def test_saved_content(self):
with tempfile.TemporaryDirectory() as tempdir:
# set up engine
def _train_func(engine, batch):
return torch.zeros(8)
engine = Engine(_train_func)
# set up testing handler
saver = CSVSaver(output_dir=tempdir, filename="predictions2.csv")
ClassificationSaver(output_dir=tempdir,
filename="predictions1.csv").attach(engine)
ClassificationSaver(saver=saver).attach(engine)
data = [{
"filename_or_obj": ["testfile" + str(i) for i in range(8)]
}]
engine.run(data, max_epochs=1)
def _test_file(filename):
filepath = os.path.join(tempdir, filename)
self.assertTrue(os.path.exists(filepath))
with open(filepath, "r") as f:
reader = csv.reader(f)
i = 0
for row in reader:
self.assertEqual(row[0], "testfile" + str(i))
self.assertEqual(
np.array(row[1:]).astype(np.float32), 0.0)
i += 1
self.assertEqual(i, 8)
_test_file("predictions1.csv")
_test_file("predictions2.csv")
def __init__(
self,
keys: KeysCollection,
meta_keys: Optional[KeysCollection] = None,
meta_key_postfix: str = DEFAULT_POST_FIX,
saver: Optional[CSVSaver] = None,
output_dir: PathLike = "./",
filename: str = "predictions.csv",
delimiter: str = ",",
overwrite: bool = True,
flush: bool = True,
allow_missing_keys: bool = False,
) -> None:
"""
Args:
keys: keys of the corresponding items to model output, this transform only supports 1 key.
See also: :py:class:`monai.transforms.compose.MapTransform`
meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
the metadata is a dictionary object which contains: filename, original_shape, etc.
it can be a sequence of string, map to the `keys`.
if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
will extract the filename of input image to save classification results.
meta_key_postfix: `key_{postfix}` was used to store the metadata in `LoadImaged`.
so need the key to extract the metadata of input image, like filename, etc. default is `meta_dict`.
for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
the metadata is a dictionary object which contains: filename, original_shape, etc.
this arg only works when `meta_keys=None`. if no corresponding metadata, set to `None`.
saver: the saver instance to save classification results, if None, create a CSVSaver internally.
the saver must provide `save(data, meta_data)` and `finalize()` APIs.
output_dir: if `saver=None`, specify the directory to save the CSV file.
filename: if `saver=None`, specify the name of the saved CSV file.
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.
overwrite: if `saver=None`, indicate 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: if `saver=None`, indicate whether to write the cache data to CSV file immediately
in this transform and clear the cache. default to True.
If False, may need user to call `saver.finalize()` manually or use `ClassificationSaver` handler.
allow_missing_keys: don't raise exception if key is missing.
"""
super().__init__(keys, allow_missing_keys)
if len(self.keys) != 1:
raise ValueError(
"only 1 key is allowed when saving the classification result.")
self.saver = saver or CSVSaver(output_dir=output_dir,
filename=filename,
overwrite=overwrite,
flush=flush,
delimiter=delimiter)
self.flush = flush
self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))
self.meta_key_postfix = ensure_tuple_rep(meta_key_postfix,
len(self.keys))