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| import csv |
| import os |
| from collections import OrderedDict |
| from typing import Dict, Optional, Union |
|
|
| import numpy as np |
| import torch |
|
|
|
|
| class CSVSaver: |
| """ |
| Save the data in a dictionary format cache, and write to a CSV file finally. |
| Typically, the data can be classification predictions, call `save` for single data |
| or call `save_batch` to save a batch of data together, and call `finalize` to write |
| the cached data into CSV file. If no meta data provided, use index from 0 to save data. |
| """ |
|
|
| def __init__(self, output_dir: str = "./", filename: str = "predictions.csv", overwrite: bool = True) -> None: |
| """ |
| Args: |
| output_dir: output CSV file directory. |
| filename: name of the saved CSV file name. |
| overwrite: whether to overwriting existing CSV file content. If we are not overwriting, |
| then we check if the results have been previously saved, and load them to the prediction_dict. |
| |
| """ |
| self.output_dir = output_dir |
| self._cache_dict: OrderedDict = OrderedDict() |
| assert isinstance(filename, str) and filename[-4:] == ".csv", "filename must be a string with CSV format." |
| self._filepath = os.path.join(output_dir, filename) |
| self.overwrite = overwrite |
| self._data_index = 0 |
|
|
| def finalize(self) -> None: |
| """ |
| Writes the cached dict to a csv |
| |
| """ |
| if not self.overwrite and os.path.exists(self._filepath): |
| with open(self._filepath, "r") as f: |
| reader = csv.reader(f) |
| for row in reader: |
| self._cache_dict[row[0]] = np.array(row[1:]).astype(np.float32) |
|
|
| if not os.path.exists(self.output_dir): |
| os.makedirs(self.output_dir) |
| with open(self._filepath, "w") as f: |
| for k, v in self._cache_dict.items(): |
| f.write(k) |
| for result in v.flatten(): |
| f.write("," + str(result)) |
| f.write("\n") |
|
|
| def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None: |
| """Save data into the cache dictionary. The metadata should have the following key: |
| - ``'filename_or_obj'`` -- save the data corresponding to file name or object. |
| If meta_data is None, use the default index from 0 to save data instead. |
| |
| Args: |
| data: target data content that save into cache. |
| meta_data: the meta data information corresponding to the data. |
| |
| """ |
| save_key = meta_data["filename_or_obj"] if meta_data else str(self._data_index) |
| self._data_index += 1 |
| if torch.is_tensor(data): |
| data = data.detach().cpu().numpy() |
| assert isinstance(data, np.ndarray) |
| self._cache_dict[save_key] = data.astype(np.float32) |
|
|
| def save_batch(self, batch_data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict] = None) -> None: |
| """Save a batch of data into the cache dictionary. |
| |
| Args: |
| batch_data: target batch data content that save into cache. |
| meta_data: every key-value in the meta_data is corresponding to 1 batch of data. |
| |
| """ |
| for i, data in enumerate(batch_data): |
| self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None) |
|
|
