fixes discussion #5678 suppress_invalid_tags (#5721)

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

fixes #5678
fixes #5724
fixes #5725

according to
https://pydicom.github.io/pydicom/stable/reference/generated/pydicom.dataset.Dataset.html#pydicom.dataset.Dataset.to_json_dict

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monai/apps/detection/networks/retinanet_detector.py

@@ -604,7 +604,7 @@ def postprocess_detections(
     ) -> List[Dict[str, Tensor]]:
         """
         Postprocessing to generate detection result from classification logits and box regression.
-        Use self.box_selector to select the final outut boxes for each image.
+        Use self.box_selector to select the final output boxes for each image.
 
         Args:
             head_outputs_reshape: reshaped head_outputs. ``head_output_reshape[self.cls_key]`` is a Tensor

monai/apps/nuclick/transforms.py

@@ -315,21 +315,21 @@ def __call__(self, data):
         mask = d[self.label] if isinstance(d[self.label], torch.Tensor) else torch.from_numpy(d[self.label])
 
         inc_sig = self.inclusion_map(mask[0], dtype=image.dtype)
-        inc_sig = self._apply_gaussion(inc_sig)
+        inc_sig = self._apply_gaussian(inc_sig)
         if self.add_exclusion_map:
             others = d[self.others] if isinstance(d[self.others], torch.Tensor) else torch.from_numpy(d[self.others])
             exc_sig = self.exclusion_map(
                 others[0], dtype=image.dtype, drop_rate=self.drop_rate, jitter_range=self.jitter_range
             )
-            exc_sig = self._apply_gaussion(exc_sig)
+            exc_sig = self._apply_gaussian(exc_sig)
             image = torch.cat((image, inc_sig[None], exc_sig[None]), dim=0)
         else:
             image = torch.cat((image, inc_sig[None]), dim=0)
 
         d[self.image] = image if isinstance(d[self.image], torch.Tensor) else convert_to_numpy(image)
         return d
 
-    def _apply_gaussion(self, t):
+    def _apply_gaussian(self, t):
         if not self.gaussian or torch.count_nonzero(t) == 0:
             return t
         x = GaussianFilter(spatial_dims=2, truncated=self.truncated, sigma=self.sigma)(t.unsqueeze(0).unsqueeze(0))
@@ -500,19 +500,19 @@ def get_patches_and_signals(self, img, click_map, bounding_boxes, cx, cy, x, y):
             this_click_map[cx[i], cy[i]] = 1
 
             nuc_points = this_click_map[x_start:x_end, y_start:y_end]
-            nuc_points = self._apply_gaussion(nuc_points)
+            nuc_points = self._apply_gaussian(nuc_points)
 
             if self.add_exclusion_map:
                 others_click_map = ((click_map - this_click_map) > 0).type(img.dtype)
                 other_points = others_click_map[x_start:x_end, y_start:y_end]
-                other_points = self._apply_gaussion(other_points)
+                other_points = self._apply_gaussian(other_points)
                 patches.append(torch.cat([patch, nuc_points[None], other_points[None]]))
             else:
                 patches.append(torch.cat([patch, nuc_points[None]]))
 
         return torch.stack(patches)
 
-    def _apply_gaussion(self, t):
+    def _apply_gaussian(self, t):
         if not self.gaussian or torch.count_nonzero(t) == 0:
             return t
         x = GaussianFilter(spatial_dims=2, truncated=self.truncated, sigma=self.sigma)(t.unsqueeze(0).unsqueeze(0))

monai/apps/pathology/engines/utils.py

@@ -28,7 +28,7 @@ class PrepareBatchHoVerNet(PrepareBatch):
 
     Args:
         extra_keys: If a sequence of strings is provided, values from the input dictionary are extracted from
-            those keys and passed to the nework as extra positional arguments.
+            those keys and passed to the network as extra positional arguments.
     """
 
     def __init__(self, extra_keys: Sequence[str]) -> None:

monai/apps/reconstruction/transforms/array.py

@@ -50,7 +50,7 @@ def __init__(
                 same length as center_fractions. If multiple values are
                 provided, then one of these is chosen uniformly each time.
             spatial_dims: Number of spatial dims (e.g., it's 2 for a 2D data;
-                it's also 2 for psuedo-3D datasets like the fastMRI dataset).
+                it's also 2 for pseudo-3D datasets like the fastMRI dataset).
                 The last spatial dim is selected for sampling. For the fastMRI
                 dataset, k-space has the form (...,num_slices,num_coils,H,W)
                 and sampling is done along W. For a general 3D data with the

monai/apps/reconstruction/transforms/dictionary.py

@@ -83,7 +83,7 @@ class RandomKspaceMaskd(RandomizableTransform, MapTransform):
             same length as center_fractions. If multiple values are provided,
             then one of these is chosen uniformly each time.
         spatial_dims: Number of spatial dims (e.g., it's 2 for a 2D data; it's
-            also 2 for psuedo-3D datasets like the fastMRI dataset).
+            also 2 for pseudo-3D datasets like the fastMRI dataset).
             The last spatial dim is selected for sampling. For the fastMRI
             dataset, k-space has the form (...,num_slices,num_coils,H,W)
             and sampling is done along W. For a general 3D data with the
@@ -151,7 +151,7 @@ class EquispacedKspaceMaskd(RandomKspaceMaskd):
             length as center_fractions. If multiple values are provided,
             then one of these is chosen uniformly each time.
         spatial_dims: Number of spatial dims (e.g., it's 2 for a 2D data;
-            it's also 2 for  psuedo-3D datasets like the fastMRI dataset).
+            it's also 2 for  pseudo-3D datasets like the fastMRI dataset).
             The last spatial dim is selected for sampling. For the fastMRI
             dataset, k-space has the form (...,num_slices,num_coils,H,W)
             and sampling is done along W. For a general 3D data with the shape

monai/bundle/scripts.py

@@ -366,7 +366,7 @@ def get_all_bundles_list(
     """
     Get all bundles names (and the latest versions) that are stored in the release of specified repository
     with the provided tag. The default values of arguments correspond to the release of MONAI model zoo.
-    In order to increase the rate limits of calling GIthub APIs, you can input your personal access token.
+    In order to increase the rate limits of calling Github APIs, you can input your personal access token.
     Please check the following link for more details about rate limiting:
     https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting
 
@@ -401,7 +401,7 @@ def get_bundle_versions(
     """
     Get the latest version, as well as all existing versions of a bundle that is stored in the release of specified
     repository with the provided tag.
-    In order to increase the rate limits of calling GIthub APIs, you can input your personal access token.
+    In order to increase the rate limits of calling Github APIs, you can input your personal access token.
     Please check the following link for more details about rate limiting:
     https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting
 
@@ -439,7 +439,7 @@ def get_bundle_info(
     Get all information
     (include "id", "name", "size", "download_count", "browser_download_url", "created_at", "updated_at") of a bundle
     with the specified bundle name and version.
-    In order to increase the rate limits of calling GIthub APIs, you can input your personal access token.
+    In order to increase the rate limits of calling Github APIs, you can input your personal access token.
     Please check the following link for more details about rate limiting:
     https://docs.github.com/en/rest/overview/resources-in-the-rest-api#rate-limiting
 
@@ -544,7 +544,7 @@ def run(
         logging_file: config file for `logging` module in the program, default to `None`. for more details:
             https://docs.python.org/3/library/logging.config.html#logging.config.fileConfig.
         tracking: enable the experiment tracking feature at runtime with optionally configurable and extensible.
-            if "mlflow", will add `MLFlowHandler` to the parsed bundle with default loggging settings,
+            if "mlflow", will add `MLFlowHandler` to the parsed bundle with default logging settings,
             if other string, treat it as file path to load the logging settings, if `dict`,
             treat it as logging settings, otherwise, use all the default settings.
             will patch the target config content with `tracking handlers` and the top-level items of `configs`.
@@ -925,6 +925,8 @@ def init_bundle(
         network: if given instead of ckpt_file this network's weights will be stored in bundle
         dataset_license: if `True`, a default license file called "data_license.txt" will be produced. This
             file is required if there are any license conditions stated for data your bundle uses.
+        metadata_str: optional metadata string to write to bundle, if not given a default will be used.
+        inference_str: optional inference string to write to bundle, if not given a default will be used.
     """
     if metadata_str is None:
         metadata_str = DEFAULT_METADATA

