Merge pull request #34 from Vance-Raiti/main

Remove depreciated torchmetrics __init__ argument and fix str_to_one_hot for CUDA devices

enformer_pytorch/metrics.py

@@ -5,11 +5,10 @@
 
 class MeanPearsonCorrCoefPerChannel(Metric):
     is_differentiable: Optional[bool] = False
-    full_state_update:bool = False
     higher_is_better: Optional[bool] = True
     def __init__(self, n_channels:int, dist_sync_on_step=False):
         """Calculates the mean pearson correlation across channels aggregated over regions"""
-        super().__init__(dist_sync_on_step=dist_sync_on_step, full_state_update=False)
+        super().__init__(dist_sync_on_step=dist_sync_on_step)
         self.reduce_dims=(0, 1)
         self.add_state("product", default=torch.zeros(n_channels, dtype=torch.float32), dist_reduce_fx="sum", )
         self.add_state("true", default=torch.zeros(n_channels, dtype=torch.float32), dist_reduce_fx="sum", )
@@ -41,4 +40,4 @@ def compute(self):
         pred_var = self.pred_squared - self.count * torch.square(pred_mean)
         tp_var = torch.sqrt(true_var) * torch.sqrt(pred_var)
         correlation = covariance / tp_var
-        return correlation
+        return correlation

enformer_pytorch/modeling_enformer.py

@@ -446,8 +446,9 @@ def forward(
         if isinstance(x, list):
             x = str_to_one_hot(x)
 
-        elif x.dtype == torch.long:
+        elif type(x) == torch.Tensor and x.dtype == torch.long:
             x = seq_indices_to_one_hot(x)
+        x.to(self.device)
 
         no_batch = x.ndim == 2