我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用torch.mode()。
def test_mode(self): x = torch.range(1, SIZE * SIZE).clone().resize_(SIZE, SIZE) x[:2] = 1 x[:,:2] = 1 x0 = x.clone() # Pre-calculated results. res1val = torch.Tensor(SIZE, 1).fill_(1) # The indices are the position of the last appearance of the mode element. res1ind = torch.LongTensor(SIZE, 1).fill_(1) res1ind[0] = SIZE-1 res1ind[1] = SIZE-1 res2val, res2ind = torch.mode(x) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.mode(res2val, res2ind, x) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test non-default dim res2val, res2ind = torch.mode(x, 0) self.assertEqual(res1val.view(1, SIZE), res2val, 0) self.assertEqual(res1ind.view(1, SIZE), res2ind, 0) # input unchanged self.assertEqual(x, x0, 0)
def test_mode(self): x = torch.arange(1, SIZE * SIZE + 1).clone().resize_(SIZE, SIZE) x[:2] = 1 x[:, :2] = 1 x0 = x.clone() # Pre-calculated results. res1val = torch.Tensor(SIZE, 1).fill_(1) # The indices are the position of the last appearance of the mode element. res1ind = torch.LongTensor(SIZE, 1).fill_(1) res1ind[0] = SIZE - 1 res1ind[1] = SIZE - 1 res2val, res2ind = torch.mode(x) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.mode(x, out=(res2val, res2ind)) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test non-default dim res2val, res2ind = torch.mode(x, 0) self.assertEqual(res1val.view(1, SIZE), res2val, 0) self.assertEqual(res1ind.view(1, SIZE), res2ind, 0) # input unchanged self.assertEqual(x, x0, 0)
def test_dim_reduction(self): dim_red_fns = [ "mean", "median", "mode", "norm", "prod", "std", "sum", "var", "max", "min"] def normfn_attr(t, dim, keepdim=True): attr = getattr(torch, "norm") return attr(t, 2, dim, keepdim) for fn_name in dim_red_fns: x = torch.randn(3, 4, 5) fn_attr = getattr(torch, fn_name) if fn_name != "norm" else normfn_attr def fn(t, dim, keepdim=True): ans = fn_attr(x, dim, keepdim) return ans if not isinstance(ans, tuple) else ans[0] dim = random.randint(0, 2) self.assertEqual(fn(x, dim, False).unsqueeze(dim), fn(x, dim)) self.assertEqual(x.ndimension() - 1, fn(x, dim, False).ndimension()) self.assertEqual(x.ndimension(), fn(x, dim, True).ndimension()) # check 1-d behavior x = torch.randn(1) dim = 0 self.assertEqual(fn(x, dim), fn(x, dim, True)) self.assertEqual(x.ndimension(), fn(x, dim).ndimension()) self.assertEqual(x.ndimension(), fn(x, dim, True).ndimension())
def test_mode(self): x = torch.arange(1, SIZE * SIZE + 1).clone().resize_(SIZE, SIZE) x[:2] = 1 x[:, :2] = 1 x0 = x.clone() # Pre-calculated results. res1val = torch.Tensor(SIZE).fill_(1) # The indices are the position of the last appearance of the mode element. res1ind = torch.LongTensor(SIZE).fill_(1) res1ind[0] = SIZE - 1 res1ind[1] = SIZE - 1 res2val, res2ind = torch.mode(x, keepdim=False) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.mode(x, keepdim=False, out=(res2val, res2ind)) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # Test non-default dim res2val, res2ind = torch.mode(x, 0, False) self.assertEqual(res1val, res2val, 0) self.assertEqual(res1ind, res2ind, 0) # input unchanged self.assertEqual(x, x0, 0)
def _test_dim_reduction(self, cast): dim_red_fns = [ "mean", "median", "mode", "norm", "prod", "std", "sum", "var", "max", "min"] def normfn_attr(t, dim, keepdim=False): attr = getattr(torch, "norm") return attr(t, 2, dim, keepdim) for fn_name in dim_red_fns: fn_attr = getattr(torch, fn_name) if fn_name != "norm" else normfn_attr def fn(x, dim, keepdim=False): ans = fn_attr(x, dim, keepdim=keepdim) return ans if not isinstance(ans, tuple) else ans[0] def test_multidim(x, dim): self.assertEqual(fn(x, dim).unsqueeze(dim), fn(x, dim, keepdim=True)) self.assertEqual(x.ndimension() - 1, fn(x, dim).ndimension()) self.assertEqual(x.ndimension(), fn(x, dim, keepdim=True).ndimension()) # general case x = cast(torch.randn(3, 4, 5)) dim = random.randint(0, 2) test_multidim(x, dim) # check 1-d behavior x = cast(torch.randn(1)) dim = 0 self.assertEqual(fn(x, dim), fn(x, dim, keepdim=True)) self.assertEqual(x.ndimension(), fn(x, dim).ndimension()) self.assertEqual(x.ndimension(), fn(x, dim, keepdim=True).ndimension()) # check reducing of a singleton dimension dims = [3, 4, 5] singleton_dim = random.randint(0, 2) dims[singleton_dim] = 1 x = cast(torch.randn(dims)) test_multidim(x, singleton_dim)
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
