[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

monailabel/tasks/infer/basic_infer.py

@@ -12,11 +12,13 @@
 import copy
 import logging
 import os
+import shutil
 import time
 from abc import abstractmethod
 from enum import Enum
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
+import numpy as np
 import torch
 from monai.data import decollate_batch
 from monai.inferers import Inferer, SimpleInferer, SlidingWindowInferer
@@ -25,13 +27,10 @@
 from monailabel.interfaces.exception import MONAILabelError, MONAILabelException
 from monailabel.interfaces.tasks.infer_v2 import InferTask, InferType
 from monailabel.interfaces.utils.transform import dump_data, run_transforms
+from monailabel.tasks.infer.prompt_utils import check_prompts_format, prompt_run_inferer
 from monailabel.transform.cache import CacheTransformDatad
 from monailabel.transform.writer import ClassificationWriter, DetectionWriter, Writer
 from monailabel.utils.others.generic import device_list, device_map, name_to_device
-from monailabel.tasks.infer.prompt_utils import prompt_run_inferer, check_prompts_format
-
-import shutil
-import numpy as np
 
 rearrange, _ = optional_import("einops", name="rearrange")
 
@@ -404,7 +403,7 @@ def __call__(self, data):
             if d is None:
                 return run_transforms(data, transforms, log_prefix="PRE", use_compose=False)
             return run_transforms(d, post_cache, log_prefix="PRE", use_compose=False) if post_cache else d
-        print('Finddddddddddd data pathhhhhhhhhhhhh:', data)
+        print("Finddddddddddd data pathhhhhhhhhhhhh:", data)
         return run_transforms(data, transforms, log_prefix="PRE", use_compose=False)
 
     def run_invert_transforms(self, data: Dict[str, Any], pre_transforms, names):
@@ -513,8 +512,15 @@ def run_inferer(self, data: Dict[str, Any], convert_to_batch=True, device="cuda"
         network = self._get_network(device, data)
         modelname = data.get("model", None)
         if network:
-            if "vista" in modelname: 
-                return prompt_run_inferer(data, inferer, network, input_key=self.input_key, output_label_key=self.output_label_key, device=device)
+            if "vista" in modelname:
+                return prompt_run_inferer(
+                    data,
+                    inferer,
+                    network,
+                    input_key=self.input_key,
+                    output_label_key=self.output_label_key,
+                    device=device,
+                )
             else:
                 inputs = data[self.input_key]
                 inputs = inputs if torch.is_tensor(inputs) else torch.from_numpy(inputs)
@@ -523,7 +529,7 @@ def run_inferer(self, data: Dict[str, Any], convert_to_batch=True, device="cuda"
 
                 with torch.no_grad():
                     outputs = inferer(
-                        inputs, 
+                        inputs,
                         network,
                     )
 

monailabel/tasks/infer/prompt_utils.py

@@ -2,12 +2,13 @@
 
 import os
 import shutil
-import torch
-import numpy as np
-from monai.utils import optional_import
-from monai.data import decollate_batch
 from typing import Any, Dict
+
+import numpy as np
+import torch
 from einops import rearrange
+from monai.data import decollate_batch
+from monai.utils import optional_import
 
 rearrange, _ = optional_import("einops", name="rearrange")
 
@@ -20,6 +21,7 @@ def transform_points(point, affine):
     point = rearrange(point, "d (b n)-> b n d", b=bs)[:, :, :3]
     return point
 
+
 def check_prompts_format(label_prompt, points, point_labels):
     """check the format of user prompts
     label_prompt: [1,2,3,4,...,B] List of tensors
@@ -28,7 +30,9 @@ def check_prompts_format(label_prompt, points, point_labels):
     """
     # check prompt is given
     if label_prompt is None and points is None:
-        everything_labels = list(set([i+1 for i in range(132)]) - set([2,16,18,20,21,23,24,25,26,27,128,129,130,131,132]))
+        everything_labels = list(
+            {i + 1 for i in range(132)} - {2, 16, 18, 20, 21, 23, 24, 25, 26, 27, 128, 129, 130, 131, 132}
+        )
         if everything_labels is not None:
             label_prompt = [torch.tensor(_) for _ in everything_labels]
 
@@ -68,7 +72,16 @@ def check_prompts_format(label_prompt, points, point_labels):
             raise ValueError("Points must be given if point labels are given.")
     return label_prompt, points, point_labels
 
-def prompt_run_inferer(data: Dict[str, Any], inferer, network, input_key="image", output_label_key="pred", device="cuda", convert_to_batch=True):
+
+def prompt_run_inferer(
+    data: Dict[str, Any],
+    inferer,
+    network,
+    input_key="image",
+    output_label_key="pred",
+    device="cuda",
+    convert_to_batch=True,
+):
     # Retrieve label_prompt, points, and point_labels
     label_prompt, points, point_labels = (
         data.get("label_prompt", None),
@@ -95,7 +108,7 @@ def prompt_run_inferer(data: Dict[str, Any], inferer, network, input_key="image"
     if points is not None:
         points = torch.as_tensor([points])
 
-        original_spatial_shape = np.array(data['image_meta_dict']['spatial_shape'])
+        original_spatial_shape = np.array(data["image_meta_dict"]["spatial_shape"])
         resized_spatial_shape = np.array(data[input_key].shape[1:])
         scaling_factors = resized_spatial_shape / original_spatial_shape
         transformed_point = points * scaling_factors
@@ -111,11 +124,9 @@ def prompt_run_inferer(data: Dict[str, Any], inferer, network, input_key="image"
     inputs = inputs[None].to(torch.device(device))
     inputs = inputs.to(torch.device(device))
 
-
     with torch.no_grad():
         outputs = inferer(inputs, network, point_coords=points, point_labels=point_labels, class_vector=label_prompt)
 
-
     if device.startswith("cuda"):
         torch.cuda.empty_cache()
 

plugins/ohifv3_vista/extensions/monai-label/src/components/actions/AutoSegmentation.tsx

@@ -26,13 +26,13 @@ export default class AutoSegmentation extends BaseTab {
 
     if (this.props.viewConstants.SupportedClasses) {
       const labels = this.props.viewConstants.SupportedClasses
-      let labelIndex = 1; 
+      let labelIndex = 1;
 
       for (const key in labels) {
         const organName = labels[key];
         console.log(organName)
         if (organName.toLowerCase() !== 'background') {
-          const hexColor = segmentColors[labelIndex] || '#000000'; 
+          const hexColor = segmentColors[labelIndex] || '#000000';
 
           selectedOrgans[organName] = { checked: false, color: hexColor };
           labelIndex++;
@@ -107,13 +107,13 @@ export default class AutoSegmentation extends BaseTab {
 
   onChangeOrgans = (organ, evt) => {
     this.setState((prevState) => {
-      const selectedOrgans = { ...prevState.selectedOrgans }; 
+      const selectedOrgans = { ...prevState.selectedOrgans };
 
       selectedOrgans[organ] = {
         ...selectedOrgans[organ],
         checked: evt.target.checked,
       };
-  
+
       return { selectedOrgans };
     });
   };

plugins/ohifv3_vista/extensions/monai-label/src/components/actions/PointPrompts.tsx

@@ -50,12 +50,12 @@ export default class PointPrompts extends BaseTab {
 
     if (this.props.viewConstants.SupportedClasses) {
       const labels = this.props.viewConstants.SupportedClasses
-      let labelIndex = 1; 
+      let labelIndex = 1;
 
       for (const key in labels) {
         const organName = labels[key];
         if (organName.toLowerCase() !== 'background') {
-          const hexColor = segmentColors[labelIndex] || '#000000'; 
+          const hexColor = segmentColors[labelIndex] || '#000000';
           selectedOrgans[organName] = { checked: false, color: hexColor };
           labelIndex++;
         }
@@ -107,7 +107,7 @@ export default class PointPrompts extends BaseTab {
     const config = this.props.onOptionsConfig();
     const params =
       config && config.infer && config.infer[model] ? config.infer[model] : {};
-      
+
 
     // let seriesInstanceUID = viewConstants.SeriesInstanceUID;
 
@@ -299,13 +299,13 @@ export default class PointPrompts extends BaseTab {
 
   onChangeOrgans = (organ, evt) => {
     this.setState((prevState) => {
-      const selectedOrgans = { ...prevState.selectedOrgans }; 
+      const selectedOrgans = { ...prevState.selectedOrgans };
 
       selectedOrgans[organ] = {
         ...selectedOrgans[organ],
         checked: evt.target.checked,
       };
-  
+
       return { selectedOrgans };
     });
   };
@@ -465,7 +465,7 @@ export default class PointPrompts extends BaseTab {
                 </tr>
 
                 {Object.entries(this.state.selectedOrgans).map(([organ, { color, checked }]) => (
-                  
+
                 <tr
                   key={organ}
                   className='clickable-row'

plugins/ohifv3_vista/run.sh

@@ -20,4 +20,4 @@ cp -r ../../monailabel/endpoints/static/ohif www/html/ohif
 cp -f config/monai_label.js www/html/ohif/app-config.js
 
 # nginx -p `pwd` -c config/nginx.conf -e logs/error.log
-nginx -p `pwd` -c config/nginx.conf 
+nginx -p `pwd` -c config/nginx.conf