QM9 doc + lint + sampled_avg_reward (#114)

* seh_frag_moo fix and lint

* Fix tox.ini

* trigger build

README.md

@@ -64,4 +64,5 @@ If package dependencies seem not to work, you may need to install the exact froz
 
 ## Developing & Contributing
 
-TODO: Write Contributing.md.
+External contributions are welcome. We use `tox` to run tests and linting, and `pre-commit` to run checks before committing.
+To ensure that these checks pass, simply run `tox -e style` and `tox run` to run linters and tests, respectively.

src/gflownet/data/sampling_iterator.py

@@ -254,6 +254,7 @@ def __iter__(self):
                     {k: v[num_offline:] for k, v in deepcopy(cond_info).items()},
                 )
             if num_online > 0:
+                extra_info["sampled_reward_avg"] = rewards[num_offline:].mean().item()
                 for hook in self.log_hooks:
                     extra_info.update(
                         hook(

src/gflownet/tasks/qm9/qm9.py

@@ -64,7 +64,15 @@ def inverse_flat_reward_transform(self, rp):
     def load_task_models(self, path):
         gap_model = mxmnet.MXMNet(mxmnet.Config(128, 6, 5.0))
         # TODO: this path should be part of the config?
-        state_dict = torch.load(path)
+        try:
+            state_dict = torch.load(path)
+        except Exception as e:
+            print(
+                "Could not load model.",
+                e,
+                "\nModel weights can be found at",
+                "https://storage.googleapis.com/emmanuel-data/models/mxmnet_gap_model.pt",
+            )
         gap_model.load_state_dict(state_dict)
         gap_model.cuda()
         gap_model, self.device = self._wrap_model(gap_model, send_to_device=True)