我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用torch.var()。
def test_hooks_cycle(self): import gc counter = [0] class GradHook(object): def __init__(self, var): self.var = var def __del__(self): counter[0] += 1 def __call__(self, *args): pass def run_test(): x = Variable(torch.ones(5, 5), requires_grad=True) y = x * 2 x.register_hook(GradHook(x)) y.register_hook(GradHook(y)) y._backward_hooks[1] = GradHook(y) run_test() gc.collect() self.assertEqual(counter[0], 3)
def var(x, axis=None, keepdims=False): def _var(x, axis, keepdims): y = torch.var(x, axis) # Since keepdims argument of torch not functional return y if keepdims else torch.squeeze(y, axis) def _compute_output_shape(x, axis, keepdims): if axis is None: return () shape = list(_get_shape(x)) if keepdims: shape[axis] = 1 else: del shape[axis] return tuple(shape) return get_op(_var, output_shape=_compute_output_shape, arguments=[axis, keepdims])(x)
def forward(self, x): n = x.size(2) * x.size(3) t = x.view(x.size(0), x.size(1), n) mean = torch.mean(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x) # Calculate the biased var. torch.var returns unbiased var var = torch.var(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x) * ((n - 1) / float(n)) scale_broadcast = self.scale.unsqueeze(1).unsqueeze(1).unsqueeze(0) scale_broadcast = scale_broadcast.expand_as(x) shift_broadcast = self.shift.unsqueeze(1).unsqueeze(1).unsqueeze(0) shift_broadcast = shift_broadcast.expand_as(x) out = (x - mean) / torch.sqrt(var + self.eps) out = out * scale_broadcast + shift_broadcast return out
def test_type_conversions(self): x = Variable(torch.randn(5, 5)) self.assertIs(type(x.float().data), torch.FloatTensor) self.assertIs(type(x.int().data), torch.IntTensor) if torch.cuda.is_available(): self.assertIs(type(x.float().cuda().data), torch.cuda.FloatTensor) self.assertIs(type(x.int().cuda().data), torch.cuda.IntTensor) self.assertIs(type(x.int().cuda().cpu().data), torch.IntTensor) if torch.cuda.device_count() >= 2: x2 = x.float().cuda(1) self.assertIs(type(x2.data), torch.cuda.FloatTensor) self.assertIs(x2.get_device(), 1) x2 = x.float().cuda() self.assertIs(type(x2.data), torch.cuda.FloatTensor) self.assertIs(x2.get_device(), 0) x2 = x2.cuda(1) self.assertIs(type(x2.data), torch.cuda.FloatTensor) self.assertIs(x2.get_device(), 1) y = Variable(torch.randn(5).cuda(1), requires_grad=True) y.cpu().sum().backward() self.assertIs(y.grad.get_device(), 1) self.assertIs(y.long().data.get_device(), 1) for t in [torch.DoubleTensor, torch.FloatTensor, torch.IntTensor, torch.ByteTensor]: for var in (True, False): y = torch.randn(5, 5).type(t) if var: y = Variable(y) self.assertIs(type(x.type_as(y).data), t)
def forward(self, x): n = x.size(2) * x.size(3) t = x.view(x.size(0), x.size(1), n) mean = torch.mean(t, 2).unsqueeze(2).expand_as(x) # Calculate the biased var. torch.var returns unbiased var var = torch.var(t, 2).unsqueeze(2).expand_as(x) * ((n - 1) / float(n)) scale_broadcast = self.weight.unsqueeze(1).unsqueeze(1).unsqueeze(0) scale_broadcast = scale_broadcast.expand_as(x) shift_broadcast = self.bias.unsqueeze(1).unsqueeze(1).unsqueeze(0) shift_broadcast = shift_broadcast.expand_as(x) out = (x - mean) / torch.sqrt(var + self.eps) out = out * scale_broadcast + shift_broadcast return out
def test_keepdim_warning(self): torch.utils.backcompat.keepdim_warning.enabled = True x = Variable(torch.randn(3, 4), requires_grad=True) def run_backward(y): y_ = y if type(y) is tuple: y_ = y[0] # check that backward runs smooth y_.backward(y_.data.new(y_.size()).normal_()) def keepdim_check(f): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") y = f(x, 1) self.assertTrue(len(w) == 1) self.assertTrue(issubclass(w[-1].category, UserWarning)) self.assertTrue("keepdim" in str(w[-1].message)) run_backward(y) self.assertEqual(x.size(), x.grad.size()) # check against explicit keepdim y2 = f(x, 1, keepdim=False) self.assertEqual(y, y2) run_backward(y2) y3 = f(x, 1, keepdim=True) if type(y3) == tuple: y3 = (y3[0].squeeze(1), y3[1].squeeze(1)) else: y3 = y3.squeeze(1) self.assertEqual(y, y3) run_backward(y3) keepdim_check(torch.sum) keepdim_check(torch.prod) keepdim_check(torch.mean) keepdim_check(torch.max) keepdim_check(torch.min) keepdim_check(torch.mode) keepdim_check(torch.median) keepdim_check(torch.kthvalue) keepdim_check(torch.var) keepdim_check(torch.std) torch.utils.backcompat.keepdim_warning.enabled = False
