Loading deepchem/models/torch_models/torch_model.py +2 −4 Original line number Diff line number Diff line Loading @@ -838,7 +838,7 @@ class TorchModel(Model): inputs = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in inputs ] inputs = [torch.as_tensor(x, device=self.device).float() for x in inputs] inputs = [torch.as_tensor(x, device=self.device) for x in inputs] if labels is not None: labels = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in labels Loading @@ -848,9 +848,7 @@ class TorchModel(Model): weights = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in weights ] weights = [ torch.as_tensor(x, device=self.device).float() for x in weights ] weights = [torch.as_tensor(x, device=self.device) for x in weights] return (inputs, labels, weights) Loading Loading
deepchem/models/torch_models/torch_model.py +2 −4 Original line number Diff line number Diff line Loading @@ -838,7 +838,7 @@ class TorchModel(Model): inputs = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in inputs ] inputs = [torch.as_tensor(x, device=self.device).float() for x in inputs] inputs = [torch.as_tensor(x, device=self.device) for x in inputs] if labels is not None: labels = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in labels Loading @@ -848,9 +848,7 @@ class TorchModel(Model): weights = [ x.astype(np.float32) if x.dtype == np.float64 else x for x in weights ] weights = [ torch.as_tensor(x, device=self.device).float() for x in weights ] weights = [torch.as_tensor(x, device=self.device) for x in weights] return (inputs, labels, weights) Loading
