def index_copy_inplace_nograd(x, dim, index, tensor):
query_key = []
for k in range(dim):
query_key.append(None)
if isinstance(index, Tensor):
index = index.long()
query_key.append(index)
query_key = paddle.concat(query_key)
paddle.scatter_(x, query_key, tensor)
return x
def scatter_add_(self, dim,index, updates ):
assert dim==0, "scatter_add_, no support dim>0"
if "64" in str(updates.dtype):
updates=updates.astype("float32")
if "64" in str(self.dtype):
self=self.astype("float32")
if len(index.shape)==1:
paddle.scatter_(self, index , updates.astype("float32"), overwrite=False)
else:
for ii in range(index.shape[1]):
paddle.scatter_(self,index[:,ii],updates.astype("float32"),overwrite=False)
return self
def __setitem__(self, key, value):
if isinstance(key,tuple):
if len(key)==2:
return super(Tensor,self).__setitem__(key,value)
if isinstance(key,int):
return super(Tensor, self).__setitem__(key, value)
def convert_key_to_inttensor(key):
if isinstance(key, np.ndarray):
# print(max(args),min(args),self.shape,len(args),len(set(args)) )
key=paddorch.from_numpy(key).long()
# print("converted numpy", type(args))
if isinstance(key,paddle.Tensor):
if key.dtype==paddle.fluid.core.VarDesc.VarType.BOOL:
key = paddle.masked_select(paddle.arange(len(key)), key)
elif key.dtype==paddle.fluid.core.VarDesc.VarType.INT32 or key.dtype==paddle.fluid.core.VarDesc.VarType.INT64:
return key
else:
return key.astype("int32")
if isinstance(key,int):
return paddorch.LongTensor(np.array([key]))
if isinstance(key,list):
key = paddorch.from_numpy(key).long()
return key
if isinstance(key, np.ndarray) or isinstance(key,paddle.Tensor):
key = convert_key_to_inttensor(key)
elif isinstance(key,Iterable) :
if isinstance(key[0],slice):
return super(Tensor, self).__setitem__(key, value)
key2=[]
for i in range(len(key)):
key2.append(convert_key_to_inttensor(key[i]))
key=paddle.stack(key2,axis=1)
if len(key2)==1:
key= key.reshape([-1])
else:
key=convert_key_to_inttensor(key)
if key.shape[0]==0: ##empty selection, do nothing
return self
if not isinstance(value,paddle.Tensor):
value=paddle.ones_like(key)*float(value)
return paddle.scatter_(self,key,value)
def inplace_api_processing(self, var):
index = paddle.to_tensor([2, 1, 0, 1], dtype='int64')
updates = paddle.to_tensor(
[[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32')
return paddle.scatter_(var, index, updates, overwrite=False)