SegMamba/monai/data/csv_saver.py

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import os
import warnings
from collections import OrderedDict
from pathlib import Path

import numpy as np
import torch

from monai.config.type_definitions import PathLike
from monai.utils import ImageMetaKey as Key


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 metadata provided, use index from 0 to save data.
    Note that this saver can't support multi-processing because it reads / writes single
    CSV file and can't guarantee the data order in multi-processing situation.

    """

    def __init__(
        self,
        output_dir: PathLike = "./",
        filename: str = "predictions.csv",
        overwrite: bool = True,
        flush: bool = False,
        delimiter: str = ",",
    ) -> 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 True, will clear the file before saving.
                otherwise, will append new content to the CSV file.
            flush: whether to write the cache data to CSV file immediately when `save_batch` and clear the cache.
                default to False.
            delimiter: the delimiter character in the saved file, default to "," as the default output type is `csv`.
                to be consistent with: https://docs.python.org/3/library/csv.html#csv.Dialect.delimiter.

        """
        self.output_dir = Path(output_dir)
        self._cache_dict: OrderedDict = OrderedDict()
        if not (isinstance(filename, str) and filename[-4:] == ".csv"):
            warnings.warn("CSV filename is not a string ends with '.csv'.")
        self._filepath = self.output_dir / filename
        if self._filepath.exists() and overwrite:
            os.remove(self._filepath)

        self.flush = flush
        self.delimiter = delimiter
        self._data_index = 0

    def finalize(self) -> None:
        """
        Writes the cached dict to a csv

        """
        if not self.output_dir.exists():
            self.output_dir.mkdir(parents=True, exist_ok=True)
        with open(self._filepath, "a") as f:
            for k, v in self._cache_dict.items():
                f.write(k)
                for result in v.flatten():
                    f.write(self.delimiter + str(result))
                f.write("\n")
        # clear cache content after writing
        self.reset_cache()

    def save(self, data: torch.Tensor | np.ndarray, meta_data: dict | None = 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 metadata information corresponding to the data.

        """
        save_key = meta_data[Key.FILENAME_OR_OBJ] if meta_data else str(self._data_index)
        self._data_index += 1
        if isinstance(data, torch.Tensor):
            data = data.detach().cpu().numpy()
        self._cache_dict[save_key] = np.asarray(data, dtype=float)

    def save_batch(self, batch_data: torch.Tensor | np.ndarray, meta_data: dict | None = 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):  # save a batch of files
            self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None)

        if self.flush:
            self.finalize()

    def get_cache(self) -> OrderedDict:
        """Get the cache dictionary, key is filename and value is the corresponding data"""

        return self._cache_dict

    def reset_cache(self) -> None:
        """Clear the cache dictionary content"""
        self._cache_dict.clear()