OmniSafe Tools#

`get_flat_params_from`(model)	This function is used to get the flattened parameters from the model.
`get_flat_gradients_from`(model)	This function is used to get the flattened gradients from the model.
`set_param_values_to_model`(model, vals)	This function is used to set the parameters to the model.
`custom_cfgs_to_dict`(key_list, value)	This function is used to convert the custom configurations to dict.
`update_dict`(total_dict, item_dict)	Updater of multi-level dictionary.
`load_yaml`(path)	Get the default kwargs from `yaml` file.
`recursive_check_config`(config, default_config)	Check whether config is valid in default_config.
`seed_all`(seed)	This function is used to set the random seed for all the packages.

Algorithms Tools#

Documentation

omnisafe.utils.tools.get_flat_params_from(model)[source]#

This function is used to get the flattened parameters from the model.

Note

Some algorithms need to get the flattened parameters from the model, such as the TRPO and CPO algorithm. In these algorithms, the parameters are flattened and then used to calculate the loss.

Examples

>>> model = torch.nn.Linear(2, 2)
>>> model.weight.data = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
>>> get_flat_params_from(model)
tensor([1., 2., 3., 4.])

Parameters:: model (torch.nn.Module) – model to be flattened.
Returns:: Flattened parameters.
Raises:: AssertionError – If no gradients were found in model parameters.
Return type:: Tensor

omnisafe.utils.tools.get_flat_gradients_from(model)[source]#

This function is used to get the flattened gradients from the model.

Note

Some algorithms need to get the flattened gradients from the model, such as the TRPO and CPO algorithm. In these algorithms, the gradients are flattened and then used to calculate the loss.

Parameters:: model (torch.nn.Module) – The model to be flattened.
Returns:: Flattened gradients.
Raises:: AssertionError – If no gradients were found in model parameters.
Return type:: Tensor

omnisafe.utils.tools.set_param_values_to_model(model, vals)[source]#

This function is used to set the parameters to the model.

Note

Some algorithms (e.g. TRPO, CPO, etc.) need to set the parameters to the model, instead of using the optimizer.step().

Examples

>>> model = torch.nn.Linear(2, 2)
>>> model.weight.data = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
>>> vals = torch.tensor([1.0, 2.0, 3.0, 4.0])
>>> set_param_values_to_model(model, vals)
>>> model.weight.data
tensor([[1., 2.],
        [3., 4.]])

Parameters:

model (torch.nn.Module) – The model to be set.
vals (torch.Tensor) – The parameters to be set.

Raises:

AssertionError – If the instance of the parameters is not torch.Tensor, or the lengths of the parameters and the model parameters do not match.

Return type:

None

Config Tools#

Documentation

omnisafe.utils.tools.custom_cfgs_to_dict(key_list, value)[source]#

This function is used to convert the custom configurations to dict.

Note

This function is used to convert the custom configurations to dict. For example, if the custom configurations are train_cfgs:use_wandb and True, then the output dict will be {'train_cfgs': {'use_wandb': True}}.

Parameters:

key_list (str) – list of keys.
value (Any) – value.

Returns:

The converted dict.

Return type:

dict[str, Any]

omnisafe.utils.tools.update_dict(total_dict, item_dict)[source]#

Updater of multi-level dictionary.

Parameters:

total_dict (dict[str, Any]) – The total dictionary.
item_dict (dict[str, Any]) – The item dictionary.

Return type:

None

Examples

>>> total_dict = {'a': {'b': 1, 'c': 2}}
>>> item_dict = {'a': {'b': 3, 'd': 4}}
>>> update_dict(total_dict, item_dict)
>>> total_dict
{'a': {'b': 3, 'c': 2, 'd': 4}}

omnisafe.utils.tools.load_yaml(path)[source]#

Get the default kwargs from yaml file.

Note

This function search the yaml file by the algorithm name and environment name. Make sure your new implemented algorithm or environment has the same name as the yaml file.

Parameters:: path (str) – The path of the yaml file.
Returns:: The default kwargs.
Raises:: AssertionError – If the yaml file is not found.
Return type:: dict[str, Any]

omnisafe.utils.tools.recursive_check_config(config, default_config, exclude_keys=())[source]#

Check whether config is valid in default_config.

Parameters:

config (dict[str, Any]) – The config to be checked.
default_config (dict[str, Any]) – The default config.
exclude_keys (tuple of str, optional) – The keys to be excluded. Defaults to ().

Raises:

AssertionError – If the type of the value is not the same as the default value.
KeyError – If the key is not in default_config.

Return type:

None

Seed Tools#

Documentation

omnisafe.utils.tools.seed_all(seed)[source]#

This function is used to set the random seed for all the packages.

Hint

To reproduce the results, you need to set the random seed for all the packages. Including numpy, random, torch, torch.cuda, torch.backends.cudnn.

Warning

If you want to use the torch.backends.cudnn.benchmark or torch.backends.cudnn.deterministic and your cuda version is over 10.2, you need to set the CUBLAS_WORKSPACE_CONFIG and PYTHONHASHSEED environment variables.

Parameters:: seed (int) – The random seed.
Return type:: None

Experiment Grid Tools#

Documentation

omnisafe.utils.exp_grid_tools.all_bools(vals)[source]#

Check if all values are bools.

Parameters:: vals (list[Any]) – Values to check.
Returns:: True if all values are bools, False otherwise.
Return type:: bool

omnisafe.utils.exp_grid_tools.valid_str(vals)[source]#

Convert a value or values to a string which could go in a path of file.

Partly based on this gist.

Parameters:: vals (list[Any] or str) – Values to convert.
Returns:: Converted string.
Return type:: str