str.
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RuntimeError: If the log_dir is not a normalized absolute path name.
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Examples:
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>>> # use in with statement to auto close
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@ -171,10 +171,10 @@ class SummaryRecord:
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def set_mode(self, mode):
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"""
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Set the mode for the recorder to be aware. The mode is set 'train' by default.
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Set the mode for the recorder to be aware. The mode is set to 'train' by default.
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Args:
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mode (str): The mode to set, which should be 'train' or 'eval'.
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mode (str): The mode to be set, which should be 'train' or 'eval'.
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Raises:
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ValueError: When the mode is not recognized.
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@ -190,29 +190,30 @@ class SummaryRecord:
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def add_value(self, plugin, name, value):
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"""
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Add value to be record later on.
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Add value to be recorded later.
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When the plugin is 'tensor', 'scalar', 'image' or 'histogram',
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the name should be the tag name, and the value should be a Tensor.
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When the plugin plugin is 'graph', the value should be a GraphProto.
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When the plugin is 'graph', the value should be a GraphProto.
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When the plugin 'dataset_graph', 'train_lineage', 'eval_lineage',
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When the plugin is 'dataset_graph', 'train_lineage', 'eval_lineage',
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or 'custom_lineage_data', the value should be a proto message.
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Args:
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plugin (str): The plugin for the value.
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name (str): The name for the value.
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plugin (str): The value of the plugin.
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name (str): The value of the name.
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value (Union[Tensor, GraphProto, TrainLineage, EvaluationLineage, DatasetGraph, UserDefinedInfo]): \
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The value to store.
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- GraphProto: The 'value' should be a serialized string this type when the plugin is 'graph'.
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- Tensor: The 'value' should be this type when the plugin is 'scalar', 'image', 'tensor' or 'histogram'.
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- TrainLineage: The 'value' should be this type when the plugin is 'train_lineage'.
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- EvaluationLineage: The 'value' should be this type when the plugin is 'eval_lineage'.
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- DatasetGraph: The 'value' should be this type when the plugin is 'dataset_graph'.
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- UserDefinedInfo: The 'value' should be this type when the plugin is 'custom_lineage_data'.
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- The data type of value should be 'GraphProto' when the plugin is 'graph'.
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- The data type of value should be 'Tensor' when the plugin is 'scalar', 'image', 'tensor'
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or 'histogram'.
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- The data type of value should be 'TrainLineage' when the plugin is 'train_lineage'.
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- The data type of value should be 'EvaluationLineage' when the plugin is 'eval_lineage'.
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- The data type of value should be 'DatasetGraph' when the plugin is 'dataset_graph'.
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- The data type of value should be 'UserDefinedInfo' when the plugin is 'custom_lineage_data'.
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Raises:
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ValueError: When the name is not valid.
|
||||
|
|
@ -248,9 +249,9 @@ class SummaryRecord:
|
|||
|
||||
Args:
|
||||
step (int): Represents training step number.
|
||||
train_network (Cell): The network that called the callback.
|
||||
train_network (Cell): The network to call the callback.
|
||||
plugin_filter (Optional[Callable[[str], bool]]): The filter function, \
|
||||
which is used to filter out plugins from being written by return False.
|
||||
which is used to filter out plugins from being written by returning False.
|
||||
|
||||
Returns:
|
||||
bool, whether the record process is successful or not.
|
||||
|
|
@ -342,7 +343,7 @@ class SummaryRecord:
|
|||
|
||||
def close(self):
|
||||
"""
|
||||
Flush all events and close summary records. Please use with statement to autoclose.
|
||||
Flush all events and close summary records. Please use the statement to autoclose.
|
||||
|
||||
Examples:
|
||||
>>> try:
|
||||
|
|
|
|||
Loading…
Reference in New Issue