Significantly clean and document code base. Project approximately complete.

tests/util_funcs.py

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+# External Modules
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Internal Modules
+from PointCNN.core import UFloatTensor
+
+def plot_pts_and_fts(pts : UFloatTensor,  # (N, x, dims)
+                     fts : UFloatTensor   # (N, x, y)
+                    ) -> None:
+    """
+    Visualization function. Shows points and number of features, represented by
+    the size of the point.
+    :param pts: Point cloud such that fts[:,p_idx,:] is the feature associated
+    with pts[:,p_idx,:].
+    :param fts: Features such that pts[:,p_idx,:] is the feature associated
+    with fts[:,p_idx,:].
+    """
+    if pts.is_cuda:
+        pts = pts.cpu()
+    num_F = fts.size()[2]
+    pts = pts[0].data.numpy()
+    plt.scatter(pts[:,0], pts[:,1], s = num_F, c = "k")
+    plt.show()
+    plt.cla()
+
+def plot_neighborhood(pts : UFloatTensor,          # (N, x, dims)
+                      rep_pts : UFloatTensor,      # (N, P, dims)
+                      pts_regional : UFloatTensor  # (N, P, dims)
+                     ) -> None:
+    """
+    Visualization function. Shows neighborhood points around a randomly
+    selected representative.
+    :param pts: Point cloud.
+    :param rep_pts: Representative points.
+    :param pts_regional: Regional neighborhoods around representative points.
+    """
+    if rep_pts.is_cuda:
+        rep_pts = rep_pts.cpu()
+        pts_regional = pts_regional.cpu()
+    n = np.randint(0, rep_pts.shape[0])
+    t = np.randint(0, rep_pts.shape[1])
+    test_point = rep_pts[n,t,:].data.numpy()
+    neighborhood = pts_regional[n,t,:,:].data.numpy()
+    plt.scatter(pts[n][:,0], pts[n][:,1])
+    plt.scatter(test_point[0], test_point[1], s = 100, c = 'green')
+    plt.scatter(neighborhood[:,0], neighborhood[:,1], s = 100, c = 'red')
+    plt.show()
+    plt.cla()