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.conv2()。
def test_conv2(self): x = torch.rand(math.floor(torch.uniform(50, 100)), math.floor(torch.uniform(50, 100))) k = torch.rand(math.floor(torch.uniform(10, 20)), math.floor(torch.uniform(10, 20))) imvc = torch.conv2(x, k) imvc2 = torch.conv2(x, k, 'V') imfc = torch.conv2(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] #for i=ks.size(), 1, -1 do kis[ks.size()-i+1]=ks[i] end imvx = torch.xcorr2(x, ki) imvx2 = torch.xcorr2(x, ki, 'V') imfx = torch.xcorr2(x, ki, 'F') self.assertEqual(imvc, imvc2, 0, 'torch.conv2') self.assertEqual(imvc, imvx, 0, 'torch.conv2') self.assertEqual(imvc, imvx2, 0, 'torch.conv2') self.assertEqual(imfc, imfx, 0, 'torch.conv2') self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr2(x, x)[0][0]), 1e-10, 'torch.conv2') xx = torch.Tensor(2, x.size(1), x.size(2)) xx[1].copy_(x) xx[2].copy_(x) kk = torch.Tensor(2, k.size(1), k.size(2)) kk[1].copy_(k) kk[2].copy_(k) immvc = torch.conv2(xx, kk) immvc2 = torch.conv2(xx, kk, 'V') immfc = torch.conv2(xx, kk, 'F') self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv2') self.assertEqual(immvc[0], imvc, 0, 'torch.conv2') self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv2') self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv2') self.assertEqual(immfc[0], imfc, 0, 'torch.conv2')
def test_conv3_conv2_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.conv2(x[i+j-1], k[k.size(1)-j+1])) self._test_conv_corr_eq(lambda x, k: torch.conv3(x, k), reference)
def test_fconv3_fconv2_eq(self): def reference(x, k, o3, o32): for i in range(o3.size(1)): for j in range(k.size(1)): o32[i+j-1].add(torch.conv2(x[i], k[j], 'F')) self._test_conv_corr_eq(lambda x, k: torch.conv3(x, k, 'F'), reference)
def test_conv2(self): x = torch.rand(math.floor(torch.uniform(50, 100)), math.floor(torch.uniform(50, 100))) k = torch.rand(math.floor(torch.uniform(10, 20)), math.floor(torch.uniform(10, 20))) imvc = torch.conv2(x, k) imvc2 = torch.conv2(x, k, 'V') imfc = torch.conv2(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] # for i=ks.size(), 1, -1 do kis[ks.size()-i+1]=ks[i] end imvx = torch.xcorr2(x, ki) imvx2 = torch.xcorr2(x, ki, 'V') imfx = torch.xcorr2(x, ki, 'F') self.assertEqual(imvc, imvc2, 0, 'torch.conv2') self.assertEqual(imvc, imvx, 0, 'torch.conv2') self.assertEqual(imvc, imvx2, 0, 'torch.conv2') self.assertEqual(imfc, imfx, 0, 'torch.conv2') self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr2(x, x)[0][0]), 1e-10, 'torch.conv2') xx = torch.Tensor(2, x.size(1), x.size(2)) xx[1].copy_(x) xx[2].copy_(x) kk = torch.Tensor(2, k.size(1), k.size(2)) kk[1].copy_(k) kk[2].copy_(k) immvc = torch.conv2(xx, kk) immvc2 = torch.conv2(xx, kk, 'V') immfc = torch.conv2(xx, kk, 'F') self.assertEqual(immvc[0], immvc[1], 0, 'torch.conv2') self.assertEqual(immvc[0], imvc, 0, 'torch.conv2') self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv2') self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv2') self.assertEqual(immfc[0], imfc, 0, 'torch.conv2')
def test_conv3_conv2_eq_valid(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.conv2(x[i + j - 1], k[k.size(1) - j + 1])) self._test_conv_corr_eq(lambda x, k: torch.conv3(x, k), reference)
def test_fconv3_fconv2_eq(self): def reference(x, k, o3, o32): for i in range(o3.size(1)): for j in range(k.size(1)): o32[i + j - 1].add(torch.conv2(x[i], k[j], 'F')) self._test_conv_corr_eq(lambda x, k: torch.conv3(x, k, 'F'), reference)
