seem need to downgrade PyTorch - see microsoft/nlp-recipes#616

WangYixuan12 · Oct 5, 2021 · 1c31f92 · 1c31f92
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diff --git a/README.md b/README.md
@@ -70,9 +70,16 @@ To prevent the repo from growing in size, recommend always "restart and clear ou
 
 Installation using the following command
 ```
+git submodule update --init --recursive
 pip3 install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html
 ```
 Using the environment through the following command
 ```
+conda activate $CONDA_ENV
 source docker/entrypoint.sh
+use_pytorch_dense_correspondence
+```
+Start the notebook
+```
+./start_notebook.py
 ```
diff --git a/dense_correspondence/dataset/dense_correspondence_dataset_masked.py b/dense_correspondence/dataset/dense_correspondence_dataset_masked.py
@@ -551,7 +551,7 @@ def set_parameters_from_training_config(self, training_config):
 
         self._use_image_b_mask_inv = training_config["training"]["use_image_b_mask_inv"] 
 
-        from spartan_dataset_masked import SpartanDatasetDataType
+        from dense_correspondence.dataset.spartan_dataset_masked import SpartanDatasetDataType
 
         self._data_load_types = []
         self._data_load_type_probabilities = []
@@ -586,7 +586,8 @@ def set_parameters_from_training_config(self, training_config):
             self._data_load_types.append(SpartanDatasetDataType.SYNTHETIC_MULTI_OBJECT)
             self._data_load_type_probabilities.append(p)
 
-        self._data_load_type_probabilities = np.array(self._data_load_type_probabilities)
+        self._data_load_type_probabilities = np.array(self._data_load_type_probabilities).astype(float)
+        # print(self._data_load_type_probabilities.dtype)
         self._data_load_type_probabilities /= np.sum(self._data_load_type_probabilities)
 
     def set_train_mode(self):

diff --git a/dense_correspondence/dataset/simple_datasets_test.ipynb b/dense_correspondence/dataset/simple_datasets_test.ipynb
diff --git a/dense_correspondence/loss_functions/pixelwise_contrastive_loss.py b/dense_correspondence/loss_functions/pixelwise_contrastive_loss.py
@@ -296,7 +296,7 @@ def non_match_loss_descriptor_only(self, image_a_pred, image_b_pred, non_matches
         non_match_loss_vec, num_hard_negatives, _, _ = PCL.non_match_descriptor_loss(image_a_pred, image_b_pred, non_matches_a,
                                                                                      non_matches_b, M=M_descriptor, invert=invert)
 
-        num_non_matches = long(non_match_loss_vec.size()[0])
+        num_non_matches = int(non_match_loss_vec.size()[0])
 
         non_match_loss = non_match_loss_vec.sum()