def test_conv2d_depthwise_multigpu(self):
n = 6
a0 = Variable(torch.randn(1, n, 5, 5).cuda(0), requires_grad=True)
a1 = Variable(torch.randn(1, n, 5, 5).cuda(1), requires_grad=True)
w0 = Variable(torch.randn(n, 1, 3, 3).double().cuda(0),
requires_grad=True)
w1 = Variable(torch.randn(n, 1, 3, 3).double().cuda(1),
requires_grad=True)
y0 = P.conv2d_depthwise(a0, w0, padding=1)
go = torch.randn(y0.size()).double().cuda()
y0.backward(go)
y1 = P.conv2d_depthwise(a1, w1, padding=1)
y1.backward(go.cuda(1))
def test_conv2d_depthwise(self):
n = 6
x = Variable(torch.randn(1, n, 5, 5).double().cuda(),
requires_grad=True)
w = Variable(torch.randn(n, 1, 3, 3).double().cuda(),
requires_grad=True)
y_fast = P.conv2d_depthwise(x, w, padding=1)
y_ref = F.conv2d(x, w, padding=1, groups=n)
go = torch.randn(y_fast.size()).double().cuda()
self.assertLess((y_fast - y_ref).data.abs().max(), 1e-9)
x.requires_grad = True
w.requires_grad = True
y_fast.backward(go)
gx_fast = x.grad.data.clone()
gw_fast = w.grad.data.clone()
x.grad.data.zero_()
w.grad.data.zero_()
y_ref.backward(go)
gx_ref = x.grad.data.clone()
gw_ref = w.grad.data.clone()
self.assertTrue(
gradcheck(partial(P.conv2d_depthwise, padding=1), (
x,
w,
)))
def forward(self, input):
return P.conv2d_depthwise(input, self.weight, self.bias, self.stride,
self.padding, self.dilation)