Significantly clean and document code base. Project approximately complete.

2026-09-11 11:51:29 +08:00 · 2018-04-08 19:47:11 -04:00
parent 8a4d826f91
commit 4af37be8fd
18 changed files with 497 additions and 472 deletions
@@ -0,0 +1,82 @@
+import unittest
+
+import torch
+from torch.autograd import Variable
+import numpy as np
+
+from PointCNN import XConv, RandPointCNN, knn_indices_func_cpu
+from PointCNN.tests.util_funcs import plot_pts_and_fts
+
+np.random.seed(0)
+
+class BasicTests(unittest.TestCase):
+    """ Basic test cases """
+
+    def test_xconv_shape(self):
+        self.assertTrue(True)
+
+        N = 4
+        D = 3
+        C_in = 8
+        C_out = 32
+        N_neighbors = 100
+
+        model = XConv(C_in, C_out, D, N_neighbors).cuda()
+        p = Variable(torch.from_numpy(np.random.rand(N,D).astype(np.float32))).cuda()
+        P = Variable(torch.from_numpy(np.random.rand(N,N_neighbors,D).astype(np.float32))).cuda()
+        F = Variable(torch.from_numpy(np.random.rand(N,N_neighbors,C_in).astype(np.float32))).cuda()
+        out = model(p, P, F)
+        self.assertEqual(out.size(), (N, C_out))
+
+    def test_knn(self):
+        P = np.array([[[0,0],
+                       [0,0.95],
+                       [1,0],
+                       [1,1]]])
+        ps = P[:,[0,3],:]
+
+        P = Variable(torch.from_numpy(P))
+        ps = Variable(torch.from_numpy(ps))
+
+        out = knn_indices_func_cpu(ps, P, 2).numpy()
+        target = np.array([[[1,2],
+                            [2,1]]])
+
+        self.assertTrue(np.array_equal(target, out))
+
+    def test_pointcnn_shape(self):
+        N = 1
+        num_points = 1000
+        dims = 2
+        C_in = 4
+        K = 10
+        D = 1
+
+        layer1 = RandPointCNN(C_in,   8, dims, K, D, 1000, knn_indices_func_cpu).cuda()
+        layer2 = RandPointCNN(   8,  16, dims, K, D,  500, knn_indices_func_cpu).cuda()
+        layer3 = RandPointCNN(  16,  32, dims, K, D,  250, knn_indices_func_cpu).cuda()
+        layer4 = RandPointCNN(  32,  64, dims, K, D,  125, knn_indices_func_cpu).cuda()
+        layer5 = RandPointCNN(  64, 128, dims, K, D,   50, knn_indices_func_cpu).cuda()
+
+        pts  = np.random.rand(N,num_points,dims).astype(np.float32)
+        fts  = np.random.rand(N,num_points,C_in).astype(np.float32)
+        pts = Variable(torch.from_numpy(pts)).cuda()
+        fts = Variable(torch.from_numpy(fts)).cuda()
+
+        if True:
+            pts, fts = layer1((pts, fts))
+        else:
+            plot_pts_and_fts(pts, fts)
+            pts, fts = layer1((pts, fts))
+            plot_pts_and_fts(pts, fts)
+            pts, fts = layer2((pts, fts))
+            plot_pts_and_fts(pts, fts)
+            pts, fts = layer3((pts, fts))
+            plot_pts_and_fts(pts, fts)
+            pts, fts = layer4((pts, fts))
+            plot_pts_and_fts(pts, fts)
+            pts, fts = layer5((pts, fts))
+            plot_pts_and_fts(pts, fts)
+
+if __name__ == "__main__":
+    unittest.main()