def preProc2(x):
# Access the global variables
global P, expP, negExpP
P = P.type_as(x)
expP = expP.type_as(x)
negExpP = negExpP.type_as(x)
# Create a variable filled with -1. Second part of the condition
z = Variable(torch.zeros(x.size())).type_as(x)
absX = torch.abs(x)
cond1 = torch.gt(absX, negExpP)
cond2 = torch.le(absX, negExpP)
if (torch.sum(cond1) > 0).data.all():
x1 = torch.sign(x[cond1])
z[cond1] = x1
if (torch.sum(cond2) > 0).data.all():
x2 = x[cond2]*expP
z[cond2] = x2
return z
python类le()的实例源码
def allclose(x: T.FloatTensor,
y: T.FloatTensor,
rtol: float = 1e-05,
atol: float = 1e-08) -> bool:
"""
Test if all elements in the two tensors are approximately equal.
absolute(x - y) <= (atol + rtol * absolute(y))
Args:
x: A tensor.
y: A tensor.
rtol (optional): Relative tolerance.
atol (optional): Absolute tolerance.
returns:
bool: Check if all of the elements in the tensors are approximately equal.
"""
return tall(torch.abs(x - y).le((atol + rtol * torch.abs(y))))
def backward(self, grad_output):
v1, v2, y = self.saved_tensors
buffer = v1.new()
_idx = self._new_idx(v1)
gw1 = grad_output.new()
gw2 = grad_output.new()
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(buffer, self.w1, self.w22)
gw1.addcmul_(-1, buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(buffer, self.w1, self.w32)
gw2.addcmul_(-1, buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
torch.le(_idx, self._outputs, 0)
_idx = _idx.view(-1, 1).expand(gw1.size())
gw1[_idx] = 0
gw2[_idx] = 0
torch.eq(_idx, y, 1)
_idx = _idx.view(-1, 1).expand(gw2.size())
gw1[_idx] = gw1[_idx].mul_(-1)
gw2[_idx] = gw2[_idx].mul_(-1)
if self.size_average:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
if grad_output[0] != 1:
gw1.mul_(grad_output)
gw2.mul_(grad_output)
return gw1, gw2, None
def updateGradInput(self, input, y):
v1 = input[0]
v2 = input[1]
gw1 = self.gradInput[0]
gw2 = self.gradInput[1]
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.buffer, self.w1, self.w22)
gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.buffer, self.w1, self.w32)
gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
# self._idx = self._outputs <= 0
torch.le(self._idx, self._outputs, 0)
self._idx = self._idx.view(-1, 1).expand(gw1.size())
gw1[self._idx] = 0
gw2[self._idx] = 0
torch.eq(self._idx, y, 1)
self._idx = self._idx.view(-1, 1).expand(gw2.size())
gw1[self._idx] = gw1[self._idx].mul_(-1)
gw2[self._idx] = gw2[self._idx].mul_(-1)
if self.sizeAverage:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
return self.gradInput
def backward(self, grad_output):
v1, v2, y = self.saved_tensors
buffer = v1.new()
_idx = v1.new().byte()
gw1 = grad_output.new()
gw2 = grad_output.new()
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=buffer)
gw1.addcmul_(-1, buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=buffer)
gw2.addcmul_(-1, buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
torch.le(self._outputs, 0, out=_idx)
_idx = _idx.view(-1, 1).expand(gw1.size())
gw1[_idx] = 0
gw2[_idx] = 0
torch.eq(y, 1, out=_idx)
_idx = _idx.view(-1, 1).expand(gw2.size())
gw1[_idx] = gw1[_idx].mul_(-1)
gw2[_idx] = gw2[_idx].mul_(-1)
if self.size_average:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
grad_output_val = grad_output[0]
if grad_output_val != 1:
gw1.mul_(grad_output_val)
gw2.mul_(grad_output_val)
return gw1, gw2, None
def updateGradInput(self, input, y):
v1 = input[0]
v2 = input[1]
gw1 = self.gradInput[0]
gw2 = self.gradInput[1]
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=self.buffer)
gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=self.buffer)
gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
# self._idx = self._outputs <= 0
torch.le(self._outputs, 0, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw1.size())
gw1[self._idx] = 0
gw2[self._idx] = 0
torch.eq(y, 1, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw2.size())
gw1[self._idx] = gw1[self._idx].mul_(-1)
gw2[self._idx] = gw2[self._idx].mul_(-1)
if self.sizeAverage:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
return self.gradInput
def backward(self, grad_output):
v1, v2, y = self.saved_tensors
buffer = v1.new()
_idx = v1.new().byte()
gw1 = grad_output.new()
gw2 = grad_output.new()
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=buffer)
gw1.addcmul_(-1, buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=buffer)
gw2.addcmul_(-1, buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
torch.le(self._outputs, 0, out=_idx)
_idx = _idx.view(-1, 1).expand(gw1.size())
gw1[_idx] = 0
gw2[_idx] = 0
torch.eq(y, 1, out=_idx)
_idx = _idx.view(-1, 1).expand(gw2.size())
gw1[_idx] = gw1[_idx].mul_(-1)
gw2[_idx] = gw2[_idx].mul_(-1)
if self.size_average:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
grad_output_val = grad_output[0]
if grad_output_val != 1:
gw1.mul_(grad_output_val)
gw2.mul_(grad_output_val)
return gw1, gw2, None
CosineEmbeddingCriterion.py 文件源码
项目：pytorch-coriander
作者: hughperkins
项目源码
文件源码
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def updateGradInput(self, input, y):
v1 = input[0]
v2 = input[1]
gw1 = self.gradInput[0]
gw2 = self.gradInput[1]
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=self.buffer)
gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=self.buffer)
gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
# self._idx = self._outputs <= 0
torch.le(self._outputs, 0, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw1.size())
gw1[self._idx] = 0
gw2[self._idx] = 0
torch.eq(y, 1, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw2.size())
gw1[self._idx] = gw1[self._idx].mul_(-1)
gw2[self._idx] = gw2[self._idx].mul_(-1)
if self.sizeAverage:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
return self.gradInput
def backward(ctx, grad_output):
v1, v2, y = ctx.saved_tensors
buffer = v1.new()
_idx = v1.new().byte()
gw1 = grad_output.new()
gw2 = grad_output.new()
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(ctx.w1, ctx.w22, out=buffer)
gw1.addcmul_(-1, buffer.expand_as(v1), v1)
gw1.mul_(ctx.w.expand_as(v1))
torch.mul(ctx.w1, ctx.w32, out=buffer)
gw2.addcmul_(-1, buffer.expand_as(v1), v2)
gw2.mul_(ctx.w.expand_as(v1))
torch.le(ctx._outputs, 0, out=_idx)
_idx = _idx.view(-1, 1).expand(gw1.size())
gw1[_idx] = 0
gw2[_idx] = 0
torch.eq(y, 1, out=_idx)
_idx = _idx.view(-1, 1).expand(gw2.size())
gw1[_idx] = gw1[_idx].mul_(-1)
gw2[_idx] = gw2[_idx].mul_(-1)
if ctx.size_average:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
grad_output_val = grad_output[0]
if grad_output_val != 1:
gw1.mul_(grad_output_val)
gw2.mul_(grad_output_val)
return gw1, gw2, None, None, None
def updateGradInput(self, input, y):
v1 = input[0]
v2 = input[1]
gw1 = self.gradInput[0]
gw2 = self.gradInput[1]
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=self.buffer)
gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=self.buffer)
gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
# self._idx = self._outputs <= 0
torch.le(self._outputs, 0, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw1.size())
gw1[self._idx] = 0
gw2[self._idx] = 0
torch.eq(y, 1, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw2.size())
gw1[self._idx] = gw1[self._idx].mul_(-1)
gw2[self._idx] = gw2[self._idx].mul_(-1)
if self.sizeAverage:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
return self.gradInput
def backward(ctx, grad_output):
v1, v2, y = ctx.saved_tensors
buffer = v1.new()
_idx = v1.new().byte()
gw1 = grad_output.new()
gw2 = grad_output.new()
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(ctx.w1, ctx.w22, out=buffer)
gw1.addcmul_(-1, buffer.expand_as(v1), v1)
gw1.mul_(ctx.w.expand_as(v1))
torch.mul(ctx.w1, ctx.w32, out=buffer)
gw2.addcmul_(-1, buffer.expand_as(v1), v2)
gw2.mul_(ctx.w.expand_as(v1))
torch.le(ctx._outputs, 0, out=_idx)
_idx = _idx.view(-1, 1).expand(gw1.size())
gw1[_idx] = 0
gw2[_idx] = 0
torch.eq(y, 1, out=_idx)
_idx = _idx.view(-1, 1).expand(gw2.size())
gw1[_idx] = gw1[_idx].mul_(-1)
gw2[_idx] = gw2[_idx].mul_(-1)
if ctx.size_average:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
grad_output_val = grad_output[0]
if grad_output_val != 1:
gw1.mul_(grad_output_val)
gw2.mul_(grad_output_val)
return gw1, gw2, None, None, None
def updateGradInput(self, input, y):
v1 = input[0]
v2 = input[1]
gw1 = self.gradInput[0]
gw2 = self.gradInput[1]
gw1.resize_as_(v1).copy_(v2)
gw2.resize_as_(v1).copy_(v1)
torch.mul(self.w1, self.w22, out=self.buffer)
gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
gw1.mul_(self.w.expand_as(v1))
torch.mul(self.w1, self.w32, out=self.buffer)
gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
gw2.mul_(self.w.expand_as(v1))
# self._idx = self._outputs <= 0
torch.le(self._outputs, 0, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw1.size())
gw1[self._idx] = 0
gw2[self._idx] = 0
torch.eq(y, 1, out=self._idx)
self._idx = self._idx.view(-1, 1).expand(gw2.size())
gw1[self._idx] = gw1[self._idx].mul_(-1)
gw2[self._idx] = gw2[self._idx].mul_(-1)
if self.sizeAverage:
gw1.div_(y.size(0))
gw2.div_(y.size(0))
return self.gradInput
def lesser_equal(x: T.FloatTensor, y: T.FloatTensor) -> T.ByteTensor:
"""
Elementwise test if x <= y.
Args:
x: A tensor.
y: A tensor.
Returns:
tensor (of bools): Elementwise test of x <= y.
"""
return torch.le(x, y)
