view metrics in example

weecology · Dec 12, 2024 · 17adc54 · 17adc54
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Showing 4 changed files with 81 additions and 31 deletions.
diff --git a/docs/examples/baseline_boxes.ipynb b/docs/examples/baseline_boxes.ipynb
diff --git a/milliontrees/common/metrics/all_metrics.py b/milliontrees/common/metrics/all_metrics.py
@@ -390,7 +390,7 @@ def _compute_element_wise(self, y_pred, y_true):
 
             gt_boxes = gt[self.geometry_name]
             pred_boxes = target_boxes[target_scores > self.score_threshold]
-            det_accuracy = torch.mean(torch.stack([ self._accuracy(gt_boxes,pred_boxes,iou_thr) for iou_thr in np.arange(0.5,0.51,0.05)]))
+            det_accuracy = torch.mean(torch.stack([ self._accuracy(gt_boxes,pred_boxes,iou_thr) for iou_thr in np.arange(0.4,0.41,0.05)]))
             batch_results.append(det_accuracy)
 
         return torch.tensor(batch_results)

diff --git a/milliontrees/datasets/TreePoints.py b/milliontrees/datasets/TreePoints.py
@@ -90,7 +90,7 @@ def __init__(self,
         self._input_lookup = df.groupby('filename').apply(lambda x: x.index.values).to_dict()
 
         # Point labels
-        self._y_array = df[["x", "y"]].values.astype(float)
+        self._y_array = df[["x", "y"]].values.astype(int)
 
         # Labels -> just 'Tree'
         self._n_classes = 1

diff --git a/weights/DeepForest.py b/weights/DeepForest.py
@@ -59,7 +59,7 @@ def main():
 
     ## Evaluate the model
     box_dataset = get_dataset("TreeBoxes", root_dir="/orange/ewhite/DeepForest/MillionTrees/")
-    box_test_data = box_dataset.get_subset("test")
+    box_test_data = box_dataset.get_subset("test",frac=0.1)
 
     test_loader = get_eval_loader("standard", box_test_data, batch_size=16)
 
@@ -75,7 +75,6 @@ def main():
         metadata, images, targets  = batch
         for image_metadata, image, image_targets in zip(metadata,images, targets):
             basename = box_dataset._filename_id_to_code[int(image_metadata[0])]
-            #image_path = os.path.join(box_dataset._data_dir._str, "images",basename)
             # Deepforest likes 0-255 data, channels first
             channels_first = image.permute(1, 2, 0).numpy() * 255
             pred = m.predict_image(channels_first)