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Commit d7f704c5 authored by Alexander Liao's avatar Alexander Liao
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fixed C++ warning and python flake8 style

parent 1111319d
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Showing with 540 additions and 442 deletions
csrc/cpu/radius_cpu.cpp
View file @ d7f704c5
......@@ -15,8 +15,8 @@ torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
AT_DISPATCH_ALL_TYPES(query.scalar_type(), "radius_cpu", [&] {
auto data_q = query.DATA_PTR<scalar_t>();
auto data_s = support.DATA_PTR<scalar_t>();
auto data_q = query.data_ptr<scalar_t>();
auto data_s = support.data_ptr<scalar_t>();
std::vector<scalar_t> queries_stl = std::vector<scalar_t>(data_q,
data_q + query.size(0)*query.size(1));
std::vector<scalar_t> supports_stl = std::vector<scalar_t>(data_s,
......@@ -34,13 +34,7 @@ torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
out = torch::from_blob(neighbors_indices_ptr, {tsize, 2}, options=options);
out = out.t();
auto result = torch::zeros_like(out);
auto index = torch::tensor({0,1});
result.index_copy_(0, index, out);
return result;
return out.clone();
}
......@@ -49,7 +43,7 @@ void get_size_batch(const vector<long>& batch, vector<long>& res){
res.resize(batch[batch.size()-1]-batch[0]+1, 0);
long ind = batch[0];
long incr = 1;
for(int i=1; i < batch.size(); i++){
for(unsigned long i=1; i < batch.size(); i++){
if(batch[i] == ind)
incr++;
......@@ -81,8 +75,7 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
auto options = torch::TensorOptions().dtype(torch::kLong).device(torch::kCPU);
int max_count = 0;
AT_DISPATCH_ALL_TYPES(query.scalar_type(), "batch_radius_search", [&] {
AT_DISPATCH_ALL_TYPES(query.scalar_type(), "batch_radius_cpu", [&] {
auto data_q = query.data_ptr<scalar_t>();
auto data_s = support.data_ptr<scalar_t>();
std::vector<scalar_t> queries_stl = std::vector<scalar_t>(data_q,
......
csrc/cpu/utils/neighbors.cpp
View file @ d7f704c5
......@@ -127,20 +127,18 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
// Initiate variables
// ******************
// indices
int i0 = 0;
size_t i0 = 0;
// Square radius
const scalar_t r2 = static_cast<scalar_t>(radius*radius);
// Counting vector
int max_count = 0;
float d2;
size_t max_count = 0;
// batch index
long b = 0;
long sum_qb = 0;
long sum_sb = 0;
size_t b = 0;
size_t sum_qb = 0;
size_t sum_sb = 0;
float eps = 0.000001;
// Nanoflann related variables
......@@ -173,16 +171,9 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
for (auto& p0 : query_pcd.pts){
// Check if we changed batch
scalar_t query_pt[dim];
scalar_t* query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
/*
std::cout << "\n ========== \n";
for(int i=0; i < dim; i++)
std::cout << query_pt[i] << '\n';
std::cout << "\n ========== \n";
*/
if (i0 == sum_qb + q_batches[b]){
sum_qb += q_batches[b];
sum_sb += s_batches[b];
......@@ -218,7 +209,7 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
}
// Reserve the memory
int size = 0; // total number of edges
size_t size = 0; // total number of edges
for (auto& inds_dists : all_inds_dists){
if(inds_dists.size() <= max_count)
size += inds_dists.size();
......@@ -230,14 +221,14 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
sum_sb = 0;
sum_qb = 0;
b = 0;
int u = 0;
size_t u = 0;
for (auto& inds_dists : all_inds_dists){
if (i0 == sum_qb + q_batches[b]){
sum_qb += q_batches[b];
sum_sb += s_batches[b];
b++;
}
for (int j = 0; j < max_count; j++){
for (size_t j = 0; j < max_count; j++){
if (j < inds_dists.size()){
neighbors_indices[u] = inds_dists[j].first + sum_sb;
neighbors_indices[u + 1] = i0;
......
test/test_radius.py
View file @ d7f704c5
......@@ -60,406 +60,533 @@ def test_radius_graph(dtype, device):
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_radius_graph_pointnet_small(dtype, device):
x = tensor([[0.2108, 0.4500, 0.8108],
[-0.2332, 0.3985, 0.8528],
[-0.2775, -0.3740, -0.1187],
[-0.1254, 0.3485, 0.5012],
[-0.1781, -0.1049, 0.3394],
[0.1526, -0.3718, 0.3394],
[-0.0544, 0.4183, 0.9912],
[-0.1490, 0.3866, 0.7689],
[-0.2845, -0.3310, 0.1143],
[-0.1204, 0.4521, 0.8257],
[0.2967, 0.4197, 0.9101],
[0.2963, 0.4398, 0.9158],
[-0.0125, -0.8122, 0.0335],
[-0.2287, -0.3621, -0.7152],
[0.1552, 0.0293, 0.4112],
[-0.1401, -0.8694, 0.0335],
[-0.3149, -0.5765, -0.0264],
[0.3324, -0.7056, -0.0264],
[-0.1534, -0.3684, 0.0335],
[-0.2079, 0.3677, 0.2303],
[-0.3143, -0.6923, -0.0407],
[-0.1147, -0.7468, -0.6810],
[-0.0311, 0.2705, 0.7223],
[0.1081, 0.0270, 0.1415],
[-0.1530, -0.8644, -0.1013],
[0.6482, 0.0973, 0.1577],
[0.4516, 0.0249, 0.0786],
[0.3632, 0.2255, 0.1577],
[-0.1171, 0.5048, 0.1520],
[0.0538, 1.0000, -0.3962],
[-0.0750, 0.0821, 0.1577],
[0.7979, 0.8418, 0.1225],
[0.1155, 1.0000, 0.1379],
[0.4076, 1.0000, -0.2864],
[0.6952, 0.7443, 0.0786],
[0.0086, 0.8644, -0.6780],
[0.4699, 0.1373, 0.5841],
[-0.1617, 0.1948, 0.0057],
[-0.3257, -0.6694, -0.4746],
[-0.2095, 0.8714, 0.1482],
[-0.1199, 0.4595, -0.3244],
[0.2812, -0.6382, -0.3244],
[0.5017, -0.6939, 0.4479],
[0.4120, -0.8335, 0.3682],
[0.3566, -0.7789, -0.3244],
[-0.2904, -0.1869, -0.3244],
[-0.1890, -0.8423, 0.0057],
[0.3787, 0.5441, -0.1557]],dtype, device)
batch = tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], torch.long, device)
x = tensor([[0.2108, 0.4500, 0.8108],
[-0.2332, 0.3985, 0.8528],
[-0.2775, -0.3740, -0.1187],
[-0.1254, 0.3485, 0.5012],
[-0.1781, -0.1049, 0.3394],
[0.1526, -0.3718, 0.3394],
[-0.0544, 0.4183, 0.9912],
[-0.1490, 0.3866, 0.7689],
[-0.2845, -0.3310, 0.1143],
[-0.1204, 0.4521, 0.8257],
[0.2967, 0.4197, 0.9101],
[0.2963, 0.4398, 0.9158],
[-0.0125, -0.8122, 0.0335],
[-0.2287, -0.3621, -0.7152],
[0.1552, 0.0293, 0.4112],
[-0.1401, -0.8694, 0.0335],
[-0.3149, -0.5765, -0.0264],
[0.3324, -0.7056, -0.0264],
[-0.1534, -0.3684, 0.0335],
[-0.2079, 0.3677, 0.2303],
[-0.3143, -0.6923, -0.0407],
[-0.1147, -0.7468, -0.6810],
[-0.0311, 0.2705, 0.7223],
[0.1081, 0.0270, 0.1415],
[-0.1530, -0.8644, -0.1013],
[0.6482, 0.0973, 0.1577],
[0.4516, 0.0249, 0.0786],
[0.3632, 0.2255, 0.1577],
[-0.1171, 0.5048, 0.1520],
[0.0538, 1.0000, -0.3962],
[-0.0750, 0.0821, 0.1577],
[0.7979, 0.8418, 0.1225],
[0.1155, 1.0000, 0.1379],
[0.4076, 1.0000, -0.2864],
[0.6952, 0.7443, 0.0786],
[0.0086, 0.8644, -0.6780],
[0.4699, 0.1373, 0.5841],
[-0.1617, 0.1948, 0.0057],
[-0.3257, -0.6694, -0.4746],
[-0.2095, 0.8714, 0.1482],
[-0.1199, 0.4595, -0.3244],
[0.2812, -0.6382, -0.3244],
[0.5017, -0.6939, 0.4479],
[0.4120, -0.8335, 0.3682],
[0.3566, -0.7789, -0.3244],
[-0.2904, -0.1869, -0.3244],
[-0.1890, -0.8423, 0.0057],
[0.3787, 0.5441, -0.1557]], dtype, device)
batch = tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3], torch.long, device)
row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
edges = set([(i,j) for (i,j) in zip(row.cpu().numpy(),col.cpu().numpy())])
truth_row = [10, 11, 7, 9, 9, 1, 9, 1, 6, 7, 0, 11, 0, 10, 15, 12, 20, 16, 34, 31, 44, 43, 42, 41]
truth_col = [0, 0, 1, 1, 6, 7, 7, 9, 9, 9, 10, 10, 11, 11, 12, 15, 16, 20, 31, 34, 41, 42, 43, 44]
truth = set([(i,j) for (i,j) in zip(truth_row, truth_col)])
assert(truth==edges)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
truth_row = [10, 11, 7, 9, 9, 1, 9, 1, 6, 7, 0, 11, 0, 10, 15, 12, 20, 16,
34, 31, 44, 43, 42, 41]
truth_col = [0, 0, 1, 1, 6, 7, 7, 9, 9, 9, 10, 10, 11, 11, 12, 15, 16, 20,
31, 34, 41, 42, 43, 44]
truth = set([(i, j) for (i, j) in zip(truth_row, truth_col)])
assert(truth == edges)
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_radius_graph_pointnet_medium(dtype, device):
#print('medium test {}'.format(device))
x = tensor([[-4.4043e-02, -5.7983e-01, -9.7623e-02],
[3.0804e-01, -1.8622e-01, 1.9274e-01],
[1.9475e-02, 1.4221e-01, -2.7513e-02],
[1.2231e-01, 1.8757e-02, -3.0827e-02],
[2.8174e-01, 9.4371e-01, -3.7868e-02],
[-3.3236e-01, -4.4852e-01, -1.6271e-01],
[2.4958e-02, -5.8395e-01, -4.7551e-02],
[-7.8186e-02, 4.2777e-01, -1.6271e-01],
[3.3085e-01, -4.6802e-01, 2.0720e-01],
[2.0581e-01, 4.4242e-01, 2.3870e-01],
[-3.0456e-02, -7.2156e-02, -2.7691e-01],
[1.2031e-01, -5.8759e-01, 3.2058e-02],
[3.0601e-01, 5.9611e-01, 1.6639e-01],
[-6.4328e-01, -5.1151e-01, -1.4634e-01],
[1.7828e-01, -4.9254e-02, 3.5978e-02],
[-2.9514e-01, -3.5513e-01, -1.6588e-01],
[6.1963e-02, -3.9553e-02, -3.2432e-01],
[2.9479e-01, -1.2834e-01, 5.4388e-02],
[-7.4315e-02, 9.8307e-01, -3.2098e-01],
[3.0474e-01, 7.0636e-01, -1.1068e-01],
[-4.8368e-01, -5.9097e-01, 3.7825e-02],
[-1.2121e-01, -1.4650e-01, -1.7073e-01],
[1.9211e-01, 7.6821e-02, 1.8061e-01],
[1.6331e-01, 6.9012e-02, -2.8236e-02],
[-1.1057e-01, -1.5881e-01, -1.7098e-01],
[-2.3803e-01, 7.0286e-01, -1.3979e-01],
[3.1751e-01, 1.7584e-02, 2.0985e-01],
[6.0034e-02, -5.7922e-01, -9.1098e-02],
[-6.8475e-01, -9.7682e-02, -3.2432e-01],
[1.6659e-01, -4.9622e-01, -5.1318e-02],
[-3.2701e-01, -4.7730e-01, 1.2881e-01],
[2.1012e-01, -8.7334e-02, 1.7803e-01],
[2.7983e-01, -3.7054e-02, -1.5182e-01],
[1.8969e-01, 7.0485e-01, 1.5651e-01],
[2.8540e-01, -5.1271e-01, -3.3813e-02],
[1.7029e-01, 3.7308e-01, -6.3154e-02],
[1.9433e-01, -7.9255e-02, -5.8674e-02],
[-1.4680e-01, 9.0324e-01, 2.0574e-02],
[2.8562e-01, 3.2440e-02, -3.5898e-02],
[-1.7669e-01, -4.3748e-02, -1.5858e-01],
[-1.8106e-02, 8.9270e-01, -9.8274e-02],
[-7.3858e-01, -4.9120e-01, -2.6034e-03],
[1.3995e-01, 4.0296e-01, -3.2432e-01],
[2.8693e-01, 5.7881e-01, -2.8429e-02],
[8.4418e-02, 8.9270e-01, -1.0944e-01],
[-2.2409e-01, 5.8757e-01, -2.3634e-01],
[2.8154e-01, 2.2791e-01, -1.6294e-01],
[-6.3224e-01, -4.8892e-01, 1.9713e-01],
[-1.7948e-01, -5.6751e-01, 2.5630e-01],
