本文整理匯總了Python中torch.roll方法的典型用法代碼示例。如果您正苦於以下問題:Python torch.roll方法的具體用法?Python torch.roll怎麽用?Python torch.roll使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torch
的用法示例。
在下文中一共展示了torch.roll方法的10個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: p2o
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def p2o(psf, shape):
'''
# psf: NxCxhxw
# shape: [H,W]
# otf: NxCxHxWx2
'''
otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf)
otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf)
for axis, axis_size in enumerate(psf.shape[2:]):
otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2)
otf = torch.rfft(otf, 2, onesided=False)
n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf)))
otf[...,1][torch.abs(otf[...,1])<n_ops*2.22e-16] = torch.tensor(0).type_as(psf)
return otf
# otf2psf: not sure where I got this one from. Maybe translated from Octave source code or whatever. It's just math.
示例2: p2o
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def p2o(psf, shape):
'''
Args:
psf: NxCxhxw
shape: [H,W]
Returns:
otf: NxCxHxWx2
'''
otf = torch.zeros(psf.shape[:-2] + shape).type_as(psf)
otf[...,:psf.shape[2],:psf.shape[3]].copy_(psf)
for axis, axis_size in enumerate(psf.shape[2:]):
otf = torch.roll(otf, -int(axis_size / 2), dims=axis+2)
otf = torch.rfft(otf, 2, onesided=False)
n_ops = torch.sum(torch.tensor(psf.shape).type_as(psf) * torch.log2(torch.tensor(psf.shape).type_as(psf)))
otf[...,1][torch.abs(otf[...,1])<n_ops*2.22e-16] = torch.tensor(0).type_as(psf)
return otf
示例3: test_roll
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def test_roll(workers):
bob, alice, james = (workers["bob"], workers["alice"], workers["james"])
t = torch.tensor([[1, 2, 3], [4, 5, 6]])
x = t.share(bob, alice, crypto_provider=james)
res1 = torch.roll(x, 2)
res2 = torch.roll(x, 2, dims=1)
res3 = torch.roll(x, (1, 2), dims=(0, 1))
assert (res1.get() == torch.roll(t, 2)).all()
assert (res2.get() == torch.roll(t, 2, dims=1)).all()
assert (res3.get() == torch.roll(t, (1, 2), dims=(0, 1))).all()
# With MultiPointerTensor
shifts = torch.tensor(1).send(alice, bob)
res = torch.roll(x, shifts)
shifts1 = torch.tensor(1).send(alice, bob)
shifts2 = torch.tensor(2).send(alice, bob)
res2 = torch.roll(x, (shifts1, shifts2), dims=(0, 1))
assert (res.get() == torch.roll(t, 1)).all()
assert (res2.get() == torch.roll(t, (1, 2), dims=(0, 1))).all()
示例4: calculate_mask
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def calculate_mask(self, inp):
# inp is batch_size x self.input_size where batch_size is num_processes*num_agents
pos = inp[:, self.pos_index:self.pos_index+2]
bsz = inp.size(0)//self.num_agents
mask = torch.full(size=(bsz,self.num_agents,self.num_agents),fill_value=0,dtype=torch.uint8)
if self.mask_dist is not None and self.mask_dist > 0:
for i in range(1,self.num_agents):
shifted = torch.roll(pos,-bsz*i,0)
dists = torch.norm(pos-shifted,dim=1)
restrict = dists > self.mask_dist
for x in range(self.num_agents):
mask[:,x,(x+i)%self.num_agents].copy_(restrict[bsz*x:bsz*(x+1)])
elif self.mask_dist is not None and self.mask_dist == -10:
if self.dropout_mask is None or bsz!=self.dropout_mask.shape[0] or np.random.random_sample() < 0.1: # sample new dropout mask
temp = torch.rand(mask.size()) > 0.85
