Add missing components to API doc

docs/source/apps.rst

@@ -114,7 +114,11 @@ Clara MMARs
 .. automodule:: monai.apps.pathology.transforms.spatial.array
 .. autoclass:: SplitOnGrid
     :members:
+.. autoclass:: TileOnGrid
+    :members:
 
 .. automodule:: monai.apps.pathology.transforms.spatial.dictionary
 .. autoclass:: SplitOnGridd
     :members:
+.. autoclass:: TileOnGridd
+    :members:

docs/source/data.rst

@@ -208,6 +208,7 @@ Decathlon Datalist
 .. autofunction:: monai.data.load_decathlon_datalist
 .. autofunction:: monai.data.load_decathlon_properties
 .. autofunction:: monai.data.check_missing_files
+.. autofunction:: monai.data.create_cross_validation_datalist
 
 
 DataLoader

docs/source/engines.rst

@@ -11,7 +11,6 @@ Multi-GPU data parallel
 .. automodule:: monai.engines.multi_gpu_supervised_trainer
   :members:
 
-
 Workflows
 ---------
 
@@ -56,3 +55,8 @@ Workflows
 ~~~~~~~~~~~~~~~~~~~
 .. autoclass:: EnsembleEvaluator
     :members:
+
+Utilities
+---------
+.. automodule:: monai.engines.utils
+  :members:

docs/source/metrics.rst

@@ -8,6 +8,8 @@ Metrics
 
 `FROC`
 ------
+.. autofunction:: compute_fp_tp_probs
+.. autofunction:: compute_froc_curve_data
 .. autofunction:: compute_froc_score
 
 `Metric`
@@ -47,13 +49,15 @@ Metrics
 `Confusion matrix`
 ------------------
 .. autofunction:: get_confusion_matrix
+.. autofunction:: compute_confusion_matrix_metric
 
 .. autoclass:: ConfusionMatrixMetric
     :members:
 
 `Hausdorff distance`
 --------------------
 .. autofunction:: compute_hausdorff_distance
+.. autofunction:: compute_percent_hausdorff_distance
 
 .. autoclass:: HausdorffDistanceMetric
     :members:
@@ -89,3 +93,8 @@ Metrics
 --------------------
 .. autoclass:: CumulativeAverage
     :members:
+
+Utilities
+---------
+.. automodule:: monai.metrics.utils
+  :members:

docs/source/networks.rst

@@ -21,7 +21,7 @@ Blocks
     :members:
 
 `CRF`
-~~~~~~~~~~~~~
+~~~~~
 .. autoclass:: CRF
     :members:
 
@@ -73,6 +73,8 @@ Blocks
     :members:
 .. autoclass:: UnetUpBlock
     :members:
+.. autoclass:: UnetOutBlock
+    :members:
 
 `SegResnet Block`
 ~~~~~~~~~~~~~~~~~
@@ -254,6 +256,11 @@ Layers
 .. automodule:: monai.networks.layers.Conv
   :members:
 
+`Pad`
+~~~~~
+.. automodule:: monai.networks.layers.Pad
+  :members:
+
 `Pool`
 ~~~~~~
 .. automodule:: monai.networks.layers.Pool
@@ -300,7 +307,7 @@ Layers
     :members:
 
 `PHLFilter`
-~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~
 .. autoclass:: PHLFilter
 
 `GaussianMixtureModel`
@@ -386,6 +393,11 @@ Nets
 .. autoclass:: EfficientNet
   :members:
 
+`BlockArgs`
+~~~~~~~~~~~
+.. autoclass:: BlockArgs
+  :members:
+
 `EfficientNetBN`
 ~~~~~~~~~~~~~~~~
 .. autoclass:: EfficientNetBN
@@ -406,6 +418,11 @@ Nets
 .. autoclass:: SegResNetVAE
   :members:
 
+`ResNet`
+~~~~~~~~
+.. autoclass:: ResNet
+  :members:
+
 `SENet`
 ~~~~~~~
 .. autoclass:: SENet
@@ -513,6 +530,11 @@ Nets
 .. autoclass:: FullyConnectedNet
   :members:
 
+`VarFullyConnectedNet`
+~~~~~~~~~~~~~~~~~~~~~~
+.. autoclass:: VarFullyConnectedNet
+  :members:
+
 `Generator`
 ~~~~~~~~~~~
 .. autoclass:: Generator

docs/source/optimizers.rst

@@ -6,6 +6,11 @@ Optimizers
 ==========
 .. currentmodule:: monai.optimizers
 
+`LearningRateFinder`
+--------------------
+.. autoclass:: LearningRateFinder
+    :members:
+
 `Novograd`
 ----------
 .. autoclass:: Novograd
@@ -14,3 +19,18 @@ Optimizers
 `Generate parameter groups`
 ---------------------------
 .. autofunction:: generate_param_groups
+
+`ExponentialLR`
+---------------
+.. autoclass:: ExponentialLR
+    :members:
+
+`LinearLR`
+----------
+.. autoclass:: LinearLR
+    :members:
+
+`WarmupCosineSchedule`
+----------------------
+.. autoclass:: WarmupCosineSchedule
+    :members:

docs/source/transforms.rst

@@ -43,6 +43,11 @@ Generic Interfaces
 .. autoclass:: InvertibleTransform
     :members:
 
