wassname/PointCNN
1
0
Fork 0
mirror of https://github.com/wassname/PointCNN.git synced 2026-09-11 11:51:29 +08:00
Code Issues Packages Projects Releases Wiki Activity

Significantly clean and document code base. Project approximately complete.

Browse Source
This commit is contained in:
Austin J. Garrett
2018-04-08 19:47:11 -04:00
parent 8a4d826f91
commit 4af37be8fd
18 changed files with 497 additions and 472 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/context.py
-4
View File
@@ -1,4 +0,0 @@
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import pointcnn
tests/test.py
+82
View File
@@ -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()
tests/test_advanced.py
-10
View File
@@ -1,10 +0,0 @@
import unittest
class AdvancedTests(unittest.TestCase):
""" Basic test cases """
def test_example(self):
self.assertTrue(True)
if __name__ == "__main__":
unittest.main()
tests/test_basic.py
+38 -3
View File
@@ -4,14 +4,16 @@ import torch
from torch.autograd import Variable
import numpy as np
from pointcnn.core import XConv, knn_indices_func
from PointCNN import XConv, RandPointCNN, knn_indices_func_cpu
from PointCNN.tests import plot_pts_and_fts
np.random.seed(0)
class BasicTests(unittest.TestCase):
""" Basic test cases """
def test_xconv_shape(self):
self.assertTrue(True)
np.random.seed(0)
N = 4
D = 3
@@ -36,12 +38,45 @@ class BasicTests(unittest.TestCase):
P = Variable(torch.from_numpy(P))
ps = Variable(torch.from_numpy(ps))
out = knn_indices_func(ps, P, 2).numpy()
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()
tests/util_funcs.py
+49
View File
@@ -0,0 +1,49 @@
# 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()