remove pickle dependency

Author: liaopeiyuan

test/knn_test_large.pkl

@@ -3,7 +3,6 @@
 import pytest
 import torch
 from torch_cluster import knn, knn_graph
-import pickle
 from .utils import grad_dtypes, devices, tensor
 
 
@@ -61,29 +60,41 @@ def test_knn_graph(dtype, device):
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
 def test_knn_graph_large(dtype, device):
-    d = pickle.load(open("test/knn_test_large.pkl", "rb"))
-    x = d['x'].to(device)
-    k = d['k']
-    truth = d['edges']
-
-    row, col = knn_graph(x, k=k, flow='source_to_target',
-                         batch=None, n_threads=24)
+    x = torch.tensor([[-1.0320,  0.2380,  0.2380],
+                      [-1.3050, -0.0930,  0.6420],
+                      [-0.3190, -0.0410,  1.2150],
+                      [1.1400, -0.5390, -0.3140],
+                      [0.8410,  0.8290,  0.6090],
+                      [-1.4380, -0.2420, -0.3260],
+                      [-2.2980,  0.7160,  0.9320],
+                      [-1.3680, -0.4390,  0.1380],
+                      [-0.6710,  0.6060,  1.1800],
+                      [0.3950, -0.0790,  1.4920]],).to(device)
+    k = 3
+    truth = set({(4, 8), (2, 8), (9, 8), (8, 0), (0, 7), (2, 1), (9, 4),
+                 (5, 1), (4, 9), (2, 9), (8, 1), (1, 5), (5, 0), (3, 2),
+                 (8, 2), (7, 1), (6, 0), (3, 9), (0, 5), (7, 5), (4, 2),
+                 (1, 0), (0, 1), (7, 0), (6, 8), (9, 2), (6, 1), (5, 7),
+                 (1, 7), (3, 4)})
+
+    row, col = knn_graph(x, k=k, flow='target_to_source',
+                         batch=None, n_threads=24, loop=False)
 
     edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
                                           list(col.cpu().numpy()))])
 
     assert(truth == edges)
 
-    row, col = knn_graph(x, k=k, flow='source_to_target',
-                         batch=None, n_threads=12)
+    row, col = knn_graph(x, k=k, flow='target_to_source',
+                         batch=None, n_threads=12, loop=False)
 
     edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
                                           list(col.cpu().numpy()))])
 
     assert(truth == edges)
 
-    row, col = knn_graph(x, k=k, flow='source_to_target',
-                         batch=None, n_threads=1)
+    row, col = knn_graph(x, k=k, flow='target_to_source',
+                         batch=None, n_threads=1, loop=False)
 
     edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
                                           list(col.cpu().numpy()))])

test/radius_test_large.pkl

@@ -4,7 +4,14 @@
 import torch
 from torch_cluster import radius, radius_graph
 from .utils import grad_dtypes, devices, tensor
-import pickle
+import scipy.spatial
+
+
+@torch.jit.script
+def sample(col: torch.Tensor, count: int) -> torch.Tensor:
+    if col.size(0) > count:
+        col = col[torch.randperm(col.size(0), dtype=torch.long)][:count]
+    return col
 
 
 def coalesce(index):
@@ -594,10 +601,22 @@ def test_radius_graph_ndim(dtype, device):
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
 def test_radius_graph_large(dtype, device):
-    d = pickle.load(open("test/radius_test_large.pkl", "rb"))
-    x = d['x'].to(device)
-    r = d['r']
-    truth = d['edges']
+    x = torch.randn((8192*4, 6))
+    r = 0.5
+
+    tree = scipy.spatial.cKDTree(x.detach().cpu().numpy())
+    col = tree.query_ball_point(x.detach().cpu().numpy(), r)
+    col = [torch.tensor(c, dtype=torch.long) for c in col]
+    col = [sample(c, 32) for c in col]
+    row = [torch.full_like(c, i) for i, c in enumerate(col)]
+    row, col = torch.cat(row, dim=0), torch.cat(col, dim=0)
+    mask = col < int(tree.n)
+    row_truth, col_truth = torch.stack([row[mask], col[mask]], dim=0)
+    mask = row_truth != col_truth
+    row_truth, col_truth = row_truth[mask], col_truth[mask]
+
+    truth = (set([(i, j) for (i, j) in zip(list(row_truth.cpu().numpy()),
+                                           list(col_truth.cpu().numpy()))]))
 
     row, col = radius_graph(x, r=r, flow='source_to_target',
                             batch=None, n_threads=24)