File size: 3,956 Bytes
5ccd75a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # Copyright 2020 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.
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): # save a batch of files
self.save(data, {k: meta_data[k][i] for k in meta_data} if meta_data else None)
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