我们从Python开源项目中，提取了以下18个代码示例，用于说明如何使用torch.median()。
def split_ps(point_set): #print point_set.size() num_points = point_set.size()[0]/2 diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0] dim = torch.max(diff, dim = 1)[1][0,0] cut = torch.median(point_set[:,dim])[0][0] left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut)) right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut)) middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut)) if torch.numel(left_idx) < num_points: left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0) if torch.numel(right_idx) < num_points: right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0) left_ps = torch.index_select(point_set, dim = 0, index = left_idx) right_ps = torch.index_select(point_set, dim = 0, index = right_idx) return left_ps, right_ps, dim
def split_ps(point_set): #print point_set.size() num_points = point_set.size()[0]/2 diff = point_set.max(dim=0, keepdim = True)[0] - point_set.min(dim=0, keepdim = True)[0] dim = torch.max(diff, dim = 1, keepdim = True)[1][0,0] cut = torch.median(point_set[:,dim], keepdim = True)[0][0] left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut)) right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut)) middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut)) if torch.numel(left_idx) < num_points: left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0) if torch.numel(right_idx) < num_points: right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0) left_ps = torch.index_select(point_set, dim = 0, index = left_idx) right_ps = torch.index_select(point_set, dim = 0, index = right_idx) return left_ps, right_ps, dim
def split_ps(point_set): #print point_set.size() num_points = point_set.size()[0]/2 diff = point_set.max(dim=0)[0] - point_set.min(dim=0)[0] diff = diff[:3] dim = torch.max(diff, dim = 1)[1][0,0] cut = torch.median(point_set[:,dim])[0][0] left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut)) right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut)) middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut)) if torch.numel(left_idx) < num_points: left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0) if torch.numel(right_idx) < num_points: right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0) left_ps = torch.index_select(point_set, dim = 0, index = left_idx) right_ps = torch.index_select(point_set, dim = 0, index = right_idx) return left_ps, right_ps, dim
def test_median(self): for size in (155, 156): x = torch.rand(size, size) x0 = x.clone() res1val, res1ind = torch.median(x) res2val, res2ind = torch.sort(x) ind = int(math.floor((size+1)/2) - 1) self.assertEqual(res2val.select(1, ind), res1val.select(1, 0), 0) self.assertEqual(res2val.select(1, ind), res1val.select(1, 0), 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.median(res2val, res2ind, x) self.assertEqual(res2val, res1val, 0) self.assertEqual(res2ind, res1ind, 0) # Test non-default dim res1val, res1ind = torch.median(x, 0) res2val, res2ind = torch.sort(x, 0) self.assertEqual(res1val[0], res2val[ind], 0) self.assertEqual(res1ind[0], res2ind[ind], 0) # input unchanged self.assertEqual(x, x0, 0)
def test_median(self): for size in (155, 156): x = torch.rand(size, size) x0 = x.clone() res1val, res1ind = torch.median(x) res2val, res2ind = torch.sort(x) ind = int(math.floor((size + 1) / 2) - 1) self.assertEqual(res2val.select(1, ind), res1val.select(1, 0), 0) self.assertEqual(res2val.select(1, ind), res1val.select(1, 0), 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.median(x, out=(res2val, res2ind)) self.assertEqual(res2val, res1val, 0) self.assertEqual(res2ind, res1ind, 0) # Test non-default dim res1val, res1ind = torch.median(x, 0) res2val, res2ind = torch.sort(x, 0) self.assertEqual(res1val[0], res2val[ind], 0) self.assertEqual(res1ind[0], res2ind[ind], 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_median(self): for size in (155, 156): x = torch.rand(size, size) x0 = x.clone() res1val, res1ind = torch.median(x, keepdim=False) res2val, res2ind = torch.sort(x) ind = int(math.floor((size + 1) / 2) - 1) self.assertEqual(res2val.select(1, ind), res1val, 0) self.assertEqual(res2val.select(1, ind), res1val, 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.median(x, keepdim=False, out=(res2val, res2ind)) self.assertEqual(res2val, res1val, 0) self.assertEqual(res2ind, res1ind, 0) # Test non-default dim res1val, res1ind = torch.median(x, 0, keepdim=False) res2val, res2ind = torch.sort(x, 0) self.assertEqual(res1val, res2val[ind], 0) self.assertEqual(res1ind, res2ind[ind], 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_median(self): for size in (155, 156): x = torch.rand(size, size) x0 = x.clone() nelem = x.nelement() res1val = torch.median(x) res2val, _ = torch.sort(x.view(nelem)) ind = int(math.floor((nelem + 1) / 2) - 1) self.assertEqual(res2val[ind], res1val, 0) res1val, res1ind = torch.median(x, dim=1, keepdim=False) res2val, res2ind = torch.sort(x) ind = int(math.floor((size + 1) / 2) - 1) self.assertEqual(res2val.select(1, ind), res1val, 0) self.assertEqual(res2val.select(1, ind), res1val, 0) # Test use of result tensor res2val = torch.Tensor() res2ind = torch.LongTensor() torch.median(x, keepdim=False, out=(res2val, res2ind)) self.assertEqual(res2val, res1val, 0) self.assertEqual(res2ind, res1ind, 0) # Test non-default dim res1val, res1ind = torch.median(x, 0, keepdim=False) res2val, res2ind = torch.sort(x, 0) self.assertEqual(res1val, res2val[ind], 0) self.assertEqual(res1ind, res2ind[ind], 0) # input unchanged self.assertEqual(x, x0, 0)
def split_ps_reuse(point_set, level, pos, tree, cutdim): sz = point_set.size() num_points = np.array(sz)[0]/2 max_value = point_set.max(dim=0)[0] min_value = -(-point_set).max(dim=0)[0] diff = max_value - min_value dim = torch.max(diff, dim = 1)[1][0,0] cut = torch.median(point_set[:,dim])[0][0] left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut)) right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut)) middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut)) if torch.numel(left_idx) < num_points: left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0) if torch.numel(right_idx) < num_points: right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0) left_ps = torch.index_select(point_set, dim = 0, index = left_idx) right_ps = torch.index_select(point_set, dim = 0, index = right_idx) tree[level+1][pos * 2] = left_ps tree[level+1][pos * 2 + 1] = right_ps cutdim[level][pos * 2] = dim cutdim[level][pos * 2 + 1] = dim return
def split_ps_reuse(point_set, level, pos, tree, cutdim): sz = point_set.size() num_points = np.array(sz)[0]/2 max_value = point_set.max(dim=0)[0] min_value = -(-point_set).max(dim=0)[0] diff = max_value - min_value diff = diff[:,:3] dim = torch.max(diff, dim = 1)[1][0,0] cut = torch.median(point_set[:,dim])[0][0] left_idx = torch.squeeze(torch.nonzero(point_set[:,dim] > cut)) right_idx = torch.squeeze(torch.nonzero(point_set[:,dim] < cut)) middle_idx = torch.squeeze(torch.nonzero(point_set[:,dim] == cut)) if torch.numel(left_idx) < num_points: left_idx = torch.cat([left_idx, middle_idx[0:1].repeat(num_points - torch.numel(left_idx))], 0) if torch.numel(right_idx) < num_points: right_idx = torch.cat([right_idx, middle_idx[0:1].repeat(num_points - torch.numel(right_idx))], 0) left_ps = torch.index_select(point_set, dim = 0, index = left_idx) right_ps = torch.index_select(point_set, dim = 0, index = right_idx) tree[level+1][pos * 2] = left_ps tree[level+1][pos * 2 + 1] = right_ps cutdim[level][pos * 2] = dim cutdim[level][pos * 2 + 1] = dim return
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
