Misc changes

.dockerignore

@@ -1,3 +1,5 @@
+**/tmp*
+test_data
 .ruff_cache
 .tox
 *.egg-info

superpixel_classification/SuperpixelClassification/SuperpixelClassification.xml

@@ -61,7 +61,7 @@
     <description>Superpixel parameters</description>
     <boolean>
       <name>gensuperpixels</name>
-      <longflag>generate-superpxiels</longflag>
+      <longflag>generate-superpixels</longflag>
       <description>If an image does not have an annotation with superpixels, generate one</description>
       <label>Generate superpixels</label>
       <default>true</default>

superpixel_classification/SuperpixelClassification/SuperpixelClassificationBase.py

@@ -416,12 +416,14 @@ def trainModelAddItem(self, gc, record, item, annotrec, elem, feature,
             item['name'], annotrec['annotation']['name'], annotrec['_id'], annotrec['_version']))
         featurePath = os.path.join(record['tempdir'], feature['name'])
         gc.downloadFile(feature['_id'], featurePath)
+        print(f"Downloaded '{feature['_id']}' to '{featurePath}'")
         with h5py.File(featurePath, 'r') as ffptr:
             fds = ffptr['images']
             for idx, labelnum in enumerate(elem['values']):
                 if labelnum and labelnum < len(elem['categories']):
                     labelname = elem['categories'][labelnum]['label']
                     if labelname in excludeLabelList:
+                        skipped_excluded += 1
                         continue
                     if labelname not in record['groups']:
                         record['groups'][labelname] = elem['categories'][labelnum]
@@ -449,6 +451,7 @@ def trainModelAddItem(self, gc, record, item, annotrec, elem, feature,
                     record['lastlog'] = time.time()
                     print(record['ds'].shape, record['counts'],
                           '%5.3f' % (time.time() - record['starttime']))
+            print(f"Skipped {skipped_excluded} samples with labels that were excluded")
 
     def trainModel(self, gc, folderId, annotationName, features, modelFolderId,
                    batchSize, epochs, trainingSplit, randomInput, labelList,
@@ -506,11 +509,11 @@ def trainModel(self, gc, folderId, annotationName, features, modelFolderId,
             for attempt in Retrying(stop=stop_after_attempt(self.uploadRetries)):
                 with attempt:
                     modelFile = gc.uploadFileToFolder(modelFolderId, modelPath)
-            print('Saved model')
+            print(f'Saved model to {modelFolderId}')
             for attempt in Retrying(stop=stop_after_attempt(self.uploadRetries)):
                 with attempt:
                     modTrainingFile = gc.uploadFileToFolder(modelFolderId, modTrainingPath)
-            print('Saved modTraining')
+            print(f'Saved modTraining to {modelFolderId}')
             return modelFile, modTrainingFile
 
     def predictLabelsForItem(self, gc, annotationName, annotationFolderId, tempdir, model, item,
@@ -734,7 +737,7 @@ def predictLabels(self, gc, folderId, annotationName, features, modelFolderId,
                     modelFile = next(gc.listFile(item['_id'], limit=1))
                     break
             if not modelFile:
-                print('No model file found')
+                print(f'No model file found in {modelFolderId}')
                 return
             print(modelFile['name'], item)
             modelPath = os.path.join(tempdir, modelFile['name'])
@@ -747,7 +750,7 @@ def predictLabels(self, gc, folderId, annotationName, features, modelFolderId,
                     modTrainingFile = next(gc.listFile(item['_id'], limit=1))
                     break
             if not modTrainingFile:
-                print('No modTraining file found')
+                print(f'No modTraining file found in {modelFolderId}')
                 return
             print(modTrainingFile['name'], item)
             modTrainingPath = os.path.join(tempdir, modTrainingFile['name'])
@@ -797,16 +800,21 @@ def main(self, args):
                     gc, args.images, args.annotationName, args.radius, args.magnification,
                     args.annotationDir, args.numWorkers, prog)
 
+            print("Creating features...")
             features = self.createFeatures(
                 gc, args.images, args.annotationName, args.features, args.patchSize,
                 args.numWorkers, prog)
+            print("Done creating features...")
 
             if args.train:
                 self.trainModel(
                     gc, args.images, args.annotationName, features, args.modeldir, args.batchSize,
                     args.epochs, args.split, args.randominput, args.labels, args.exclude, prog)
+                print("Done training...")
 
             self.predictLabels(
                 gc, args.images, args.annotationName, features, args.modeldir, args.annotationDir,
                 args.heatmaps, args.radius, args.magnification, args.certainty, args.batchSize,
                 prog)
+            print("Done predicting labels...")
+        print("Done, exiting")

superpixel_classification/SuperpixelClassification/SuperpixelClassificationTensorflow.py

@@ -3,6 +3,7 @@
 from typing import Optional
 
 import h5py
+import numpy as np
 import tensorflow as tf
 from SuperpixelClassificationBase import SuperpixelClassificationBase
 

superpixel_classification/SuperpixelClassification/SuperpixelClassificationTorch.py

@@ -171,6 +171,7 @@ def trainModelDetails(
         val_ds: torch.utils.data.TensorDataset
         train_dl: torch.utils.data.DataLoader
         val_dl: torch.utils.data.DataLoader
+        prog.message('Loading features for model training')
         train_arg1 = torch.from_numpy(record['ds'][train_indices].transpose((0, 3, 2, 1)))
         train_arg2 = torch.from_numpy(record['labelds'][train_indices])
         val_arg1 = torch.from_numpy(record['ds'][val_indices].transpose((0, 3, 2, 1)))
@@ -410,9 +411,15 @@ def cacheOptimalBatchSize(self, batchSize: int, model: torch.nn.Module, training
         return batchSize
 
     def loadModel(self, modelPath):
-        model = torch.load(modelPath)
-        model.eval()
-        return model
+        self.add_safe_globals()
+        try:
+            model = torch.load(modelPath, weights_only=False)
+            model.eval()
+            return model
+        except Exception as e:
+            print(f"Unable to load {modelPath}")
+            raise
+
 
     def saveModel(self, model, modelPath):
         torch.save(model, modelPath)

tools/inspect_image_feature_file.py

@@ -0,0 +1,37 @@
+'''
+This script will open a feature file (.h5) and show a 3x3 grid of images.
+This tool is useful if you suspect that features are not extracted properly, for example due to erroneous mask values/indexing.
+'''
+
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+import sys
+
+if len(sys.argv) > 0:
+    feature_file = sys.argv[1]
+else:
+    feature_file = "features.h5"
+
+# open the file
+with h5py.File(feature_file, "r") as f:
+    # get the images dataset
+    images = f["images"]
+    # get the first 9 images
+    images = images[:9]
+    # reshape the images to 3x3
+    #images = np.reshape(images, (3,3,100,100,3))
+    # transpose the images to 3x3
+    #images = np.transpose(images, (0,2,1,3,4))
+    # flatten the images to 9x100x100x3
+    #images = np.reshape(images, (9,100,100,3))
+
+    # hide axis from pyplot
+    plt.axis('off')
+
+    # plot the images
+    for i in range(9):
+        plt.subplot(3,3,i+1)
+        plt.imshow(images[i])
+    plt.show()
+    print(f"Image {i+1} is {images[i].shape}")