Fix argument names in docstrings

monai/apps/auto3dseg/bundle_gen.py

@@ -491,7 +491,7 @@ class BundleGen(AlgoGen):
         mlflow_tracking_uri: a tracking URI for MLflow server which could be local directory or address of
             the remote tracking Server; MLflow runs will be recorded locally in algorithms' model folder if
             the value is None.
-        mlfow_experiment_name: a string to specify the experiment name for MLflow server.
+        mlflow_experiment_name: a string to specify the experiment name for MLflow server.
     .. code-block:: bash
 
         python -m monai.apps.auto3dseg BundleGen generate --data_stats_filename="../algorithms/datastats.yaml"

monai/apps/detection/metrics/matching.py

@@ -193,7 +193,7 @@ def _matching_no_gt(
 
     Args:
         iou_thresholds: defined which IoU thresholds should be evaluated
-        dt_scores: predicted scores
+        pred_scores: predicted scores
         max_detections: maximum number of allowed detections per image.
             This functions uses this parameter to stay consistent with
             the actual matching function which needs this limit.

monai/inferers/inferer.py

@@ -1251,7 +1251,7 @@ def _get_decoder_log_likelihood(
         given image. Code adapted from https://github.com/openai/improved-diffusion.
 
         Args:
-            input: the target images. It is assumed that this was uint8 values,
+            inputs: the target images. It is assumed that this was uint8 values,
                       rescaled to the range [-1, 1].
             means: the Gaussian mean Tensor.
             log_scales: the Gaussian log stddev Tensor.

monai/networks/nets/patchgan_discriminator.py

@@ -126,7 +126,7 @@ class PatchDiscriminator(nn.Sequential):
         out_channels: number of output channels
         num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in the discriminator.
         kernel_size: kernel size of the convolution layers
-        act: activation type and arguments. Defaults to LeakyReLU.
+        activation: activation type and arguments. Defaults to LeakyReLU.
         norm: feature normalization type and arguments. Defaults to batch norm.
         bias: whether to have a bias term in convolution blocks. Defaults to False.
         padding: padding to be applied to the convolutional layers

monai/networks/schedulers/ddpm.py

@@ -131,7 +131,7 @@ def _get_mean(self, timestep: int, x_0: torch.Tensor, x_t: torch.Tensor) -> torc
 
         Args:
             timestep: current timestep.
-            x0: the noise-free input.
+            x_0: the noise-free input.
             x_t: the input noised to timestep t.
 
         Returns: