fixes #203

timeseriesAI · Sep 10, 2021 · 50f8767 · 50f8767
1 parent d24e572
commit 50f8767
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Showing 6 changed files with 45 additions and 79 deletions.
diff --git a/nbdev_jekyll.py b/nbdev_jekyll.py
diff --git a/nbs/060_callback.core.ipynb b/nbs/060_callback.core.ipynb
diff --git a/nbs/index.ipynb b/nbs/index.ipynb
@@ -344,8 +344,8 @@
       "\n",
       "Checking folder: /Users/nacho/Documents/Machine_Learning/Jupyter_Notebooks/tsai/tsai\n",
       "Correct conversion! 😃\n",
-      "Total time elapsed 245 s\n",
-      "Saturday 04/09/21 14:10:34 CEST\n"
+      "Total time elapsed 304 s\n",
+      "Friday 10/09/21 18:13:29 CEST\n"
      ]
     },
     {

diff --git a/settings.ini b/settings.ini
@@ -3,7 +3,7 @@ host = github
 lib_name = tsai
 user = timeseriesAI
 branch = main
-version = 0.2.22
+version = 0.2.23
 description = Practical Deep Learning for Time Series / Sequential Data library based on fastai & Pytorch
 keywords = fastai time-series time-series-classification time-series-regression deep-learning Pytorch
 author = Ignacio Oguiza and contributors

diff --git a/tsai/__init__.py b/tsai/__init__.py
@@ -1 +1 @@
-__version__ = "0.2.22"
+__version__ = "0.2.23"
diff --git a/tsai/callback/core.py b/tsai/callback/core.py
@@ -166,20 +166,15 @@ def before_fit(self):
         self.reduction = getattr(self.learn.loss_func, 'reduction', None)
         self.learn.loss_func = _PerInstanceLoss(crit=self.learn.loss_func)
         assert len(self.instance_weights) == len(self.learn.dls.train.dataset) + len(self.learn.dls.valid.dataset)
-        self.instance_weights = tensor(self.instance_weights).to(self.learn.dls.device)
+        self.instance_weights = torch.as_tensor(self.instance_weights, device=self.learn.dls.device)
 
     def before_batch(self):
-        if self.training:
-            original_idxs = tensor([self.learn.dls.train.split_idxs[self.learn.dls.train.idxs]], device=self.x.device)[0]
-            self.learn.loss_func.weights = self.instance_weights[original_idxs]
-        else:
-            original_idxs = tensor([self.learn.dls.valid.split_idxs[self.learn.dls.valid.idxs]], device=self.x.device)[0]
-            self.learn.loss_func.weights = self.instance_weights[original_idxs]
+        input_idxs = self.learn.dls.train.input_idxs if self.training else self.learn.dls.valid.input_idxs
+        self.learn.loss_func.weights = self.instance_weights[input_idxs]
 
     def after_fit(self):
         self.learn.loss_func = self.old_loss
-        if self.reduction is not None:
-            self.learn.loss_func.reduction = self.reduction
+        if self.reduction is not None: self.learn.loss_func.reduction = self.reduction
 
 
 class _PerInstanceLoss(Module):