Image segmentation update (#549)

Small updates to image segmentation

tests/conftest.py

@@ -50,7 +50,7 @@ from utils_cv.segmentation.data import Urls as seg_urls
 from utils_cv.segmentation.dataset import load_im, load_mask
 from utils_cv.segmentation.model import (
     confusion_matrix,
-    get_objective_fct,
+    get_ratio_correct_metric,
     predict,
 )
 from utils_cv.similarity.data import Urls as is_urls
@@ -970,7 +970,7 @@ def seg_learner(tiny_seg_databunch, seg_classes):
         tiny_seg_databunch,
         models.resnet18,
         wd=1e-2,
-        metrics=get_objective_fct(seg_classes),
+        metrics=get_ratio_correct_metric(seg_classes),
     )
 
 

tests/unit/segmentation/test_segmentation_model.py

@@ -4,15 +4,15 @@ import functools
 import numpy as np
 
 from utils_cv.segmentation.model import (
-    get_objective_fct,
+    get_ratio_correct_metric,
     predict,
     confusion_matrix,
     print_accuracies,
 )
 
 
-def test_get_objective_fct(seg_classes):
-    fct = get_objective_fct(seg_classes)
+def test_get_ratio_correct_metric(seg_classes):
+    fct = get_ratio_correct_metric(seg_classes)
     assert type(fct) == functools.partial
 
 

utils_cv/classification/parameter_sweeper.py

@@ -21,6 +21,7 @@ from fastai.vision import (
     imagenet_stats,
     Learner,
     models,
+    ResizeMethod,
     SegmentationItemList,
     unet_learner,
 )
@@ -30,7 +31,7 @@ import pandas as pd
 
 from utils_cv.common.gpu import db_num_workers
 from utils_cv.segmentation.dataset import read_classes
-from utils_cv.segmentation.model import get_objective_fct
+from utils_cv.segmentation.model import get_ratio_correct_metric
 
 
 Time = float
@@ -294,7 +295,7 @@ class ParameterSweeper:
             SegmentationItemList.from_folder(im_path)
             .split_by_rand_pct(valid_pct=0.33)
             .label_from_func(get_gt_filename, classes=classes)
-            .transform(tfms=tfms, size=im_size, tfm_y=True)
+            .transform(tfms=tfms, resize_method = ResizeMethod.CROP, size=im_size, tfm_y=True)
             .databunch(bs=bs, num_workers=db_num_workers())
             .normalize(imagenet_stats)
         )
@@ -412,7 +413,7 @@ class ParameterSweeper:
         elif learner_type == "unet":
             classes = read_classes(os.path.join(data_path, "classes.txt"))
             data = self._get_data_bunch_segmentationitemlist(data_path, transform, im_size, batch_size, classes)
-            metric = get_objective_fct(classes)
+            metric = get_ratio_correct_metric(classes)
             metric.__name__ = "ratio_correct"
             learn = unet_learner(
                 data,

utils_cv/segmentation/model.py

@@ -14,7 +14,7 @@ from .dataset import load_im
 
 
 # Ignore pixels marked as void. That could be pixels which are hard to annotate and hence should not influence training.
-def _objective_fct_partial(void_id, input, target):
+def ratio_correct(void_id, input, target):
     """ Helper function to compute the ratio of correctly classified pixels. """
     target = target.squeeze(1)
     if void_id:
@@ -28,8 +28,8 @@ def _objective_fct_partial(void_id, input, target):
     return ratio_correct
 
 
-def get_objective_fct(classes: List[str]):
-    """ Returns objective function for model training, defined as ratio of correctly classified pixels.
+def get_ratio_correct_metric(classes: List[str]):
+    """ Returns metric which computes the ratio of correctly classified pixels.
 
     Args:
         classes: list of class names
@@ -43,7 +43,7 @@ def get_objective_fct(classes: List[str]):
     else:
         void_id = None
 
-    return partial(_objective_fct_partial, void_id)
+    return partial(ratio_correct, void_id)
 
 
 def predict(