| import torch.nn as nn |
| from utils import move_to, detach_and_clone |
|
|
|
|
| class Algorithm(nn.Module): |
| def __init__(self, device): |
| super().__init__() |
| self.device = device |
| self.out_device = 'cpu' |
| self._has_log = False |
| self.reset_log() |
|
|
| def update(self, batch): |
| """ |
| Process the batch, update the log, and update the model |
| Args: |
| - batch (tuple of Tensors): a batch of data yielded by data loaders |
| Output: |
| - results (dictionary): information about the batch, such as: |
| - g (Tensor) |
| - y_true (Tensor) |
| - metadata (Tensor) |
| - loss (Tensor) |
| - metrics (Tensor) |
| """ |
| raise NotImplementedError |
|
|
| def evaluate(self, batch): |
| """ |
| Process the batch and update the log, without updating the model |
| Args: |
| - batch (tuple of Tensors): a batch of data yielded by data loaders |
| Output: |
| - results (dictionary): information about the batch, such as: |
| - g (Tensor) |
| - y_true (Tensor) |
| - metadata (Tensor) |
| - loss (Tensor) |
| - metrics (Tensor) |
| """ |
| raise NotImplementedError |
|
|
| def train(self, mode=True): |
| """ |
| Switch to train mode |
| """ |
| self.is_training = mode |
| super().train(mode) |
| self.reset_log() |
|
|
| @property |
| def has_log(self): |
| return self._has_log |
|
|
| def reset_log(self): |
| """ |
| Resets log by clearing out the internal log, Algorithm.log_dict |
| """ |
| self._has_log = False |
| self.log_dict = {} |
|
|
| def update_log(self, results): |
| """ |
| Updates the internal log, Algorithm.log_dict |
| Args: |
| - results (dictionary) |
| """ |
| raise NotImplementedError |
|
|
| def get_log(self): |
| """ |
| Sanitizes the internal log (Algorithm.log_dict) and outputs it. |
| |
| """ |
| raise NotImplementedError |
|
|
| def get_pretty_log_str(self): |
| raise NotImplementedError |
|
|
| def step_schedulers(self, is_epoch, metrics={}, log_access=False): |
| """ |
| Update all relevant schedulers |
| Args: |
| - is_epoch (bool): epoch-wise update if set to True, batch-wise update otherwise |
| - metrics (dict): a dictionary of metrics that can be used for scheduler updates |
| - log_access (bool): whether metrics from self.get_log() can be used to update schedulers |
| """ |
| raise NotImplementedError |
|
|
| def sanitize_dict(self, in_dict, to_out_device=True): |
| """ |
| Helper function that sanitizes dictionaries by: |
| - moving to the specified output device |
| - removing any gradient information |
| - detaching and cloning the tensors |
| Args: |
| - in_dict (dictionary) |
| Output: |
| - out_dict (dictionary): sanitized version of in_dict |
| """ |
| out_dict = detach_and_clone(in_dict) |
| if to_out_device: |
| out_dict = move_to(out_dict, self.out_device) |
| return out_dict |