+`TraceableTransform`
+^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: TraceableTransform
+    :members:
+
 `BatchInverseTransform`
 ^^^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: BatchInverseTransform
@@ -64,6 +69,12 @@ Vanilla Transforms
 Crop and Pad
 ^^^^^^^^^^^^
 
+`PadListDataCollate`
+""""""""""""""""""""
+.. autoclass:: PadListDataCollate
+    :members:
+    :special-members: __call__
+
 `Pad`
 """""
 .. autoclass:: Pad
@@ -395,6 +406,12 @@ Intensity
     :members:
     :special-members: __call__
 
+`RandRicianNoise`
+"""""""""""""""""
+.. autoclass:: RandRicianNoise
+    :members:
+    :special-members: __call__
+
 `RandCoarseTransform`
 """""""""""""""""""""
 .. autoclass:: RandCoarseTransform
@@ -830,6 +847,12 @@ Utility
     :members:
     :special-members: __call__
 
+`RemoveRepeatedChannel`
+"""""""""""""""""""""""
+.. autoclass:: RemoveRepeatedChannel
+    :members:
+    :special-members: __call__
+
 `LabelToMask`
 """""""""""""
 .. autoclass:: LabelToMask
@@ -1568,6 +1591,12 @@ Utility (Dict)
     :members:
     :special-members: __call__
 
+`ToPIL`
+"""""""
+.. autoclass:: ToPIL
+    :members:
+    :special-members: __call__
+
 `ToCupyd`
 """""""""
 .. autoclass:: ToCupyd
@@ -1710,6 +1739,11 @@ Transform Adaptors
 ------------------
 .. automodule:: monai.transforms.adaptors
 
+`FunctionSignature`
+^^^^^^^^^^^^^^^^^^^
+.. autoclass:: FunctionSignature
+    :members:
+
 `adaptor`
 ^^^^^^^^^
 .. autofunction:: monai.transforms.adaptors.adaptor

docs/source/utils.rst

@@ -51,3 +51,28 @@ Type conversion
 ---------------
 .. automodule:: monai.utils.type_conversion
   :members:
+
+Decorators
+----------
+.. automodule:: monai.utils.decorators
+  :members:
+
+Distributed Data Parallel
+-------------------------
+.. automodule:: monai.utils.dist
+  :members:
+
+Enums
+-----
+.. automodule:: monai.utils.enums
+  :members:
+
+Jupyter Utilities
+-----------------
+.. automodule:: monai.utils.jupyter_utils
+  :members:
+
+State Cacher
+------------
+.. automodule:: monai.utils.state_cacher
+  :members:

docs/source/visualize.rst

@@ -24,3 +24,8 @@ Occlusion sensitivity
 
 .. automodule:: monai.visualize.occlusion_sensitivity
   :members:
+
+Utilities
+---------
+.. automodule:: monai.visualize.utils
+  :members:

monai/engines/utils.py

@@ -166,9 +166,9 @@ class PrepareBatchExtraInput(PrepareBatch):
 
     Args:
         extra_keys: if a string or list provided, every item is the key of extra data in current batch,
-            and will pass the extra data to the network(*args) in order.
+            and will pass the extra data to the `network(*args)` in order.
             If a dictionary is provided, every `{k, v}` pair is the key of extra data in current batch,
-            `k` the param name in network, `v` is the key of extra data in current batch,
+            `k` is the param name in network, `v` is the key of extra data in current batch,
             and will pass the `{k1: batch[v1], k2: batch[v2], ...}` as kwargs to the network.
 
     """

monai/metrics/utils.py

@@ -28,13 +28,15 @@
 def ignore_background(y_pred: Union[np.ndarray, torch.Tensor], y: Union[np.ndarray, torch.Tensor]):
     """
     This function is used to remove background (the first channel) for `y_pred` and `y`.
+
     Args:
         y_pred: predictions. As for classification tasks,
             `y_pred` should has the shape [BN] where N is larger than 1. As for segmentation tasks,
             the shape should be [BNHW] or [BNHWD].
         y: ground truth, the first dim is batch.
 
