Adding pretrained weights S3DIS

doc/pretrained_models_guide.md

@@ -1,5 +1,48 @@
 
 
-## Test a pretrained network
+## S3DIS Pretrained Models
+
+### Models
+
+We provide pretrained weights for S3DIS dataset. The raw weights come with a parameter file describing the architecture and network hyperparameters. THe code can thus load the network automatically.
+
+The instructions to run these models are in the S3DIS documentation, section [Test the trained model](./doc/scene_segmentation_guide.md#test-the-trained-model).
+
+
+| Name (link) | KPConv Type | Description | Score |
+|:-------------|:-------------:|:-----|:-----:|
+| [Light_KPFCNN](https://drive.google.com/file/d/14sz0hdObzsf_exxInXdOIbnUTe0foOOz/view?usp=sharing) | rigid | A network with small `in_radius` for light GPU consumption (~8GB) | 65.4% |
+| [Heavy_KPFCNN](https://drive.google.com/file/d/1ySQq3SRBgk2Vt5Bvj-0N7jDPi0QTPZiZ/view?usp=sharing) | rigid | A network with better performances but needing bigger GPU (>18GB). | 66.4% |
+
+
+
+### Instructions
+
+1. Unzip and place the folder in your 'results' folder.
+
+2. In the test script `test_any_model.py`, set the variable `chosen_log` to the path were you placed the folder.
+
+3. Run the test script
+
+        python3 test_any_model.py
+
+4. You will see the performance (on the subsampled input clouds) increase as the test goes on. 
+
+        Confusion on sub clouds
+        65.08 | 92.11 98.40 81.83  0.00 18.71 55.41 68.65 90.93 79.79 74.83 65.31 63.41 56.62
+
+
+5. After a few minutes, the script will reproject the results form the subsampled input clouds to the real data and get you the real score
+
+        Reproject Vote #9
+        Done in 2.6 s
+
+        Confusion on full clouds
+        Done in 2.1 s
+
+        --------------------------------------------------------------------------------------
+        65.38 | 92.62 98.39 81.77  0.00 18.87 57.80 67.93 91.52 80.27 74.24 66.14 64.01 56.42
+        --------------------------------------------------------------------------------------
+
+6. The test script creates a folder `test/name-of-your-log`, where it saves the predictions, potentials, and probabilities per class. You can load them with CloudCompare for visualization.
 
-TODO

plot_convergence.py

@@ -221,7 +221,7 @@ def compare_trainings(list_of_paths, list_of_labels=None):
 
         print(path)
 
-        if ('val_IoUs.txt' in [f.decode('ascii') for f in listdir(path)]) or ('val_confs.txt' in [f.decode('ascii') for f in listdir(path)]):
+        if ('val_IoUs.txt' in [f for f in listdir(path)]) or ('val_confs.txt' in [f for f in listdir(path)]):
             config = Config()
             config.load(path)
         else:

test_models.py

@@ -95,10 +95,10 @@ if __name__ == '__main__':
     #       > 'last_XXX': Automatically retrieve the last trained model on dataset XXX
     #       > '(old_)results/Log_YYYY-MM-DD_HH-MM-SS': Directly provide the path of a trained model
 
-    chosen_log = 'results/Log_2020-04-05_19-19-20'  # => ModelNet40
+    chosen_log = 'results/Light_KPFCNN'
 
     # Choose the index of the checkpoint to load OR None if you want to load the current checkpoint
-    chkp_idx = None
+    chkp_idx = -1
 
     # Choose to test on validation or test split
     on_val = True

utils/tester.py

@@ -360,10 +360,10 @@ class ModelTester:
                     proj_probs = []
                     for i, file_path in enumerate(test_loader.dataset.files):
 
-                        print(i, file_path, test_loader.dataset.test_proj[i].shape, self.test_probs[i].shape)
+                        # print(i, file_path, test_loader.dataset.test_proj[i].shape, self.test_probs[i].shape)
 
-                        print(test_loader.dataset.test_proj[i].dtype, np.max(test_loader.dataset.test_proj[i]))
+                        # print(test_loader.dataset.test_proj[i].dtype, np.max(test_loader.dataset.test_proj[i]))
-                        print(test_loader.dataset.test_proj[i][:5])
+                        # print(test_loader.dataset.test_proj[i][:5])
 
                         # Reproject probs on the evaluations points
                         probs = self.test_probs[i][test_loader.dataset.test_proj[i], :]