Python torch 模块,xcorr3() 实例源码
我们从Python开源项目中,提取了以下15个代码示例,用于说明如何使用torch.xcorr3()。
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i+j-1], k[j]))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k), reference)
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i + j - 1], k[j]))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k), reference)
def test_xcorr3_xcorr2_eq_full(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i + j - 1], k[j]))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k), reference)
def test_xcorr3_xcorr2_eq_full(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i + j - 1], k[j]))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k), reference)
def test_xcorr3_xcorr2_eq_full(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i + j - 1], k[j]))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k), reference)
def test_xcorr3_xcorr2_eq_full(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone();
ks = k.storage()
kis = ki.storage()
for i in range(ks.size()-1, 0, -1):
kis[ks.size()-i+1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, 'torch.conv3')
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, 'torch.conv3')
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, 'torch.conv3')
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, 'torch.conv3')
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, 'torch.conv3')