temp.diagonal(dim1=1,dim2=2).fill_(0)
self.dropout_mask = (temp+temp.transpose(1,2))!=0
mask.copy_(self.dropout_mask)
return mask
示例5: otf2psf
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def otf2psf(otf, outsize=None):
insize = np.array(otf.shape)
psf = np.fft.ifftn(otf, axes=(0, 1))
for axis, axis_size in enumerate(insize):
psf = np.roll(psf, np.floor(axis_size / 2).astype(int), axis=axis)
if type(outsize) != type(None):
insize = np.array(otf.shape)
outsize = np.array(outsize)
n = max(np.size(outsize), np.size(insize))
# outsize = postpad(outsize(:), n, 1);
# insize = postpad(insize(:) , n, 1);
colvec_out = outsize.flatten().reshape((np.size(outsize), 1))
colvec_in = insize.flatten().reshape((np.size(insize), 1))
outsize = np.pad(colvec_out, ((0, max(0, n - np.size(colvec_out))), (0, 0)), mode="constant")
insize = np.pad(colvec_in, ((0, max(0, n - np.size(colvec_in))), (0, 0)), mode="constant")
pad = (insize - outsize) / 2
if np.any(pad < 0):
print("otf2psf error: OUTSIZE must be smaller than or equal than OTF size")
prepad = np.floor(pad)
postpad = np.ceil(pad)
dims_start = prepad.astype(int)
dims_end = (insize - postpad).astype(int)
for i in range(len(dims_start.shape)):
psf = np.take(psf, range(dims_start[i][0], dims_end[i][0]), axis=i)
n_ops = np.sum(otf.size * np.log2(otf.shape))
psf = np.real_if_close(psf, tol=n_ops)
return psf
# psf2otf copied/modified from https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py
示例6: __getitem__
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def __getitem__(self, index):
item = self.dataset[index]
return torch.roll(item, self.shifts)
示例7: test_roll
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def test_roll(workers):
x = torch.tensor([1.0, 2.0, 3, 4, 5])
expected = torch.roll(x, -1)
index = torch.tensor([-1.0])
result = torch.roll(x, index)
assert (result == expected).all()
示例8: psf2otf
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def psf2otf(psf, shape=None):
"""
Convert point-spread function to optical transfer function.
Compute the Fast Fourier Transform (FFT) of the point-spread
function (PSF) array and creates the optical transfer function (OTF)
array that is not influenced by the PSF off-centering.
By default, the OTF array is the same size as the PSF array.
To ensure that the OTF is not altered due to PSF off-centering, PSF2OTF
post-pads the PSF array (down or to the right) with zeros to match
dimensions specified in OUTSIZE, then circularly shifts the values of
the PSF array up (or to the left) until the central pixel reaches (1,1)
position.
Parameters
----------
psf : `numpy.ndarray`
PSF array
shape : int
Output shape of the OTF array
Returns
-------
otf : `numpy.ndarray`
OTF array
Notes
-----
Adapted from MATLAB psf2otf function
"""
if type(shape) == type(None):
shape = psf.shape
shape = np.array(shape)
if np.all(psf == 0):
# return np.zeros_like(psf)
return np.zeros(shape)
if len(psf.shape) == 1:
psf = psf.reshape((1, psf.shape[0]))
inshape = psf.shape
psf = zero_pad(psf, shape, position='corner')
for axis, axis_size in enumerate(inshape):
psf = np.roll(psf, -int(axis_size / 2), axis=axis)
# Compute the OTF
otf = np.fft.fft2(psf, axes=(0, 1))
