towhee.trainer.optimization.adamw.AdamW¶
- class towhee.trainer.optimization.adamw.AdamW(params: Iterable[Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0, correct_bias: bool = True)[source]¶
Bases:
Optimizer
Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Regularization <https://arxiv.org/abs/1711.05101>. :param params: Iterable of parameters to optimize or dictionaries defining parameter groups. :type params: Iterable[nn.parameter.Parameter] :param lr: The learning rate to use. :type lr: float, optional :param betas: Adam’s betas parameters (b1, b2). :type betas: Tuple[float,float], optional :param eps: Adam’s epsilon for numerical stability. :type eps: float, optional :param weight_decay: Decoupled weight decay to apply. :type weight_decay: float, optional :param correct_bias: Whether or not to correct bias in Adam. :type correct_bias: bool, optional
Methods
Add a param group to the
Optimizer
s param_groups.Loads the optimizer state.
profile_hook_step
Register an optimizer step post hook which will be called after optimizer step. It should have the following signature::.
Register an optimizer step pre hook which will be called before optimizer step. It should have the following signature::.
Returns the state of the optimizer as a
dict
.Performs a single optimization step.
Sets the gradients of all optimized
torch.Tensor
s to zero.- __init__(params: Iterable[Parameter], lr: float = 0.001, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-06, weight_decay: float = 0.0, correct_bias: bool = True)[source]¶
- __repr__()¶
Return repr(self).
- add_param_group(param_group)¶
Add a param group to the
Optimizer
s param_groups.This can be useful when fine tuning a pre-trained network as frozen layers can be made trainable and added to the
Optimizer
as training progresses.- Parameters:
param_group (dict) – Specifies what Tensors should be optimized along with group specific optimization options.
- load_state_dict(state_dict)¶
Loads the optimizer state.
- Parameters:
state_dict (dict) – optimizer state. Should be an object returned from a call to
state_dict()
.
- register_step_post_hook(hook: Callable[[...], None]) RemovableHandle ¶
Register an optimizer step post hook which will be called after optimizer step. It should have the following signature:
hook(optimizer, args, kwargs) -> None
The
optimizer
argument is the optimizer instance being used.- Parameters:
hook (Callable) – The user defined hook to be registered.
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemoveableHandle
- register_step_pre_hook(hook: Callable[[...], None]) RemovableHandle ¶
Register an optimizer step pre hook which will be called before optimizer step. It should have the following signature:
hook(optimizer, args, kwargs) -> None or modified args and kwargs
The
optimizer
argument is the optimizer instance being used. If args and kwargs are modified by the pre-hook, then the transformed values are returned as a tuple containing the new_args and new_kwargs.- Parameters:
hook (Callable) – The user defined hook to be registered.
- Returns:
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type:
torch.utils.hooks.RemoveableHandle
- state_dict()¶
Returns the state of the optimizer as a
dict
.It contains two entries:
- state - a dict holding current optimization state. Its content
differs between optimizer classes.
- param_groups - a list containing all parameter groups where each
parameter group is a dict
- step(closure: Optional[Callable] = None)[source]¶
Performs a single optimization step. :param closure: A closure that reevaluates the model and returns the loss. :type closure: Callable, optional
- zero_grad(set_to_none: bool = True)¶
Sets the gradients of all optimized
torch.Tensor
s to zero.- Parameters:
set_to_none (bool) – instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests
zero_grad(set_to_none=True)
followed by a backward pass,.grad
s are guaranteed to be None for params that did not receive a gradient. 3.torch.optim
optimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether).