Fixing #324 and #325 (#338)

* update colormap to a non-discrete one -- fixes #324

* fix mask_to_disk to normalize by n_classes

* changes to test.py

* Updating data.py

* bug fix

* increased timeout time for main_build

* retrigger build

* retrigger the build

* increase timeout

Author: yalaudah

cv_lib/cv_lib/utils.py

@@ -1,4 +1,3 @@
-  
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
@@ -8,6 +7,7 @@
 import numpy as np
 from matplotlib import pyplot as plt
 
+
 def normalize(array):
     """
     Normalizes a segmentation mask array to be in [0,1] range
@@ -16,12 +16,19 @@ def normalize(array):
     min = array.min()
     return (array - min) / (array.max() - min)
 
-def mask_to_disk(mask, fname, cmap_name="Paired"):
+
+def mask_to_disk(mask, fname, n_classes, cmap_name="rainbow"):
     """
     write segmentation mask to disk using a particular colormap
+    mask (float): this contains the predicted labels in the range [0, n_classes].
+    fname (str): of the the image to be saved 
+    n_classes (int): total number of classes in the dataset
+    cmap_name (str): name of the matplotlib colormap to be used. The default "rainbow"
+        colormap works well for any number of classes. 
     """
     cmap = plt.get_cmap(cmap_name)
-    Image.fromarray(cmap(normalize(mask), bytes=True)).save(fname)
+    Image.fromarray(cmap(mask / n_classes, bytes=True)).save(fname)
+
 
 def image_to_disk(mask, fname, cmap_name="seismic"):
     """
@@ -30,7 +37,8 @@ def image_to_disk(mask, fname, cmap_name="seismic"):
     cmap = plt.get_cmap(cmap_name)
     Image.fromarray(cmap(normalize(mask), bytes=True)).save(fname)
 
-def decode_segmap(label_mask, colormap_name="Paired"):
+
+def decode_segmap(label_mask, colormap_name="rainbow"):
     """
     Decode segmentation class labels into a colour image
         Args:
@@ -48,6 +56,7 @@ def decode_segmap(label_mask, colormap_name="Paired"):
 
     return out
 
+
 def load_log_configuration(log_config_file):
     """
     Loads logging configuration from the given configuration file.

experiments/interpretation/dutchf3_patch/local/test.py

@@ -231,14 +231,16 @@ def _patch_label_2d(
             outdir = f"debug/batch_{split}"
             generate_path(outdir)
             for i in range(batch.shape[0]):
-                image_to_disk(
-                    np.array(batch[i, 0, :, :]), f"{outdir}/{batch_indexes[i][0]}_{batch_indexes[i][1]}_img.png"
-                )
-                # now dump model predictions
+                path_prefix = f"{outdir}/{batch_indexes[i][0]}_{batch_indexes[i][1]}"
+                model_output = model_output.detach().cpu()
+                # save image:
+                image_to_disk(np.array(batch[i, 0, :, :]), path_prefix + "_img.png")
+                # dump model prediction:
+                mask_to_disk(model_output[i, :, :, :].argmax(dim=1).numpy(), path_prefix + "_pred.png", num_classes)
+                # dump model confidence values
                 for nclass in range(num_classes):
-                    mask_to_disk(
-                        np.array(model_output[i, nclass, :, :].detach().cpu()),
-                        f"{outdir}/{batch_indexes[i][0]}_{batch_indexes[i][1]}_class_{nclass}_pred.png",
+                    image_to_disk(
+                        model_output[i, nclass, :, :].numpy(), path_prefix + f"_class_{nclass}_conf.png",
                     )
 
     # crop the output_p in the middle
@@ -279,7 +281,7 @@ def _evaluate_split(
 
     running_metrics_split = runningScore(n_classes)
 
-    # testing mode:
+    # evaluation mode:
     with torch.no_grad():  # operations inside don't track history
         model.eval()
         total_iteration = 0
@@ -307,8 +309,8 @@ def _evaluate_split(
             running_metrics_overall.update(gt, pred)
 
             #  dump images to disk for review
-            mask_to_disk(pred.squeeze(), os.path.join(output_dir, f"{i}_pred.png"))
-            mask_to_disk(gt.squeeze(), os.path.join(output_dir, f"{i}_gt.png"))
+            mask_to_disk(pred.squeeze(), os.path.join(output_dir, f"{i}_pred.png"), n_classes)
+            mask_to_disk(gt.squeeze(), os.path.join(output_dir, f"{i}_gt.png"), n_classes)
 
     # get scores
     score, class_iou = running_metrics_split.get_scores()

experiments/interpretation/dutchf3_patch/local/train.py

@@ -83,7 +83,7 @@ def run(*options, cfg=None, debug=False):
     # Set CUDNN benchmark mode:
     torch.backends.cudnn.benchmark = config.CUDNN.BENCHMARK
 
-    # we will write the model under outputs / config_file_name / model_dir
+    # We will write the model under outputs / config_file_name / model_dir
     config_file_name = "default_config" if not cfg else cfg.split("/")[-1].split(".")[0]
 
     # Fix random seeds:

interpretation/deepseismic_interpretation/dutchf3/data.py

@@ -154,8 +154,9 @@ def __getitem__(self, index):
         if self.debug and "test" in self.split:
             outdir = f"debug/sectionLoader_{self.split}_raw"
             generate_path(outdir)
-            image_to_disk(im, f"{outdir}/index_{index}_section_{section_name}_img.png")
-            mask_to_disk(lbl, f"{outdir}/index_{index}_section_{section_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{section_name}"
+            image_to_disk(im, path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.augmentations is not None:
             augmented_dict = self.augmentations(image=im, mask=lbl)
@@ -167,8 +168,9 @@ def __getitem__(self, index):
         if self.debug and "test" in self.split:
             outdir = f"debug/sectionLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             generate_path(outdir)
-            image_to_disk(np.array(im[0]), f"{outdir}/index_{index}_section_{section_name}_img.png")
-            mask_to_disk(np.array(lbl[0]), f"{outdir}/index_{index}_section_{section_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{section_name}"
+            image_to_disk(np.array(im[0]), path_prefix + "_img.png")
+            mask_to_disk(np.array(lbl[0]), path_prefix + "_lbl.png", self.n_classes)
 
         return im, lbl
 
@@ -408,8 +410,9 @@ def __getitem__(self, index):
             outdir = f"debug/testSectionLoaderWithDepth_{self.split}_raw"
             generate_path(outdir)
             # this needs to take the first dimension of image (no depth) but lbl only has 1 dim
-            image_to_disk(im[0, :, :], f"{outdir}/index_{index}_section_{section_name}_img.png")
-            mask_to_disk(lbl, f"{outdir}/index_{index}_section_{section_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{section_name}"
+            image_to_disk(im[0, :, :], path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.augmentations is not None:
             im = _transform_CHW_to_HWC(im)
@@ -426,8 +429,9 @@ def __getitem__(self, index):
                 f"debug/testSectionLoaderWithDepth_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             )
             generate_path(outdir)
-            image_to_disk(np.array(im[0, :, :]), f"{outdir}/index_{index}_section_{section_name}_img.png")
-            mask_to_disk(np.array(lbl[0, :, :]), f"{outdir}/index_{index}_section_{section_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{section_name}"
+            image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
+            mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)
 
         return im, lbl
 
@@ -495,8 +499,9 @@ def __getitem__(self, index):
         if self.debug:
             outdir = f"debug/patchLoader_{self.split}_raw"
             generate_path(outdir)
-            image_to_disk(im, f"{outdir}/index_{index}_section_{patch_name}_img.png")
-            mask_to_disk(lbl, f"{outdir}/index_{index}_section_{patch_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
+            image_to_disk(im, path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.augmentations is not None:
             augmented_dict = self.augmentations(image=im, mask=lbl)
@@ -506,8 +511,9 @@ def __getitem__(self, index):
         if self.debug:
             outdir = f"patchLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             generate_path(outdir)
-            image_to_disk(im, f"{outdir}/{index}_img.png")
-            mask_to_disk(lbl, f"{outdir}/{index}_lbl.png")
+            path_prefix = f"{outdir}/{index}"
+            image_to_disk(im, path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.is_transform:
             im, lbl = self.transform(im, lbl)
@@ -516,8 +522,9 @@ def __getitem__(self, index):
         if self.debug:
             outdir = f"debug/patchLoader_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             generate_path(outdir)
-            image_to_disk(np.array(im[0, :, :]), f"{outdir}/index_{index}_section_{patch_name}_img.png")
-            mask_to_disk(np.array(lbl[0, :, :]), f"{outdir}/index_{index}_section_{patch_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
+            image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
+            mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)
 
         return im, lbl
 
@@ -761,8 +768,9 @@ def __getitem__(self, index):
         if self.debug:
             outdir = f"debug/patchLoaderWithSectionDepth_{self.split}_raw"
             generate_path(outdir)
-            image_to_disk(im[0, :, :], f"{outdir}/index_{index}_section_{patch_name}_img.png")
-            mask_to_disk(lbl, f"{outdir}/index_{index}_section_{patch_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
+            image_to_disk(im[0, :, :], path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.augmentations is not None:
             im = _transform_CHW_to_HWC(im)
@@ -774,8 +782,9 @@ def __getitem__(self, index):
         if self.debug:
             outdir = f"patchLoaderWithSectionDepth_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             generate_path(outdir)
-            image_to_disk(im[0,:,:], f"{outdir}/{index}_img.png")
-            mask_to_disk(lbl, f"{outdir}/{index}_lbl.png")
+            path_prefix = f"{outdir}/{index}"
+            image_to_disk(im[0, :, :], path_prefix + "_img.png")
+            mask_to_disk(lbl, path_prefix + "_lbl.png", self.n_classes)
 
         if self.is_transform:
             im, lbl = self.transform(im, lbl)
@@ -786,8 +795,9 @@ def __getitem__(self, index):
                 f"debug/patchLoaderWithSectionDepth_{self.split}_{'aug' if self.augmentations is not None else 'noaug'}"
             )
             generate_path(outdir)
-            image_to_disk(np.array(im[0, :, :]), f"{outdir}/index_{index}_section_{patch_name}_img.png")
-            mask_to_disk(np.array(lbl[0, :, :]), f"{outdir}/index_{index}_section_{patch_name}_lbl.png")
+            path_prefix = f"{outdir}/index_{index}_section_{patch_name}"
+            image_to_disk(np.array(im[0, :, :]), path_prefix + "_img.png")
+            mask_to_disk(np.array(lbl[0, :, :]), path_prefix + "_lbl.png", self.n_classes)
 
         return im, lbl
 

tests/cicd/main_build.yml

@@ -272,7 +272,7 @@ jobs:
 
 - job: dutchf3_patch
   dependsOn: checkerboard_dutchf3_patch
-  timeoutInMinutes: 25
+  timeoutInMinutes: 60
   displayName: Dutch F3 patch local
   pool:
     name: deepseismicagentpool