| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| |
|
| | 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) |
| |
|
