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Nic-Ma merged 14 commits into from
Oct 22, 2021
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3146 Added callable example to the doc-string of output_transform #3158

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42a45e0
Merge pull request #19 from Project-MONAI/master
Nic-Ma Feb 1, 2021
cd16a13
Merge pull request #32 from Project-MONAI/master
Nic-Ma Feb 24, 2021
6f87afd
Merge pull request #180 from Project-MONAI/dev
Nic-Ma Jul 22, 2021
f398298
Merge pull request #214 from Project-MONAI/dev
Nic-Ma Sep 8, 2021
8f074e5
Merge pull request #251 from Project-MONAI/dev
Nic-Ma Oct 20, 2021
8fdfd2b
[DLMED] add example in doc-string
Nic-Ma Oct 20, 2021
0b4579b
[DLMED] add example about copy_cache
Nic-Ma Oct 20, 2021
3f0699c
Merge branch 'dev' into 3146-update-output-transform-doc
Nic-Ma Oct 20, 2021
97b2b16
Merge branch 'dev' into 3146-update-output-transform-doc
Nic-Ma Oct 21, 2021
e73b26f
[DLMED] update according to comments
Nic-Ma Oct 22, 2021
4cbc865
[MONAI] python code formatting
monai-bot Oct 22, 2021
7b5dd6d
Merge branch 'dev' into 3146-update-output-transform-doc
Nic-Ma Oct 22, 2021
f74c288
[DLMED] fix flake8
Nic-Ma Oct 22, 2021
ccf1314
Merge branch 'dev' into 3146-update-output-transform-doc
Nic-Ma Oct 22, 2021
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5 changes: 3 additions & 2 deletions monai/data/dataset.py
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Expand Up @@ -589,8 +589,9 @@ def __init__(
If num_workers is None then the number returned by os.cpu_count() is used.
progress: whether to display a progress bar.
copy_cache: whether to `deepcopy` the cache content before applying the random transforms,
default to `True`. if the random transforms don't modify the cache content
or every cache item is only used once in a `multi-processing` environment,
default to `True`. if the random transforms don't modify the cached content
(for example, randomly crop from the cached image and deepcopy the crop region)
or if every cache item is only used once in a `multi-processing` environment,
may set `copy=False` for better performance.
"""
if not isinstance(transform, Compose):
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6 changes: 6 additions & 0 deletions monai/handlers/classification_saver.py
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Expand Up @@ -55,9 +55,15 @@ def __init__(
batch_transform: a callable that is used to extract the `meta_data` dictionary of
the input images from `ignite.engine.state.batch`. the purpose is to get the input
filenames from the `meta_data` and store with classification results together.
`engine.state` and `batch_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
output_transform: a callable that is used to extract the model prediction data from
`ignite.engine.state.output`. the first dimension of its output will be treated as
the batch dimension. each item in the batch will be saved individually.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
name: identifier of logging.logger to use, defaulting to `engine.logger`.
save_rank: only the handler on specified rank will save to CSV file in multi-gpus validation,
default to 0.
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5 changes: 3 additions & 2 deletions monai/handlers/confusion_matrix.py
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Expand Up @@ -43,8 +43,9 @@ def __init__(
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: TP/TN/FP/FN of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

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5 changes: 3 additions & 2 deletions monai/handlers/hausdorff_distance.py
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Expand Up @@ -44,8 +44,9 @@ def __init__(
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: hausdorff distance
of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key.

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5 changes: 3 additions & 2 deletions monai/handlers/ignite_metric.py
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Expand Up @@ -41,8 +41,9 @@ class IgniteMetric(Metric): # type: ignore[valid-type, misc] # due to optional_
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean_dice of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

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5 changes: 3 additions & 2 deletions monai/handlers/mean_dice.py
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Expand Up @@ -32,8 +32,9 @@ def __init__(
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean dice of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

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3 changes: 3 additions & 0 deletions monai/handlers/metric_logger.py
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Expand Up @@ -57,6 +57,9 @@ class MetricLogger:

Args:
loss_transform: Converts the `output` value from the trainer's state into a loss value
`engine.state` and `loss_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
metric_transform: Converts the metric value coming from the trainer/evaluator's state into a storable value
evaluator: Optional evaluator to consume metric results from at the end of its evaluation run
"""
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3 changes: 3 additions & 0 deletions monai/handlers/metrics_saver.py
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Expand Up @@ -50,6 +50,9 @@ class MetricsSaver:
batch_transform: a callable that is used to extract the `meta_data` dictionary of
the input images from `ignite.engine.state.batch` if saving metric details. the purpose is to get the
input filenames from the `meta_data` and store with metric details together.
`engine.state` and `batch_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
summary_ops: expected computation operations to generate the summary report.
it can be: None, "*" or list of strings, default to None.
None - don't generate summary report for every expected metric_details.
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3 changes: 3 additions & 0 deletions monai/handlers/mlflow_handler.py
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Expand Up @@ -59,6 +59,9 @@ class MLFlowHandler:
By default this value logging happens when every iteration completed.
The default behavior is to track loss from output[0] as output is a decollated list
and we replicated loss value for every item of the decollated list.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
global_epoch_transform: a callable that is used to customize global epoch number.
For example, in evaluation, the evaluator engine might want to track synced epoch number
with the trainer engine.
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32 changes: 24 additions & 8 deletions monai/handlers/regression_metrics.py
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Expand Up @@ -28,8 +28,9 @@ def __init__(self, output_transform: Callable = lambda x: x, save_details: bool
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

Expand All @@ -49,8 +50,13 @@ def __init__(self, output_transform: Callable = lambda x: x, save_details: bool
"""

Args:
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: mean absolute error of every image.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

See also:
Expand All @@ -69,8 +75,13 @@ def __init__(self, output_transform: Callable = lambda x: x, save_details: bool
"""

Args:
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: root mean squared error of every image.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.

See also:
Expand All @@ -93,8 +104,13 @@ def __init__(
Args:
max_val: The dynamic range of the images/volumes (i.e., the difference between the
maximum and the minimum allowed values e.g. 255 for a uint8 image).
output_transform: transform the ignite.engine.state.output into [y_pred, y] pair.
save_details: whether to save metric computation details per image, for example: PSNR of every image.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean squared error of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,

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5 changes: 3 additions & 2 deletions monai/handlers/roc_auc.py
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Expand Up @@ -36,8 +36,9 @@ class ROCAUC(IgniteMetric): # type: ignore[valid-type, misc] # due to optional
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.

Note:
ROCAUC expects y to be comprised of 0's and 1's.
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6 changes: 6 additions & 0 deletions monai/handlers/segmentation_saver.py
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Expand Up @@ -113,9 +113,15 @@ def __init__(
batch_transform: a callable that is used to extract the `meta_data` dictionary of the input images
from `ignite.engine.state.batch`. the purpose is to extract necessary information from the meta data:
filename, affine, original_shape, etc.
`engine.state` and `batch_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
output_transform: a callable that is used to extract the model prediction data from
`ignite.engine.state.output`. the first dimension of its output will be treated as the batch dimension.
each item in the batch will be saved individually.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
name: identifier of logging.logger to use, defaulting to `engine.logger`.

"""
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3 changes: 3 additions & 0 deletions monai/handlers/stats_handler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,9 @@ def __init__(
By default this value logging happens when every iteration completed.
The default behavior is to print loss from output[0] as output is a decollated list
and we replicated loss value for every item of the decollated list.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
global_epoch_transform: a callable that is used to customize global epoch number.
For example, in evaluation, the evaluator engine might want to print synced epoch number
with the trainer engine.
Expand Down
5 changes: 3 additions & 2 deletions monai/handlers/surface_distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,8 +41,9 @@ def __init__(
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
for example: if `ignite.engine.state.output` is `{"pred": xxx, "label": xxx, "other": xxx}`,
output_transform can be `lambda x: (x["pred"], x["label"])`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: surface dice
of every image. default to True, will save to `engine.state.metric_details` dict with the metric name as key.

Expand Down
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