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Posted to dev@singa.apache.org by GitBox <gi...@apache.org> on 2020/06/15 10:25:17 UTC
[GitHub] [singa] joddiy opened a new pull request #736: Add expand operator
joddiy opened a new pull request #736:
URL: https://github.com/apache/singa/pull/736
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[GitHub] [singa] nudles merged pull request #736: Add expand operator
Posted by GitBox <gi...@apache.org>.
nudles merged pull request #736:
URL: https://github.com/apache/singa/pull/736
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[GitHub] [singa] joddiy commented on a change in pull request #736: Add expand operator
Posted by GitBox <gi...@apache.org>.
joddiy commented on a change in pull request #736:
URL: https://github.com/apache/singa/pull/736#discussion_r440637288
##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
return CosSim()(a, b)[0]
+class Expand(Operator):
+ """
+ Init a expand operator
Review comment:
I've added an example usage at the comment.
The `expand` duplicates the data along with the axis. It looks like `upsample` but still has some differences.
>
Example usage::
data = [[1.], [2.], [3.]]
# dim_changed
shape = [2, 1, 6]
output = [[[1., 1., 1., 1., 1., 1.],
[2., 2., 2., 2., 2., 2.],
[3., 3., 3., 3., 3., 3.]],
[[1., 1., 1., 1., 1., 1.],
[2., 2., 2., 2., 2., 2.],
[3., 3., 3., 3., 3., 3.]]]
# dim_unchanged
shape = [3, 4]
output = [[1., 1., 1., 1.],
[2., 2., 2., 2.],
[3., 3., 3., 3.]]
>
For upsample
>
Example usage::
data = [[[[1, 2],
[3, 4],]]]
# nearest
scales = [1.0, 1.0, 2.0, 3.0]
output = [[[[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[3, 3, 3, 4, 4, 4],
[3, 3, 3, 4, 4, 4],]]]
>
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[GitHub] [singa] nudles commented on a change in pull request #736: Add expand operator
Posted by GitBox <gi...@apache.org>.
nudles commented on a change in pull request #736:
URL: https://github.com/apache/singa/pull/736#discussion_r440535951
##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
return CosSim()(a, b)[0]
+class Expand(Operator):
+ """
+ Init a expand operator
Review comment:
You can explain what this operator does?
Does it reshape the tensor with more dimensions?
##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
return CosSim()(a, b)[0]
+class Expand(Operator):
+ """
+ Init a expand operator
+ """
+
+ def __init__(self, shape):
+ """
+ Args:
+ shape (list[int]: indicates the shape you want to expand to,
+ following the broadcast rule
+ """
+ super(Expand, self).__init__()
+ self.shape = shape
+
+ def forward(self, x):
+ """
+ forward of Expand
+ Args:
+ x (CTensor): input tensor.
+ Returns:
+ the output CTensor.
+ """
+ if isinstance(self.shape, np.ndarray):
+ self.shape = self.shape.tolist()
+ else:
+ self.shape = list(self.shape)
+ self.dim_changed = True
+ self.x_shape = list(x.shape())
+ x_shape = self.x_shape.copy()
+ for s_1, s_2 in zip(self.shape[::-1], x_shape[::-1]):
+ if s_1 != 1 and s_2 !=1:
+ if len(self.shape)!=len(x_shape):
+ assert False, ('not support dim_unchanged mode')
+ self.dim_changed = False
+ break
+ if self.dim_changed:
+ tmp_tensor = singa.Tensor(self.shape, x.device())
+ tmp_tensor.SetFloatValue(1.)
+ x = singa.__mul__(x, tmp_tensor)
+ else:
+ for axis, s_1, s_2 in zip(range(len(self.shape)), self.shape, x_shape):
+ if s_1 == s_2:
+ continue
+ xs = [x] * (s_1//s_2)
+ x = singa.VecTensor(xs)
+ x = singa.ConcatOn(x, axis)
+ return x
+
+ def backward(self, dy):
+ """
+ backward of Expand
+ Args:f
+ dy (CTensor), gradient tensor.
+ Return:
+ the gradient tensor over input tensor.
+ """
+ x_shape = self.x_shape
+ if self.dim_changed:
+ dy = tensor.from_raw_tensor(dy)
+ if len(self.shape) > len(x_shape):
+ x_shape = [1] * (len(self.shape) - len(x_shape))+ x_shape
+ for axis, s in zip(range(len(self.shape))[::-1], x_shape[::1]):
+ if s == 1:
+ dy = tensor.sum(dy, axis)
+ dy = dy.data
+ else:
+ for axis, s_1, s_2 in zip(range(len(self.shape))[::-1], self.shape[::-1], x_shape[::-1]):
+ if s_1 > s_2:
+ duplic = s_1//s_2
+ dxs = []
+ for i in range(s_2):
+ tmp_tensor = None
+ for j in range(duplic):
+ if not tmp_tensor:
+ tmp_tensor = singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+ else:
+ tmp_tensor += singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+ dxs.append(tmp_tensor)
+ dxs = singa.VecTensor(dxs)
+ dy = singa.ConcatOn(dxs, axis)
+ dy = singa.Reshape(dy, self.x_shape)
+ return dy
+
+
+def expand(x, shape):
+ """
+ Produces a cos similarity operator
Review comment:
expand and cos similarity are related??
##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
return CosSim()(a, b)[0]
+class Expand(Operator):
+ """
+ Init a expand operator
Review comment:
what's the difference to reshape?
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[GitHub] [singa] joddiy commented on a change in pull request #736: Add expand operator
Posted by GitBox <gi...@apache.org>.
joddiy commented on a change in pull request #736:
URL: https://github.com/apache/singa/pull/736#discussion_r440633751
##########
File path: python/singa/autograd.py
##########
@@ -4698,6 +4698,103 @@ def cossim(a, b):
return CosSim()(a, b)[0]
+class Expand(Operator):
+ """
+ Init a expand operator
+ """
+
+ def __init__(self, shape):
+ """
+ Args:
+ shape (list[int]: indicates the shape you want to expand to,
+ following the broadcast rule
+ """
+ super(Expand, self).__init__()
+ self.shape = shape
+
+ def forward(self, x):
+ """
+ forward of Expand
+ Args:
+ x (CTensor): input tensor.
+ Returns:
+ the output CTensor.
+ """
+ if isinstance(self.shape, np.ndarray):
+ self.shape = self.shape.tolist()
+ else:
+ self.shape = list(self.shape)
+ self.dim_changed = True
+ self.x_shape = list(x.shape())
+ x_shape = self.x_shape.copy()
+ for s_1, s_2 in zip(self.shape[::-1], x_shape[::-1]):
+ if s_1 != 1 and s_2 !=1:
+ if len(self.shape)!=len(x_shape):
+ assert False, ('not support dim_unchanged mode')
+ self.dim_changed = False
+ break
+ if self.dim_changed:
+ tmp_tensor = singa.Tensor(self.shape, x.device())
+ tmp_tensor.SetFloatValue(1.)
+ x = singa.__mul__(x, tmp_tensor)
+ else:
+ for axis, s_1, s_2 in zip(range(len(self.shape)), self.shape, x_shape):
+ if s_1 == s_2:
+ continue
+ xs = [x] * (s_1//s_2)
+ x = singa.VecTensor(xs)
+ x = singa.ConcatOn(x, axis)
+ return x
+
+ def backward(self, dy):
+ """
+ backward of Expand
+ Args:f
+ dy (CTensor), gradient tensor.
+ Return:
+ the gradient tensor over input tensor.
+ """
+ x_shape = self.x_shape
+ if self.dim_changed:
+ dy = tensor.from_raw_tensor(dy)
+ if len(self.shape) > len(x_shape):
+ x_shape = [1] * (len(self.shape) - len(x_shape))+ x_shape
+ for axis, s in zip(range(len(self.shape))[::-1], x_shape[::1]):
+ if s == 1:
+ dy = tensor.sum(dy, axis)
+ dy = dy.data
+ else:
+ for axis, s_1, s_2 in zip(range(len(self.shape))[::-1], self.shape[::-1], x_shape[::-1]):
+ if s_1 > s_2:
+ duplic = s_1//s_2
+ dxs = []
+ for i in range(s_2):
+ tmp_tensor = None
+ for j in range(duplic):
+ if not tmp_tensor:
+ tmp_tensor = singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+ else:
+ tmp_tensor += singa.SliceOn(dy, j*s_2+i, j*s_2+i+1, axis)
+ dxs.append(tmp_tensor)
+ dxs = singa.VecTensor(dxs)
+ dy = singa.ConcatOn(dxs, axis)
+ dy = singa.Reshape(dy, self.x_shape)
+ return dy
+
+
+def expand(x, shape):
+ """
+ Produces a cos similarity operator
Review comment:
just a typo, fixed
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