# Estimate the rough number of operations involved in the FFT
# and discard the PSF imaginary part if within roundoff error
# roundoff error = machine epsilon = sys.float_info.epsilon
# or np.finfo().eps
n_ops = np.sum(psf.size * np.log2(psf.shape))
otf = np.real_if_close(otf, tol=n_ops)
return otf
示例9: maxpool_deriv
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def maxpool_deriv(x_sh):
""" Compute derivative of MaxPool
Args:
x_sh (AdditiveSharingTensor): the private tensor on which the op applies
Returns:
an AdditiveSharingTensor of the same shape as x_sh full of zeros except for
a 1 at the position of the max value
"""
assert (
x_sh.dtype != "custom"
), "`custom` dtype shares are unsupported in SecureNN, use dtype = `long` or `int` instead"
workers = x_sh.locations
crypto_provider = x_sh.crypto_provider
L = x_sh.field
dtype = get_dtype(L)
torch_dtype = get_torch_dtype(L)
n1, n2 = x_sh.shape
n = n1 * n2
assert L % n == 0
x_sh = x_sh.view(-1)
# Common Randomness
U_sh = _shares_of_zero(n, L, dtype, crypto_provider, *workers)
r = _random_common_value(L, *workers)
# 1)
_, ind_max_sh = maxpool(x_sh)
# 2)
j = sy.MultiPointerTensor(
children=[torch.tensor([int(i == 0)]).send(w, **no_wrap) for i, w in enumerate(workers)]
)
k_sh = ind_max_sh + j * r
# 3)
t = k_sh.get()
k = t % n
E_k = torch.zeros(n, dtype=torch_dtype)
E_k[k] = 1
E_sh = E_k.share(*workers, field=L, dtype=dtype, **no_wrap)
# 4)
g = r % n
D_sh = torch.roll(E_sh, -g)
maxpool_d_sh = D_sh + U_sh
return maxpool_d_sh.view(n1, n2)
示例10: fog_creator
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import roll [as 別名]
def fog_creator(fog_vars, bsize=1, mapsize=256, wibbledecay=1.75):
assert (mapsize & (mapsize - 1) == 0)
maparray = torch.from_numpy(np.empty((bsize, mapsize, mapsize), dtype=np.float32)).cuda()
maparray[:, 0, 0] = 0
stepsize = mapsize
wibble = 100
var_num = 0
def wibbledmean(array, var_num):
result = array / 4. + fog_vars[var_num] * 2 * wibble - wibble
return result
def fillsquares(var_num):
"""For each square of points stepsize apart,
calculate middle value as mean of points + wibble"""
cornerref = maparray[:, 0:mapsize:stepsize, 0:mapsize:stepsize]
squareaccum = cornerref + torch.roll(cornerref, -1, 1)
squareaccum = squareaccum + torch.roll(squareaccum, -1, 2)
maparray[:, stepsize // 2:mapsize:stepsize,
stepsize // 2:mapsize:stepsize] = wibbledmean(squareaccum, var_num)
return var_num + 1
def filldiamonds(var_num):
"""For each diamond of points stepsize apart,
calculate middle value as mean of points + wibble"""
mapsize = maparray.size(1)
drgrid = maparray[:, stepsize // 2:mapsize:stepsize, stepsize // 2:mapsize:stepsize]
ulgrid = maparray[:, 0:mapsize:stepsize, 0:mapsize:stepsize]
ldrsum = drgrid + torch.roll(drgrid, 2, 1)
lulsum = ulgrid + torch.roll(ulgrid, -1, 2)
ltsum = ldrsum + lulsum
maparray[:, 0:mapsize:stepsize, stepsize // 2:mapsize:stepsize] = wibbledmean(ltsum, var_num)
var_num += 1
tdrsum = drgrid + torch.roll(drgrid, 2, 2)
tulsum = ulgrid + torch.roll(ulgrid, -1, 1)
ttsum = tdrsum + tulsum
maparray[:, stepsize // 2:mapsize:stepsize, 0:mapsize:stepsize] = wibbledmean(ttsum, var_num)
return var_num + 1
while stepsize >= 2:
var_num = fillsquares(var_num)
var_num = filldiamonds(var_num)
stepsize //= 2
wibble /= wibbledecay
maparray = maparray - maparray.min()
return (maparray / maparray.max()).reshape(bsize, 1, mapsize, mapsize)