added Pytorch 2.0 compatibility

Author: oguiza

nbs/006_data.core.ipynb

@@ -15,10 +15,10 @@ status = 4
 min_python = 3.7
 audience = Developers
 language = English
-requirements = fastai>=2.7.9 pyts>=0.12.0 imbalanced-learn>=0.8.0 psutil>=5.4.8
-pip_requirements = torch>=1.7,<1.14
+requirements = fastai>=2.7.12 pyts>=0.12.0 imbalanced-learn>=0.8.0 psutil>=5.4.8
+pip_requirements = torch>=1.7,<2.1
 conda_user = timeseriesAI
-conda_requirements = pytorch>=1.7,<1.14
+conda_requirements = pytorch>=1.7,<2.1
 extra_requirements = sktime>=0.10.1 tsfresh>=0.18.0 PyWavelets>=1.1.1 nbformat>=5.1.3
 dev_requirements = nbdev>2 ipykernel>6
 console_scripts = nb2py=tsai.export:nb2py

nbs/009_data.preprocessing.ipynb

@@ -44,11 +44,11 @@ def data(self): return cast(self, Tensor)
 
     def __repr__(self):
         if self.ndim > 0: return f'NumpyTensor(shape:{tuple(self.shape)}, device={self.device}, dtype={self.dtype})'
-        else: return f'NumpyTensor([{self}], device={self.device}, dtype={self.dtype})'
+        else: return f'NumpyTensor([{self.data}], device={self.device}, dtype={self.dtype})'
 
 
     def show(self, ax=None, ctx=None, title=None, **kwargs):
-        if self.ndim == 0: return str(self)
+        if self.ndim == 0: return str(self.data)
         elif self.ndim != 2: self = type(self)(to2d(self))
         if not isinstance(self,np.ndarray): self = self.detach().cpu().numpy()
         ax = ifnone(ax, ctx)
@@ -79,13 +79,9 @@ def __new__(cls, o, dtype=None, device=None, copy=None, requires_grad=False, **k
 
     @property
     def data(self): return cast(self, Tensor)
-    
-    def __repr__(self):
-        if self.ndim > 0: return f'NumpyTensor(shape:{tuple(self.shape)}, device={self.device}, dtype={self.dtype})'
-        else: return f'NumpyTensor([{self}], device={self.device}, dtype={self.dtype})'
 
     def show(self, ax=None, ctx=None, title=None, **kwargs):
-        if self.ndim == 0: return str(self)
+        if self.ndim == 0: return str(self.data)
         elif self.ndim != 2: self = type(self)(to2d(self))
         if not isinstance(self,np.ndarray): self = self.detach().cpu().numpy()
         ax = ifnone(ax, ctx)
@@ -115,7 +111,7 @@ def __repr__(self):
             return f'TSTensor(vars:{self.shape[-2]}, len:{self.shape[-1]}, device={self.device}, dtype={self.dtype})'
         elif self.ndim == 1:
             return f'TSTensor(len:{self.shape[-1]}, device={self.device}, dtype={self.dtype})'
-        else: return f'TSTensor([{self}], device={self.device}, dtype={self.dtype})'
+        else: return f'TSTensor([{self.data}], device={self.device}, dtype={self.dtype})'
 
 # %% ../../nbs/006_data.core.ipynb 8
 class ToTSTensor(Transform):
@@ -134,12 +130,12 @@ def show_tuple(tup, **kwargs):
 # %% ../../nbs/006_data.core.ipynb 27
 class TSLabelTensor(NumpyTensor): 
     def __repr__(self):
-        if self.ndim == 0: return f'{self}'
+        if self.ndim == 0: return f'{self.data}'
         else: return f'TSLabelTensor(shape:{tuple(self.shape)}, device={self.device}, dtype={self.dtype})'
 
 class TSMaskTensor(NumpyTensor): 
     def __repr__(self):
-        if self.ndim == 0: return f'{self}'
+        if self.ndim == 0: return f'{self.data}'
         else: return f'TSMaskTensor(shape:{tuple(self.shape)}, device={self.device}, dtype={self.dtype})'
 
 # %% ../../nbs/006_data.core.ipynb 30

nbs/012_data.image.ipynb

@@ -31,7 +31,7 @@ def vars(self):
     def len(self): return self.shape[-2:]
 
     def __repr__(self):
-        if self.ndim == 0: return f'{self}'
+        if self.ndim == 0: return f'{self.data}'
         else: return f'TSImage(shape:{self.shape})'
 
     def show(self, **kwargs):