[-6.8280e-02, 2.8193e-01, -1.6271e-01],
[8.6932e-02, -4.6200e-01, 1.9654e-02],
[2.4729e-01, 7.7544e-01, -3.2432e-01],
[1.6294e-01, 7.2947e-01, -6.0069e-02],
[1.6623e-01, 7.0351e-01, -3.9932e-03],
[2.2792e-02, 4.4052e-01, -1.6035e-01],
[2.1738e-01, -5.9662e-01, -2.0707e-01],
[1.7814e-01, -4.9335e-01, 2.0165e-01],
[-4.8512e-01, -5.7955e-01, -1.0108e-01],
[-3.2409e-01, -1.8065e-01, -1.7123e-01],
[2.8822e-01, -4.3429e-01, -7.2851e-03],
[-1.1018e-01, -2.6267e-01, -1.6717e-01],
[2.0082e-01, 4.2539e-01, 1.4322e-01],
[1.6313e-01, 1.2146e-01, -7.6836e-02],
[-4.6902e-03, -5.6606e-01, 2.5757e-01],
[2.5186e-02, -9.2425e-01, -1.2439e-01],
[9.0190e-02, -3.8543e-01, -3.0639e-02],
[3.0500e-01, 4.9113e-01, 1.5575e-01],
[-4.7773e-01, -1.7712e-01, -1.2046e-01],
[-3.5994e-01, -3.8259e-01, -3.2411e-02],
[-4.2129e-01, -6.2995e-01, 6.4865e-02],
[-4.3695e-01, -7.5720e-01, -1.3847e-01],
[3.0692e-01, -4.3793e-02, 2.0492e-01],
[2.2872e-01, -6.3545e-01, -3.0639e-02],
[-2.0786e-01, 2.5038e-01, -3.2411e-02],
[-4.8664e-01, 4.0222e-01, -1.0370e-01],
[-1.9203e-01, -3.7129e-01, -1.2439e-01],
[4.0446e-01, -2.8067e-01, -1.0378e-01],
[2.1059e-01, 9.2508e-01, -1.2439e-01],
[-1.9723e-01, 2.4433e-01, -3.0639e-02],
[-3.1944e-01, -1.3357e-01, 6.4865e-02],
[-2.6128e-01, -2.9865e-02, 6.4865e-02],
[-2.6348e-01, 7.5135e-01, -1.2439e-01],
[-2.8745e-01, 9.4139e-02, -1.2439e-01],
[2.8162e-01, -1.0000e+00, 1.2521e-01],
[1.8000e-01, -1.1031e-01, 6.8989e-02],
[-9.7091e-02, 8.2881e-01, 1.6214e-01],
[2.4762e-01, -7.0979e-02, -3.0639e-02],
[3.1566e-01, -9.9768e-02, 2.9613e-01],
[2.4752e-01, 5.3690e-01, -1.2439e-01],
[-3.8513e-01, 2.4669e-01, 6.4865e-02],
[2.2998e-01, -4.9642e-02, -1.2439e-01],
[-4.5175e-01, -3.2219e-01, 6.3611e-02],
[7.1355e-02, -6.7209e-01, 6.3499e-02],
[3.7264e-01, 7.5637e-01, -1.2439e-01],
[-3.5348e-01, 9.7893e-01, 1.4849e-01],
[1.0323e-01, 5.5731e-01, 6.4865e-02],
[1.8360e-01, -9.0216e-01, 1.6214e-01],
[2.7071e-01, -6.9052e-01, -1.2439e-01],
[4.0446e-01, -3.9623e-02, -7.8365e-02],
[2.8596e-01, -1.0000e+00, -7.9833e-02],
[4.4756e-02, 4.8919e-01, -1.2439e-01],
[3.0237e-01, 2.1532e-01, 1.2105e-01],
[2.8567e-01, 2.1856e-01, -2.0426e-02],
[4.0446e-01, 2.3101e-01, 1.0086e-01],
[-3.4453e-01, 4.4406e-01, -3.0639e-02],
[-4.8664e-01, 6.1598e-01, -8.0291e-02],
[2.6624e-01, -4.0841e-01, -2.9835e-02],
[3.1751e-01, -3.5890e-01, 3.2058e-01],
[4.0446e-01, -7.6102e-01, -5.2483e-02],
[-1.7093e-01, -6.3454e-01, -1.2439e-01],
[-1.1814e-01, 2.7095e-01, -1.2439e-01],
[7.5540e-02, -9.8103e-01, -1.2383e-01],
[4.0041e-01, -1.4177e-01, 1.5437e-01],
[1.0351e-01, 3.8102e-01, -1.2439e-01],
[3.0761e-01, -2.0948e-01, 1.9012e-01],
[1.8582e-01, 4.5887e-01, 6.8633e-02],
[2.4285e-01, 1.7587e-01, -1.2439e-01],
[3.0026e-01, 6.6768e-01, 9.3234e-02],
[2.8018e-01, -2.8312e-01, 6.7638e-02],
[4.0413e-01, 6.2224e-01, 1.4709e-01],
[2.1721e-01, -2.8875e-01, -3.2411e-02],
[2.9549e-01, -1.9357e-01, 1.1317e-01],
[4.3894e-02, 6.3914e-01, -3.0639e-02],
[-4.3525e-01, 7.3082e-01, 1.3111e-01],
[1.9329e-01, 7.3155e-01, 2.6939e-01],
[3.0241e-01, 1.3610e-02, 2.0000e-01],
[-4.8255e-01, 6.7159e-01, -1.1665e-01],
[-4.0376e-01, 5.2112e-01, -1.2439e-01],
[-2.1529e-01, -9.0250e-01, -1.8576e-01],
[1.3653e-01, 6.0331e-01, -1.4182e-02],
[-5.1030e-01, 5.2375e-01, -1.4160e-01],
[9.3857e-02, 8.5117e-01, -1.8576e-01],
[1.7257e-01, -7.1580e-01, 1.2117e-01],
[-2.9819e-02, 8.6545e-01, -1.8576e-01],
[-2.9166e-01, -8.3588e-01, 6.6004e-02],
[1.7725e-01, 2.2532e-01, 2.6537e-01],
[2.3682e-01, 4.0249e-01, -1.5797e-01],
[-5.1030e-01, 3.2232e-01, -1.4140e-01],
[2.1317e-01, 5.9061e-01, -1.4182e-02],
[-5.1030e-01, -3.0540e-01, -1.0576e-01],
[-4.2774e-01, 1.0000e+00, 7.8977e-02],
[8.6148e-02, 9.2760e-01, -1.4182e-02],
[-4.1586e-01, -8.1449e-01, 1.6100e-01],
[1.8051e-01, 7.4713e-01, 9.9315e-02],
[-4.4974e-01, 1.5543e-01, -1.8576e-01],
[1.8339e-01, -2.2648e-01, 1.6155e-01],
[-1.8434e-01, -7.9208e-01, 8.9535e-02],
[2.3367e-01, -9.2556e-01, -1.8576e-01],
[-3.6223e-01, 8.6446e-01, -1.8547e-01],
[-7.7763e-02, -8.4014e-01, 8.9535e-02],
[8.6664e-02, -2.2030e-02, -1.8576e-01],
[1.3196e-01, 4.9885e-01, 9.6345e-02],
[-3.9771e-01, 3.9167e-01, -1.4182e-02],
[-3.3379e-01, -2.4647e-01, -1.4182e-02],
[6.3328e-02, -5.9357e-01, -1.8576e-01],
[-4.2640e-01, -7.4439e-01, -1.4182e-02],
[2.3682e-01, -1.8300e-01, -9.5117e-02],
[2.2544e-01, -6.9952e-01, 3.1850e-01],
[4.8674e-02, 4.2719e-01, -1.8576e-01],
[1.7041e-01, -5.5259e-03, 2.3983e-01],
[1.0906e-01, -6.9281e-01, -1.8576e-01],
[1.7104e-01, -8.0757e-02, 2.4217e-01],
[1.7095e-01, 5.1883e-03, 1.3790e-01],
[-4.1693e-01, -9.9418e-01, 1.2441e-01],
[1.3677e-01, 6.2831e-01, 1.1430e-01],
[1.7208e-01, -5.1367e-01, 1.1934e-01],
[1.2066e-01, -6.9953e-01, 3.1373e-02],
[2.3682e-01, 5.1519e-01, -9.3649e-02],
[1.7667e-01, 6.2188e-01, 2.6320e-01],
[-2.3054e-01, 9.4740e-01, -1.8576e-01],
[-2.9772e-01, 7.9202e-01, 1.1509e-01],
[-4.8740e-01, 8.6416e-01, -2.7967e-01],
[2.3682e-01, 9.7401e-02, -1.1460e-01],
[-3.7717e-01, -4.9797e-01, -1.8576e-01],
[-1.4868e-01, 4.9006e-02, -1.8576e-01],
[-3.6907e-01, 2.5399e-01, -1.4182e-02],
[2.3682e-01, -7.3611e-01, -4.9100e-02],
[1.5108e-01, -7.0813e-01, 1.6769e-01],
[-5.1030e-01, 6.0329e-02, -1.3040e-01],
[1.3562e-01, -8.8510e-01, 8.9535e-02],
[-5.0098e-01, -9.4882e-01, -1.8576e-01],
[2.2378e-02, 5.1602e-01, -1.8576e-01],
[-4.4093e-01, -3.7677e-01, -1.4182e-02],
[-3.3312e-02, -8.2281e-01, 1.6100e-01],
[2.3682e-01, -3.7022e-01, -8.0912e-02],
[-4.1024e-01, 1.6750e-01, -1.8576e-01],
[-5.1030e-01, 6.2264e-01, -1.2082e-01],
[4.7247e-02, -3.5037e-01, -1.4182e-02],
[1.3448e-01, 1.6774e-03, 6.1691e-02],
[2.0452e-01, 3.8128e-01, 3.6715e-01],
[2.3765e-01, 6.1790e-01, 3.6407e-01],
[1.0179e-01, 9.6686e-01, -1.4182e-02],
[-1.4317e-01, -8.2904e-01, 1.3263e-01],
[-3.8616e-01, -8.9195e-01, -5.1920e-02],
[3.3982e-01, -6.1857e-01, 1.3609e-01],
[-9.7382e-02, 1.0669e-01, 1.2561e-01],
[-5.1578e-02, -1.8835e-01, -5.8711e-02],
[-2.7611e-01, 3.1850e-01, 1.4525e-01],
[1.1082e-01, 5.5939e-01, 1.3517e-01],
[-3.9811e-01, 1.9702e-01, 8.2159e-02],
[-4.2162e-01, 1.2716e-01, -5.2557e-02],
[-4.1794e-01, -4.3431e-01, 3.1557e-02],
[-2.3018e-01, 5.3723e-01, -5.8711e-02],
[3.0747e-01, 4.8079e-01, 1.4079e-01],
[-4.0380e-01, 6.2203e-01, 1.2976e-02],
[1.3282e-01, 7.1973e-01, 2.1321e-01],
[-4.1427e-01, 2.9916e-01, 3.2114e-02],
[-3.3050e-02, -7.6029e-01, 1.3022e-01],
[-5.6017e-02, 2.1223e-01, 1.2740e-01],
[-1.7645e-01, 3.6704e-02, 1.1948e-01],
[-3.3695e-01, 6.4689e-01, -5.8711e-02],
[2.5100e-01, -6.9047e-01, 1.2395e-01],
[6.1637e-02, -7.8208e-01, 1.3098e-01],
[1.0924e-01, -3.4489e-01, 1.1948e-01],
[-4.2138e-01, -2.7649e-01, 3.0480e-02],
[2.8935e-01, 3.2112e-01, -5.8711e-02],
[4.9335e-02, 1.0779e-01, 1.1948e-01],
[-4.1153e-01, -9.8230e-01, -1.9865e-01],
[-1.4141e-01, 2.7516e-01, 1.1948e-01],
[-2.2923e-01, 5.5029e-01, 1.3529e-01],
[-8.3756e-02, 6.7739e-01, 1.4031e-01],
[-1.0300e-01, 9.8000e-01, 2.3907e-02],
[9.6010e-02, 2.0017e-01, 1.4851e-01],
[2.9399e-01, 1.3362e-01, 1.2795e-01],
[-7.6118e-02, 4.6750e-01, 1.4116e-01],
[2.1055e-01, -1.2122e-01, 1.5125e-01],
[-4.0380e-01, -8.6360e-01, 1.2610e-02],
[3.1182e-01, -3.0756e-01, 1.1948e-01],
[-1.2531e-01, -6.6049e-01, 1.5181e-01],
[3.9872e-01, 8.2265e-01, 3.9365e-02],
[4.1678e-01, 2.0115e-01, -6.8050e-02],
[-2.4971e-01, 4.1474e-01, 1.4261e-01],
[-1.2480e-01, -4.5028e-01, 1.1985e-01],
[3.5515e-01, 3.4642e-01, 1.4046e-01],
[3.5099e-01, -3.7661e-01, -5.8711e-02],
[-3.8801e-01, 7.2518e-01, -5.0083e-02],
[3.8532e-01, -2.5975e-01, 1.0816e-01],
[-1.0815e-01, -5.9211e-01, 1.5576e-01],
[3.9896e-01, 5.9310e-01, -5.2468e-02],
[-3.7451e-01, -4.4229e-01, 1.1878e-01],
[1.4604e-01, -4.7458e-01, 1.5354e-01],
[1.8387e-01, -5.1880e-01, 1.2019e-01],
[-5.5425e-02, 3.0991e-01, 1.1948e-01],
[3.2365e-01, 1.4492e-01, 1.4381e-01],
[3.9883e-01, 5.3333e-01, 2.5506e-02],
[-1.2786e-01, -1.6478e-01, -5.8711e-02],
[2.2632e-01, -6.4876e-01, 1.2236e-01],
[2.6546e-01, -8.1790e-01, 1.3022e-01],
[-4.0153e-01, -8.1647e-01, 6.2641e-02],
[2.2915e-01, -9.4253e-04, -5.8711e-02],
[-2.6010e-01, 1.3121e-01, 1.5039e-01],
[9.4847e-02, -3.8382e-01, 1.5446e-01],
[-1.3159e-01, 8.5891e-01, -5.8711e-02],
[3.1891e-01, -4.4107e-01, 1.4460e-01],
[-1.2279e-01, 1.7300e-01, 1.4925e-01],
[-4.0297e-01, -1.2408e-01, 1.1571e-02]], dtype, device)
batch = tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], torch.long, device)
row_02, col_02 = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
edges_02 = set([(i,j) for (i,j) in zip(row_02.cpu().numpy(),col_02.cpu().numpy())])
truth_row_02 = [6, 27, 17, 31, 3, 23, 62, 2, 14, 23, 36, 38, 62, 15, 0, 11, 27, 29, 50, 49, 54, 56, 12, 61, 16, 21, 24, 39, 6, 27, 29, 34, 50, 9, 33, 43, 41, 57, 3, 17, 22, 23, 31, 36, 38, 5, 58, 10, 1, 14, 31, 36, 38, 43, 52, 53, 57, 10, 24, 39, 60, 14, 26, 31, 2, 3, 14, 36, 38, 62, 10, 21, 39, 60, 45, 22, 31, 0, 6, 11, 29, 50, 55, 6, 11, 27, 34, 50, 55, 59, 1, 14, 17, 22, 26, 36, 38, 12, 53, 11, 29, 59, 54, 3, 14, 17, 23, 32, 38, 40, 3, 14, 17, 23, 32, 36, 62, 10, 21, 24, 37, 44, 13, 12, 19, 52, 53, 40, 52, 25, 62, 63, 7, 54, 6, 11, 27, 29, 19, 43, 44, 53, 19, 33, 43, 52, 7, 35, 49, 27, 29, 8, 13, 20, 15, 29, 34, 21, 24, 9, 2, 3, 23, 38, 46, 48, 111, 106, 120, 115, 117, 119, 75, 91, 84, 87, 112, 114, 121, 125, 92, 97, 78, 82, 110, 104, 127, 68, 73, 82, 110, 80, 79, 73, 78, 96, 71, 86, 118, 121, 84, 90, 98, 121, 71, 112, 114, 125, 86, 98, 68, 72, 115, 122, 83, 72, 108, 86, 90, 113, 122, 102, 103, 101, 103, 116, 101, 102, 74, 127, 126, 127, 65, 118, 120, 114, 97, 73, 78, 64, 71, 87, 114, 121, 125, 100, 71, 87, 107, 112, 118, 121, 66, 95, 102, 66, 119, 84, 106, 114, 120, 121, 66, 117, 