monai/bundle/utils.py

@@ -153,7 +153,7 @@
     },
 }
 
-DEFAULT_EXP_MGMT_SETTINGS = {"mlflow": DEFAULT_MLFLOW_SETTINGS}  # default expriment management settings
+DEFAULT_EXP_MGMT_SETTINGS = {"mlflow": DEFAULT_MLFLOW_SETTINGS}  # default experiment management settings
 
 
 def load_bundle_config(bundle_path: str, *config_names, **load_kw_args) -> Any:

monai/data/box_utils.py

@@ -1055,7 +1055,7 @@ def non_max_suppression(
     boxes_t, *_ = convert_data_type(boxes, torch.Tensor)
     scores_t, *_ = convert_to_dst_type(scores, boxes_t)
 
-    # sort boxes in desending order according to the scores
+    # sort boxes in descending order according to the scores
     sort_idxs = torch.argsort(scores_t, dim=0, descending=True)
     boxes_sort = deepcopy(boxes_t)[sort_idxs, :]
 

monai/data/dataset.py

@@ -978,10 +978,10 @@ class SmartCacheDataset(Randomizable, CacheDataset):
             will take the minimum of (cache_num, data_length x cache_rate, data_length).
         num_init_workers: the number of worker threads to initialize the cache for first epoch.
             If num_init_workers is None then the number returned by os.cpu_count() is used.
-            If a value less than 1 is speficied, 1 will be used instead.
+            If a value less than 1 is specified, 1 will be used instead.
         num_replace_workers: the number of worker threads to prepare the replacement cache for every epoch.
             If num_replace_workers is None then the number returned by os.cpu_count() is used.
-            If a value less than 1 is speficied, 1 will be used instead.
+            If a value less than 1 is specified, 1 will be used instead.
         progress: whether to display a progress bar when caching for the first epoch.
         shuffle: whether to shuffle the whole data list before preparing the cache content for first epoch.
             it will not modify the original input data sequence in-place.

monai/data/fft_utils.py

@@ -19,7 +19,7 @@
 def ifftn_centered(ksp: NdarrayOrTensor, spatial_dims: int, is_complex: bool = True) -> NdarrayOrTensor:
     """
     Pytorch-based ifft for spatial_dims-dim signals. "centered" means this function automatically takes care
-    of the required ifft and fft shifts. This function calls monai.metworks.blocks.fft_utils_t.ifftn_centered_t.
+    of the required ifft and fft shifts. This function calls monai.networks.blocks.fft_utils_t.ifftn_centered_t.
     This is equivalent to do fft in numpy based on numpy.fft.ifftn, numpy.fft.fftshift, and numpy.fft.ifftshift
 
     Args:
@@ -58,7 +58,7 @@ def ifftn_centered(ksp: NdarrayOrTensor, spatial_dims: int, is_complex: bool = T
 def fftn_centered(im: NdarrayOrTensor, spatial_dims: int, is_complex: bool = True) -> NdarrayOrTensor:
     """
     Pytorch-based fft for spatial_dims-dim signals. "centered" means this function automatically takes care
-    of the required ifft and fft shifts. This function calls monai.metworks.blocks.fft_utils_t.fftn_centered_t.
+    of the required ifft and fft shifts. This function calls monai.networks.blocks.fft_utils_t.fftn_centered_t.
     This is equivalent to do ifft in numpy based on numpy.fft.fftn, numpy.fft.fftshift, and numpy.fft.ifftshift
 
     Args: