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| import torch | |
| import logging | |
| import math | |
| from ding.torch_utils import to_list | |
| from ding.utils.data import NaiveRLDataset | |
| from torch.utils.data import DataLoader | |
| logging.basicConfig(level=logging.INFO) | |
| def test_accuracy_in_dataset(data_path, batch_size, policy): | |
| """ | |
| Overview: | |
| Evaluate total accuracy and accuracy of each action in dataset from | |
| ``datapath`` using the ``policy`` for gfootball env. | |
| """ | |
| dataset = NaiveRLDataset(data_path) | |
| dataloader = DataLoader(dataset, batch_size) | |
| total_accuracy_in_dataset = [] | |
| action_accuracy_in_dataset = {k: [] for k in range(19)} | |
| for _, minibatch in enumerate(dataloader): | |
| policy_output = policy._forward_eval(minibatch['obs']) | |
| pred_action = policy_output['action'] | |
| total_accuracy = (pred_action == minibatch['action'].view(-1)).float().mean() | |
| total_accuracy_in_dataset.append(total_accuracy) | |
| for action_unique in to_list(torch.unique(minibatch['action'])): | |
| # find the index where action is `action_unique` in `pred_action` | |
| action_index = (pred_action == action_unique).nonzero(as_tuple=True)[0] | |
| action_accuracy = (pred_action[action_index] == minibatch['action'].view(-1)[action_index]).float().mean() | |
| if math.isnan(action_accuracy): | |
| action_accuracy = 0.0 | |
| action_accuracy_in_dataset[action_unique].append(action_accuracy) | |
| # logging.info(f'the accuracy of action {action_unique} in current train mini-batch is: {action_accuracy}') | |
| logging.info(f'total accuracy in dataset is: {torch.tensor(total_accuracy_in_dataset).mean().item()}') | |
| logging.info( | |
| f'accuracy of each action in dataset is (nan means the action does not appear in the dataset): ' | |
| f'{ {k: torch.tensor(action_accuracy_in_dataset[k]).mean().item() for k in range(19)} }' | |
| ) | |