65, 106, 118, 121, 71, 84, 86, 112, 114, 118, 120, 95, 100, 71, 87, 112, 105, 127, 74, 104, 105, 126, 138, 143, 151, 164, 167, 186, 133, 141, 166, 176, 177, 179, 131, 142, 146, 155, 189, 158, 167, 144, 152, 185, 129, 143, 151, 164, 167, 182, 131, 191, 134, 155, 163, 129, 138, 164, 137, 178, 185, 161, 134, 149, 183, 169, 171, 146, 183, 129, 138, 164, 137, 175, 182, 160, 134, 142, 184, 177, 136, 181, 161, 162, 188, 154, 176, 145, 159, 162, 159, 161, 188, 142, 129, 138, 143, 151, 168, 132, 176, 177, 179, 129, 136, 138, 164, 190, 148, 148, 152, 185, 132, 160, 166, 132, 157, 166, 179, 144, 185, 132, 166, 177, 183, 158, 139, 153, 146, 149, 179, 156, 137, 144, 175, 178, 130, 159, 162, 135, 168, 141, 207, 228, 217, 226, 248, 211, 241, 246, 253, 208, 209, 216, 218, 250, 254, 245, 199, 206, 218, 231, 250, 205, 197, 200, 206, 250, 199, 206, 214, 239, 210, 219, 233, 244, 210, 235, 198, 197, 199, 200, 192, 212, 228, 237, 195, 216, 218, 222, 242, 254, 195, 250, 254, 202, 204, 235, 194, 241, 246, 247, 207, 240, 241, 251, 201, 239, 255, 230, 195, 208, 222, 254, 193, 195, 197, 208, 231, 242, 250, 254, 202, 220, 224, 231, 219, 252, 208, 216, 242, 243, 219, 231, 242, 193, 248, 234, 236, 253, 192, 207, 237, 215, 197, 218, 219, 224, 237, 203, 227, 204, 210, 227, 253, 207, 228, 232, 244, 201, 214, 213, 241, 246, 251, 253, 194, 211, 213, 240, 246, 251, 253, 208, 218, 222, 224, 254, 223, 203, 238, 196, 194, 211, 240, 241, 247, 211, 246, 193, 226, 195, 197, 199, 209, 218, 254, 213, 240, 241, 221, 194, 227, 236, 240, 241, 195, 208, 209, 216, 218, 242, 250, 214]
truth_col_02 = [0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 5, 6, 6, 6, 6, 6, 7, 7, 8, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 16, 17, 17, 17, 17, 17, 19, 19, 19, 20, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 25, 26, 26, 27, 27, 27, 27, 27, 27, 29, 29, 29, 29, 29, 29, 29, 31, 31, 31, 31, 31, 32, 32, 33, 33, 34, 34, 34, 35, 36, 36, 36, 36, 36, 36, 37, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 40, 40, 41, 43, 43, 43, 43, 44, 44, 45, 46, 48, 49, 49, 50, 50, 50, 50, 52, 52, 52, 52, 53, 53, 53, 53, 54, 54, 54, 55, 55, 56, 57, 57, 58, 59, 59, 60, 60, 61, 62, 62, 62, 62, 62, 63, 64, 65, 65, 66, 66, 66, 68, 68, 71, 71, 71, 71, 71, 71, 72, 72, 73, 73, 73, 74, 74, 75, 78, 78, 78, 79, 80, 82, 82, 83, 84, 84, 84, 84, 86, 86, 86, 86, 87, 87, 87, 87, 90, 90, 91, 92, 95, 95, 96, 97, 97, 98, 98, 100, 100, 101, 101, 102, 102, 102, 103, 103, 104, 104, 105, 105, 106, 106, 106, 107, 108, 110, 110, 111, 112, 112, 112, 112, 112, 113, 114, 114, 114, 114, 114, 114, 115, 115, 116, 117, 117, 118, 118, 118, 118, 118, 119, 119, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 121, 122, 122, 125, 125, 125, 126, 126, 127, 127, 127, 127, 129, 129, 129, 129, 129, 130, 131, 131, 132, 132, 132, 132, 133, 134, 134, 134, 135, 136, 136, 137, 137, 137, 138, 138, 138, 138, 138, 139, 141, 141, 142, 142, 142, 143, 143, 143, 144, 144, 144, 145, 146, 146, 146, 148, 148, 149, 149, 151, 151, 151, 152, 152, 153, 154, 155, 155, 156, 157, 158, 158, 159, 159, 159, 160, 160, 161, 161, 161, 162, 162, 162, 163, 164, 164, 164, 164, 164, 166, 166, 166, 166, 167, 167, 167, 168, 168, 169, 171, 175, 175, 176, 176, 176, 177, 177, 177, 177, 178, 178, 179, 179, 179, 179, 181, 182, 182, 183, 183, 183, 184, 185, 185, 185, 185, 186, 188, 188, 189, 190, 191, 192, 192, 193, 193, 193, 194, 194, 194, 194, 195, 195, 195, 195, 195, 195, 196, 197, 197, 197, 197, 197, 198, 199, 199, 199, 199, 200, 200, 201, 201, 202, 202, 203, 203, 204, 204, 205, 206, 206, 206, 207, 207, 207, 207, 208, 208, 208, 208, 208, 208, 209, 209, 209, 210, 210, 210, 211, 211, 211, 211, 212, 213, 213, 213, 214, 214, 214, 215, 216, 216, 216, 216, 217, 218, 218, 218, 218, 218, 218, 218, 219, 219, 219, 219, 220, 221, 222, 222, 222, 223, 224, 224, 224, 226, 226, 227, 227, 227, 228, 228, 228, 230, 231, 231, 231, 231, 232, 233, 234, 235, 235, 236, 236, 237, 237, 237, 238, 239, 239, 240, 240, 240, 240, 240, 241, 241, 241, 241, 241, 241, 241, 242, 242, 242, 242, 242, 243, 244, 244, 245, 246, 246, 246, 246, 246, 247, 247, 248, 248, 250, 250, 250, 250, 250, 250, 251, 251, 251, 252, 253, 253, 253, 253, 253, 254, 254, 254, 254, 254, 254, 254, 255]
truth_02 = set([(i,j) for (i,j) in zip(truth_row_02, truth_col_02)])
assert(truth_02==edges_02)
[3.0804e-01, -1.8622e-01, 1.9274e-01],
[1.9475e-02, 1.4221e-01, -2.7513e-02],
[1.2231e-01, 1.8757e-02, -3.0827e-02],
[2.8174e-01, 9.4371e-01, -3.7868e-02],
[-3.3236e-01, -4.4852e-01, -1.6271e-01],
[2.4958e-02, -5.8395e-01, -4.7551e-02],
[-7.8186e-02, 4.2777e-01, -1.6271e-01],
[3.3085e-01, -4.6802e-01, 2.0720e-01],
[2.0581e-01, 4.4242e-01, 2.3870e-01],
[-3.0456e-02, -7.2156e-02, -2.7691e-01],
[1.2031e-01, -5.8759e-01, 3.2058e-02],
[3.0601e-01, 5.9611e-01, 1.6639e-01],
[-6.4328e-01, -5.1151e-01, -1.4634e-01],
[1.7828e-01, -4.9254e-02, 3.5978e-02],
[-2.9514e-01, -3.5513e-01, -1.6588e-01],
[6.1963e-02, -3.9553e-02, -3.2432e-01],
[2.9479e-01, -1.2834e-01, 5.4388e-02],
[-7.4315e-02, 9.8307e-01, -3.2098e-01],
[3.0474e-01, 7.0636e-01, -1.1068e-01],
[-4.8368e-01, -5.9097e-01, 3.7825e-02],
[-1.2121e-01, -1.4650e-01, -1.7073e-01],
[1.9211e-01, 7.6821e-02, 1.8061e-01],
[1.6331e-01, 6.9012e-02, -2.8236e-02],
[-1.1057e-01, -1.5881e-01, -1.7098e-01],
[-2.3803e-01, 7.0286e-01, -1.3979e-01],
[3.1751e-01, 1.7584e-02, 2.0985e-01],
[6.0034e-02, -5.7922e-01, -9.1098e-02],
[-6.8475e-01, -9.7682e-02, -3.2432e-01],
[1.6659e-01, -4.9622e-01, -5.1318e-02],
[-3.2701e-01, -4.7730e-01, 1.2881e-01],
[2.1012e-01, -8.7334e-02, 1.7803e-01],
[2.7983e-01, -3.7054e-02, -1.5182e-01],
[1.8969e-01, 7.0485e-01, 1.5651e-01],
[2.8540e-01, -5.1271e-01, -3.3813e-02],
[1.7029e-01, 3.7308e-01, -6.3154e-02],
[1.9433e-01, -7.9255e-02, -5.8674e-02],
[-1.4680e-01, 9.0324e-01, 2.0574e-02],
[2.8562e-01, 3.2440e-02, -3.5898e-02],
[-1.7669e-01, -4.3748e-02, -1.5858e-01],
[-1.8106e-02, 8.9270e-01, -9.8274e-02],
[-7.3858e-01, -4.9120e-01, -2.6034e-03],
[1.3995e-01, 4.0296e-01, -3.2432e-01],
[2.8693e-01, 5.7881e-01, -2.8429e-02],
[8.4418e-02, 8.9270e-01, -1.0944e-01],
[-2.2409e-01, 5.8757e-01, -2.3634e-01],
[2.8154e-01, 2.2791e-01, -1.6294e-01],
[-6.3224e-01, -4.8892e-01, 1.9713e-01],
[-1.7948e-01, -5.6751e-01, 2.5630e-01],
[-6.8280e-02, 2.8193e-01, -1.6271e-01],
[8.6932e-02, -4.6200e-01, 1.9654e-02],
[2.4729e-01, 7.7544e-01, -3.2432e-01],
[1.6294e-01, 7.2947e-01, -6.0069e-02],
[1.6623e-01, 7.0351e-01, -3.9932e-03],
[2.2792e-02, 4.4052e-01, -1.6035e-01],
[2.1738e-01, -5.9662e-01, -2.0707e-01],
[1.7814e-01, -4.9335e-01, 2.0165e-01],
[-4.8512e-01, -5.7955e-01, -1.0108e-01],
[-3.2409e-01, -1.8065e-01, -1.7123e-01],
[2.8822e-01, -4.3429e-01, -7.2851e-03],
[-1.1018e-01, -2.6267e-01, -1.6717e-01],
[2.0082e-01, 4.2539e-01, 1.4322e-01],
[1.6313e-01, 1.2146e-01, -7.6836e-02],
[-4.6902e-03, -5.6606e-01, 2.5757e-01],
[2.5186e-02, -9.2425e-01, -1.2439e-01],
[9.0190e-02, -3.8543e-01, -3.0639e-02],
[3.0500e-01, 4.9113e-01, 1.5575e-01],
[-4.7773e-01, -1.7712e-01, -1.2046e-01],
[-3.5994e-01, -3.8259e-01, -3.2411e-02],
[-4.2129e-01, -6.2995e-01, 6.4865e-02],
[-4.3695e-01, -7.5720e-01, -1.3847e-01],
[3.0692e-01, -4.3793e-02, 2.0492e-01],
[2.2872e-01, -6.3545e-01, -3.0639e-02],
[-2.0786e-01, 2.5038e-01, -3.2411e-02],
[-4.8664e-01, 4.0222e-01, -1.0370e-01],
[-1.9203e-01, -3.7129e-01, -1.2439e-01],
[4.0446e-01, -2.8067e-01, -1.0378e-01],
[2.1059e-01, 9.2508e-01, -1.2439e-01],
[-1.9723e-01, 2.4433e-01, -3.0639e-02],
[-3.1944e-01, -1.3357e-01, 6.4865e-02],
[-2.6128e-01, -2.9865e-02, 6.4865e-02],
[-2.6348e-01, 7.5135e-01, -1.2439e-01],
[-2.8745e-01, 9.4139e-02, -1.2439e-01],
[2.8162e-01, -1.0000e+00, 1.2521e-01],
[1.8000e-01, -1.1031e-01, 6.8989e-02],
[-9.7091e-02, 8.2881e-01, 1.6214e-01],
[2.4762e-01, -7.0979e-02, -3.0639e-02],
[3.1566e-01, -9.9768e-02, 2.9613e-01],
[2.4752e-01, 5.3690e-01, -1.2439e-01],
[-3.8513e-01, 2.4669e-01, 6.4865e-02],
[2.2998e-01, -4.9642e-02, -1.2439e-01],
[-4.5175e-01, -3.2219e-01, 6.3611e-02],
[7.1355e-02, -6.7209e-01, 6.3499e-02],
[3.7264e-01, 7.5637e-01, -1.2439e-01],
[-3.5348e-01, 9.7893e-01, 1.4849e-01],
[1.0323e-01, 5.5731e-01, 6.4865e-02],
[1.8360e-01, -9.0216e-01, 1.6214e-01],
[2.7071e-01, -6.9052e-01, -1.2439e-01],
[4.0446e-01, -3.9623e-02, -7.8365e-02],
[2.8596e-01, -1.0000e+00, -7.9833e-02],
[4.4756e-02, 4.8919e-01, -1.2439e-01],
[3.0237e-01, 2.1532e-01, 1.2105e-01],
[2.8567e-01, 2.1856e-01, -2.0426e-02],
[4.0446e-01, 2.3101e-01, 1.0086e-01],
[-3.4453e-01, 4.4406e-01, -3.0639e-02],
[-4.8664e-01, 6.1598e-01, -8.0291e-02],
[2.6624e-01, -4.0841e-01, -2.9835e-02],
[3.1751e-01, -3.5890e-01, 3.2058e-01],
[4.0446e-01, -7.6102e-01, -5.2483e-02],
[-1.7093e-01, -6.3454e-01, -1.2439e-01],
[-1.1814e-01, 2.7095e-01, -1.2439e-01],
[7.5540e-02, -9.8103e-01, -1.2383e-01],
[4.0041e-01, -1.4177e-01, 1.5437e-01],
[1.0351e-01, 3.8102e-01, -1.2439e-01],
[3.0761e-01, -2.0948e-01, 1.9012e-01],
[1.8582e-01, 4.5887e-01, 6.8633e-02],
[2.4285e-01, 1.7587e-01, -1.2439e-01],
[3.0026e-01, 6.6768e-01, 9.3234e-02],
[2.8018e-01, -2.8312e-01, 6.7638e-02],
[4.0413e-01, 6.2224e-01, 1.4709e-01],
[2.1721e-01, -2.8875e-01, -3.2411e-02],
[2.9549e-01, -1.9357e-01, 1.1317e-01],
[4.3894e-02, 6.3914e-01, -3.0639e-02],
[-4.3525e-01, 7.3082e-01, 1.3111e-01],
[1.9329e-01, 7.3155e-01, 2.6939e-01],
[3.0241e-01, 1.3610e-02, 2.0000e-01],
[-4.8255e-01, 6.7159e-01, -1.1665e-01],
[-4.0376e-01, 5.2112e-01, -1.2439e-01],
[-2.1529e-01, -9.0250e-01, -1.8576e-01],
[1.3653e-01, 6.0331e-01, -1.4182e-02],
[-5.1030e-01, 5.2375e-01, -1.4160e-01],
[9.3857e-02, 8.5117e-01, -1.8576e-01],
[1.7257e-01, -7.1580e-01, 1.2117e-01],
[-2.9819e-02, 8.6545e-01, -1.8576e-01],
[-2.9166e-01, -8.3588e-01, 6.6004e-02],
[1.7725e-01, 2.2532e-01, 2.6537e-01],
[2.3682e-01, 4.0249e-01, -1.5797e-01],
[-5.1030e-01, 3.2232e-01, -1.4140e-01],
[2.1317e-01, 5.9061e-01, -1.4182e-02],
[-5.1030e-01, -3.0540e-01, -1.0576e-01],
[-4.2774e-01, 1.0000e+00, 7.8977e-02],
[8.6148e-02, 9.2760e-01, -1.4182e-02],
[-4.1586e-01, -8.1449e-01, 1.6100e-01],
[1.8051e-01, 7.4713e-01, 9.9315e-02],
[-4.4974e-01, 1.5543e-01, -1.8576e-01],
[1.8339e-01, -2.2648e-01, 1.6155e-01],
[-1.8434e-01, -7.9208e-01, 8.9535e-02],
[2.3367e-01, -9.2556e-01, -1.8576e-01],
[-3.6223e-01, 8.6446e-01, -1.8547e-01],
[-7.7763e-02, -8.4014e-01, 8.9535e-02],
[8.6664e-02, -2.2030e-02, -1.8576e-01],
[1.3196e-01, 4.9885e-01, 9.6345e-02],
[-3.9771e-01, 3.9167e-01, -1.4182e-02],
[-3.3379e-01, -2.4647e-01, -1.4182e-02],
[6.3328e-02, -5.9357e-01, -1.8576e-01],
[-4.2640e-01, -7.4439e-01, -1.4182e-02],
[2.3682e-01, -1.8300e-01, -9.5117e-02],
[2.2544e-01, -6.9952e-01, 3.1850e-01],
[4.8674e-02, 4.2719e-01, -1.8576e-01],
[1.7041e-01, -5.5259e-03, 2.3983e-01],
[1.0906e-01, -6.9281e-01, -1.8576e-01],
[1.7104e-01, -8.0757e-02, 2.4217e-01],
[1.7095e-01, 5.1883e-03, 1.3790e-01],
[-4.1693e-01, -9.9418e-01, 1.2441e-01],
[1.3677e-01, 6.2831e-01, 1.1430e-01],
[1.7208e-01, -5.1367e-01, 1.1934e-01],
[1.2066e-01, -6.9953e-01, 3.1373e-02],
[2.3682e-01, 5.1519e-01, -9.3649e-02],
[1.7667e-01, 6.2188e-01, 2.6320e-01],
[-2.3054e-01, 9.4740e-01, -1.8576e-01],
[-2.9772e-01, 7.9202e-01, 1.1509e-01],
[-4.8740e-01, 8.6416e-01, -2.7967e-01],
[2.3682e-01, 9.7401e-02, -1.1460e-01],
[-3.7717e-01, -4.9797e-01, -1.8576e-01],
[-1.4868e-01, 4.9006e-02, -1.8576e-01],
[-3.6907e-01, 2.5399e-01, -1.4182e-02],
[2.3682e-01, -7.3611e-01, -4.9100e-02],
[1.5108e-01, -7.0813e-01, 1.6769e-01],
[-5.1030e-01, 6.0329e-02, -1.3040e-01],
[1.3562e-01, -8.8510e-01, 8.9535e-02],
[-5.0098e-01, -9.4882e-01, -1.8576e-01],
[2.2378e-02, 5.1602e-01, -1.8576e-01],
[-4.4093e-01, -3.7677e-01, -1.4182e-02],
[-3.3312e-02, -8.2281e-01, 1.6100e-01],
[2.3682e-01, -3.7022e-01, -8.0912e-02],
[-4.1024e-01, 1.6750e-01, -1.8576e-01],
[-5.1030e-01, 6.2264e-01, -1.2082e-01],
[4.7247e-02, -3.5037e-01, -1.4182e-02],
[1.3448e-01, 1.6774e-03, 6.1691e-02],
[2.0452e-01, 3.8128e-01, 3.6715e-01],
[2.3765e-01, 6.1790e-01, 3.6407e-01],
[1.0179e-01, 9.6686e-01, -1.4182e-02],
[-1.4317e-01, -8.2904e-01, 1.3263e-01],
[-3.8616e-01, -8.9195e-01, -5.1920e-02],
[3.3982e-01, -6.1857e-01, 1.3609e-01],
[-9.7382e-02, 1.0669e-01, 1.2561e-01],
[-5.1578e-02, -1.8835e-01, -5.8711e-02],
[-2.7611e-01, 3.1850e-01, 1.4525e-01],
[1.1082e-01, 5.5939e-01, 1.3517e-01],
[-3.9811e-01, 1.9702e-01, 8.2159e-02],
[-4.2162e-01, 1.2716e-01, -5.2557e-02],
[-4.1794e-01, -4.3431e-01, 3.1557e-02],
[-2.3018e-01, 5.3723e-01, -5.8711e-02],
[3.0747e-01, 4.8079e-01, 1.4079e-01],
[-4.0380e-01, 6.2203e-01, 1.2976e-02],
[1.3282e-01, 7.1973e-01, 2.1321e-01],
[-4.1427e-01, 2.9916e-01, 3.2114e-02],
[-3.3050e-02, -7.6029e-01, 1.3022e-01],
[-5.6017e-02, 2.1223e-01, 1.2740e-01],
[-1.7645e-01, 3.6704e-02, 1.1948e-01],
[-3.3695e-01, 6.4689e-01, -5.8711e-02],
[2.5100e-01, -6.9047e-01, 1.2395e-01],
[6.1637e-02, -7.8208e-01, 1.3098e-01],
[1.0924e-01, -3.4489e-01, 1.1948e-01],
[-4.2138e-01, -2.7649e-01, 3.0480e-02],
[2.8935e-01, 3.2112e-01, -5.8711e-02],
[4.9335e-02, 1.0779e-01, 1.1948e-01],
[-4.1153e-01, -9.8230e-01, -1.9865e-01],
[-1.4141e-01, 2.7516e-01, 1.1948e-01],
[-2.2923e-01, 5.5029e-01, 1.3529e-01],
[-8.3756e-02, 6.7739e-01, 1.4031e-01],
[-1.0300e-01, 9.8000e-01, 2.3907e-02],
[9.6010e-02, 2.0017e-01, 1.4851e-01],
[2.9399e-01, 1.3362e-01, 1.2795e-01],
[-7.6118e-02, 4.6750e-01, 1.4116e-01],
[2.1055e-01, -1.2122e-01, 1.5125e-01],
[-4.0380e-01, -8.6360e-01, 1.2610e-02],
[3.1182e-01, -3.0756e-01, 1.1948e-01],
[-1.2531e-01, -6.6049e-01, 1.5181e-01],
[3.9872e-01, 8.2265e-01, 3.9365e-02],
[4.1678e-01, 2.0115e-01, -6.8050e-02],
[-2.4971e-01, 4.1474e-01, 1.4261e-01],
[-1.2480e-01, -4.5028e-01, 1.1985e-01],
[3.5515e-01, 3.4642e-01, 1.4046e-01],
[3.5099e-01, -3.7661e-01, -5.8711e-02],
[-3.8801e-01, 7.2518e-01, -5.0083e-02],
[3.8532e-01, -2.5975e-01, 1.0816e-01],
[-1.0815e-01, -5.9211e-01, 1.5576e-01],
[3.9896e-01, 5.9310e-01, -5.2468e-02],
[-3.7451e-01, -4.4229e-01, 1.1878e-01],
[1.4604e-01, -4.7458e-01, 1.5354e-01],
[1.8387e-01, -5.1880e-01, 1.2019e-01],
[-5.5425e-02, 3.0991e-01, 1.1948e-01],
[3.2365e-01, 1.4492e-01, 1.4381e-01],
[3.9883e-01, 5.3333e-01, 2.5506e-02],
[-1.2786e-01, -1.6478e-01, -5.8711e-02],
[2.2632e-01, -6.4876e-01, 1.2236e-01],
[2.6546e-01, -8.1790e-01, 1.3022e-01],
[-4.0153e-01, -8.1647e-01, 6.2641e-02],
[2.2915e-01, -9.4253e-04, -5.8711e-02],
[-2.6010e-01, 1.3121e-01, 1.5039e-01],
[9.4847e-02, -3.8382e-01, 1.5446e-01],
[-1.3159e-01, 8.5891e-01, -5.8711e-02],
[3.1891e-01, -4.4107e-01, 1.4460e-01],
[-1.2279e-01, 1.7300e-01, 1.4925e-01],