     """
+
     y = y[:, 1:] if y.shape[1] > 1 else y
     y_pred = y_pred[:, 1:] if y_pred.shape[1] > 1 else y_pred
     return y_pred, y

monai/networks/nets/resnet.py

@@ -154,6 +154,7 @@ class ResNet(nn.Module):
     ResNet based on: `Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_
     and `Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? <https://arxiv.org/pdf/1711.09577.pdf>`_.
     Adapted from `<https://github.com/kenshohara/3D-ResNets-PyTorch/tree/master/models>`_.
+
     Args:
         block: which ResNet block to use, either Basic or Bottleneck.
         layers: how many layers to use.

monai/optimizers/__init__.py

@@ -10,5 +10,6 @@
 # limitations under the License.
 
 from .lr_finder import LearningRateFinder
+from .lr_scheduler import ExponentialLR, LinearLR, WarmupCosineSchedule
 from .novograd import Novograd
 from .utils import generate_param_groups

monai/optimizers/lr_finder.py

@@ -146,30 +146,30 @@ class LearningRateFinder:
     and what is the optimal learning rate.
 
     Example (fastai approach):
-        >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
-        >>> lr_finder.range_test(data_loader, end_lr=100, num_iter=100)
-        >>> lr_finder.get_steepest_gradient()
-        >>> lr_finder.plot() # to inspect the loss-learning rate graph
+    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
+    >>> lr_finder.range_test(data_loader, end_lr=100, num_iter=100)
+    >>> lr_finder.get_steepest_gradient()
+    >>> lr_finder.plot() # to inspect the loss-learning rate graph
 
     Example (Leslie Smith's approach):
-        >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
-        >>> lr_finder.range_test(train_loader, val_loader=val_loader, end_lr=1, num_iter=100, step_mode="linear")
+    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
+    >>> lr_finder.range_test(train_loader, val_loader=val_loader, end_lr=1, num_iter=100, step_mode="linear")
 
     Gradient accumulation is supported; example:
-        >>> train_data = ...    # prepared dataset
-        >>> desired_bs, real_bs = 32, 4         # batch size
-        >>> accumulation_steps = desired_bs // real_bs     # required steps for accumulation
-        >>> data_loader = torch.utils.data.DataLoader(train_data, batch_size=real_bs, shuffle=True)
-        >>> acc_lr_finder = LearningRateFinder(net, optimizer, criterion)
-        >>> acc_lr_finder.range_test(data_loader, end_lr=10, num_iter=100, accumulation_steps=accumulation_steps)
+    >>> train_data = ...    # prepared dataset
+    >>> desired_bs, real_bs = 32, 4         # batch size
+    >>> accumulation_steps = desired_bs // real_bs     # required steps for accumulation
+    >>> data_loader = torch.utils.data.DataLoader(train_data, batch_size=real_bs, shuffle=True)
+    >>> acc_lr_finder = LearningRateFinder(net, optimizer, criterion)
+    >>> acc_lr_finder.range_test(data_loader, end_lr=10, num_iter=100, accumulation_steps=accumulation_steps)
 
     By default, image will be extracted from data loader with x["image"] and x[0], depending on whether
     batch data is a dictionary or not (and similar behaviour for extracting the label). If your data loader
     returns something other than this, pass a callable function to extract it, e.g.:
-        >>> image_extractor = lambda x: x["input"]
-        >>> label_extractor = lambda x: x[100]
-        >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
-        >>> lr_finder.range_test(train_loader, val_loader, image_extractor, label_extractor)
+    >>> image_extractor = lambda x: x["input"]
+    >>> label_extractor = lambda x: x[100]
+    >>> lr_finder = LearningRateFinder(net, optimizer, criterion)
+    >>> lr_finder.range_test(train_loader, val_loader, image_extractor, label_extractor)
 
     References:
     Modified from: https://github.com/davidtvs/pytorch-lr-finder.

monai/transforms/intensity/array.py

@@ -129,10 +129,10 @@ class RandRicianNoise(RandomizableTransform):
     """
     Add Rician noise to image.
     Rician noise in MRI is the result of performing a magnitude operation on complex
-    data with Gaussian noise of the same variance in both channels, as described in `Noise in Magnitude
-    Magnetic Resonance Images <https://doi.org/10.1002/cmr.a.20124>`_. This transform is adapted from
-    `DIPY<https://github.com/dipy/dipy>`_. See also: `The rician distribution of noisy mri data
-    <https://doi.org/10.1002/mrm.1910340618>`_.
+    data with Gaussian noise of the same variance in both channels, as described in
+    `Noise in Magnitude Magnetic Resonance Images <https://doi.org/10.1002/cmr.a.20124>`_.
+    This transform is adapted from `DIPY <https://github.com/dipy/dipy>`_.
+    See also: `The rician distribution of noisy mri data <https://doi.org/10.1002/mrm.1910340618>`_.
 
     Args:
         prob: Probability to add Rician noise.