Complete as good as the torch for numpy users

conversions.yaml

@@ -1,4 +1,6 @@
 types:
+  - numpy: np.ndarray
+    pytorch: torch.Tensor
   - numpy: np.float32
     pytorch: torch.FloatTensor
   - numpy: np.float64
@@ -32,15 +34,140 @@ constructors:
       pytorch: torch.zeros
     - numpy: np.zeros_like
       pytorch: torch.zeros(x.size()).type(x.type())
-
-# - numpy: x.astype(np.int32)
-#   pytorch: x.type(torch.IntTensor)
-#
-# - numpy: y = x.copy()
-#   pytorch: y = x.clone()
-#
-# - numpy: x.shape
-#   pytorch: x.size()
-#
-# - numpy: x.size
-#   pytorch: x.nelement()
+  from existing data:
+    - numpy: np.array([[1, 2], [3, 4]])
+      pytorch: torch.Tensor([[1, 2], [3, 4])
+    - numpy: x.copy()
+      pytorch: x.clone()
+    - numpy: np.fromfile(file)
+      pytorch: torch.Tensor(torch.Storage(file))
+    - numpy: np.frombuffer
+      pytorch:
+    - numpy: np.fromfunction
+      pytorch:
+    - numpy: np.fromiter
+      pytorch:
+    - numpy: np.fromstring
+      pytorch:
+    - numpy: np.loadtxt
+      pytorch:
+    - numpy: np.concatenate
+      pytorch: torch.cat
+  numerical ranges:
+    - numpy: np.arange(10)
+      pytorch: torch.range(0, 9)
+    - numpy: np.arange(2, 3, 0.1)
+      pytorch: torch.range(2, 2.9, 10)
+    - numpy: np.linspace
+      pytorch: torch.linspace
+    - numpy: np.logspace
+      pytorch: np.logspace
+  building matrices:
+    - numpy: np.diag
+      pytorch: torch.diag
+    - numpy: np.tril
+      pytorch: torch.tril
+    - numpy: np.triu
+      pytorch: torch.triu
+  attributes:
+    - numpy: x.shape
+      pytorch: x.size()
+    - numpy: x.strides
+      pytorch: x.stride()
+    - numpy: x.ndim
+      pytorch: x.dim()
+    - numpy: x.data
+      pytorch: x.data()
+    - numpy: x.size
+      pytorch: x.nelement()
+    - numpy: x.dtype
+      pytorch: x.type()
+  indexing:
+    - numpy: x[0]
+      pytorch: x[0]
+    - numpy: x[:, 0]
+      pytorch: x[:, 0]
+    - numpy: x[indices]
+      pytorch: x[torch.LongTensor(indices)]
+    - numpy: np.take(x, indices)
+      pytorch: x[torch.LongTensor(indices)]
+    - numpy: x[x != 0]
+      pytorch: x[x != 0]
+  shape manipulation:
+    - numpy: x.reshape
+      pytorch: x.view
+    - numpy: x.resize
+      pytorch: x.resize_
+    - numpy:
+      pytorch: x.resize_as_
+    - numpy: x.transpose
+      pytorch: x.permute
+    - numpy: x.flatten()
+      pytorch: x.view(-1)
+    - numpy: x.squeeze
+      pytorch: x.squeeze
+    - numpy: x[:, np.newaxis]
+      pytorch: x.unsqueeze(1)
+  item selection and manipulation:
+    - numpy: np.put
+      pytorch:
+    - numpy: x.repeat
+      pytorch:
+    - numpy: x.tile
+      pytorch: x.repeat
+    - numpy: np.choose
+      pytorch:
+    - numpy: np.sort
+      pytorch: sorted, indices = torch.sort(x, [dim])
+    - numpy: np.argsort
+      pytorch: sorted, indices = torch.sort(x, [dim])
+    - numpy: np.nonzero
+      pytorch: torch.nonzero
+    - numpy: np.where
+      pytorch: torch.nonzero
+  calculation:
+    - numpy: x.min
+      pytorch: mins, indices = torch.min(x, [dim])
+    - numpy: x.argmin
+      pytorch: mins, indices = torch.min(x, [dim])
+    - numpy: x.max
+      pytorch: maxs, indices = torch.max(x, [dim])
+    - numpy: x.argmax
+      pytorch: maxs, indices = torch.max(x, [dim])
+    - numpy: x.clip
+      pytorch:
+    - numpy: x.round
+      pytorch: y.round
+    - numpy:
+      pytorch: y.floor
+    - numpy: x.trace
+      pytorch: y.trace
+    - numpy: x.sum
+      pytorch: y.sum
+    - numpy: x.cumsum
+      pytorch: y.cumsum
+    - numpy: x.mean
+      pytorch: x.mean
+    - numpy: x.std
+      pytorch: x.std
+    - numpy: x.prod
+      pytorch: x.prod
+    - numpy: x.cumprod
+      pytorch: x.cumprod
+    - numpy: x.all
+      pytorch: (y == 1).sum() == y.nelement()
+    - numpy: x.any
+      pytorch: (y == 1).sum() > 0
+  arithmetic and comparison operations:
+    - numpy: x.lt
+      pytorch: x.lt
+    - numpy: x.le
+      pytorch: x.le
+    - numpy: x.gt
+      pytorch: x.gt
+    - numpy: x.ge
+      pytorch: x.ge
+    - numpy: x.eq
+      pytorch: x.eq
+    - numpy: x.ne
+      pytorch: x.ne