[-4.0297e-01, -1.2408e-01, 1.1571e-02]], dtype, device)
batch = tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3], torch.long, device)
row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
truth_row = [6, 27, 17, 31, 3, 23, 62, 2, 14, 23, 36, 38, 62, 15, 0, 11,
27, 29, 50, 49, 54, 56, 12, 61, 16, 21, 24, 39, 6, 27, 29,
34, 50, 9, 33, 43, 41, 57, 3, 17, 22, 23, 31, 36, 38, 5, 58,
10, 1, 14, 31, 36, 38, 43, 52, 53, 57, 10, 24, 39, 60, 14,
26, 31, 2, 3, 14, 36, 38, 62, 10, 21, 39, 60, 45, 22, 31, 0,
6, 11, 29, 50, 55, 6, 11, 27, 34, 50, 55, 59, 1, 14, 17, 22,
26, 36, 38, 12, 53, 11, 29, 59, 54, 3, 14, 17, 23, 32, 38,
40, 3, 14, 17, 23, 32, 36, 62, 10, 21, 24, 37, 44, 13, 12,
19, 52, 53, 40, 52, 25, 62, 63, 7, 54, 6, 11, 27, 29, 19, 43,
44, 53, 19, 33, 43, 52, 7, 35, 49, 27, 29, 8, 13, 20, 15, 29,
34, 21, 24, 9, 2, 3, 23, 38, 46, 48, 111, 106, 120, 115, 117,
119, 75, 91, 84, 87, 112, 114, 121, 125, 92, 97, 78, 82, 110,
104, 127, 68, 73, 82, 110, 80, 79, 73, 78, 96, 71, 86, 118,
121, 84, 90, 98, 121, 71, 112, 114, 125, 86, 98, 68, 72, 115,
122, 83, 72, 108, 86, 90, 113, 122, 102, 103, 101, 103, 116,
101, 102, 74, 127, 126, 127, 65, 118, 120, 114, 97, 73, 78,
64, 71, 87, 114, 121, 125, 100, 71, 87, 107, 112, 118, 121,
66, 95, 102, 66, 119, 84, 106, 114, 120, 121, 66, 117, 65,
106, 118, 121, 71, 84, 86, 112, 114, 118, 120, 95, 100, 71,
87, 112, 105, 127, 74, 104, 105, 126, 138, 143, 151, 164, 167,
186, 133, 141, 166, 176, 177, 179, 131, 142, 146, 155, 189,
158, 167, 144, 152, 185, 129, 143, 151, 164, 167, 182, 131,
191, 134, 155, 163, 129, 138, 164, 137, 178, 185, 161, 134,
149, 183, 169, 171, 146, 183, 129, 138, 164, 137, 175, 182,
160, 134, 142, 184, 177, 136, 181, 161, 162, 188, 154, 176,
145, 159, 162, 159, 161, 188, 142, 129, 138, 143, 151, 168,
132, 176, 177, 179, 129, 136, 138, 164, 190, 148, 148, 152,
185, 132, 160, 166, 132, 157, 166, 179, 144, 185, 132, 166,
177, 183, 158, 139, 153, 146, 149, 179, 156, 137, 144, 175,
178, 130, 159, 162, 135, 168, 141, 207, 228, 217, 226, 248,
211, 241, 246, 253, 208, 209, 216, 218, 250, 254, 245, 199,
206, 218, 231, 250, 205, 197, 200, 206, 250, 199, 206, 214,
239, 210, 219, 233, 244, 210, 235, 198, 197, 199, 200, 192,
212, 228, 237, 195, 216, 218, 222, 242, 254, 195, 250, 254,
202, 204, 235, 194, 241, 246, 247, 207, 240, 241, 251, 201,
239, 255, 230, 195, 208, 222, 254, 193, 195, 197, 208, 231,
242, 250, 254, 202, 220, 224, 231, 219, 252, 208, 216, 242,
243, 219, 231, 242, 193, 248, 234, 236, 253, 192, 207, 237,
215, 197, 218, 219, 224, 237, 203, 227, 204, 210, 227, 253,
207, 228, 232, 244, 201, 214, 213, 241, 246, 251, 253, 194,
211, 213, 240, 246, 251, 253, 208, 218, 222, 224, 254, 223,
203, 238, 196, 194, 211, 240, 241, 247, 211, 246, 193, 226,
195, 197, 199, 209, 218, 254, 213, 240, 241, 221, 194, 227,
236, 240, 241, 195, 208, 209, 216, 218, 242, 250, 214]
truth_col = [0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 5, 6, 6, 6, 6, 6, 7,
7, 8, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12,
13, 13, 14, 14, 14, 14, 14, 14, 14, 15, 15, 16, 17, 17, 17,
17, 17, 19, 19, 19, 20, 21, 21, 21, 21, 22, 22, 22, 23, 23,
23, 23, 23, 23, 24, 24, 24, 24, 25, 26, 26, 27, 27, 27,
27, 27, 27, 29, 29, 29, 29, 29, 29, 29, 31, 31, 31, 31,
31, 32, 32, 33, 33, 34, 34, 34, 35, 36, 36, 36, 36, 36, 36,
37, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 40, 40, 41, 43,
43, 43, 43, 44, 44, 45, 46, 48, 49, 49, 50, 50, 50, 50, 52,
52, 52, 52, 53, 53, 53, 53, 54, 54, 54, 55, 55, 56, 57, 57,
58, 59, 59, 60, 60, 61, 62, 62, 62, 62, 62, 63, 64, 65, 65,
66, 66, 66, 68, 68, 71, 71, 71, 71, 71, 71, 72, 72, 73, 73,
73, 74, 74, 75, 78, 78, 78, 79, 80, 82, 82, 83, 84, 84, 84,
84, 86, 86, 86, 86, 87, 87, 87, 87, 90, 90, 91, 92, 95, 95,
96, 97, 97, 98, 98, 100, 100, 101, 101, 102, 102, 102, 103,
103, 104, 104, 105, 105, 106, 106, 106, 107, 108, 110, 110,
111, 112, 112, 112, 112, 112, 113, 114, 114, 114, 114, 114,
114, 115, 115, 116, 117, 117, 118, 118, 118, 118, 118, 119,
119, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 121,
122, 122, 125, 125, 125, 126, 126, 127, 127, 127, 127, 129,
129, 129, 129, 129, 130, 131, 131, 132, 132, 132, 132, 133,
134, 134, 134, 135, 136, 136, 137, 137, 137, 138, 138, 138,
138, 138, 139, 141, 141, 142, 142, 142, 143, 143, 143, 144,
144, 144, 145, 146, 146, 146, 148, 148, 149, 149, 151, 151,
151, 152, 152, 153, 154, 155, 155, 156, 157, 158, 158, 159,
159, 159, 160, 160, 161, 161, 161, 162, 162, 162, 163, 164,
164, 164, 164, 164, 166, 166, 166, 166, 167, 167, 167, 168,
168, 169, 171, 175, 175, 176, 176, 176, 177, 177, 177, 177,
178, 178, 179, 179, 179, 179, 181, 182, 182, 183, 183, 183,
184, 185, 185, 185, 185, 186, 188, 188, 189, 190, 191, 192,
192, 193, 193, 193, 194, 194, 194, 194, 195, 195, 195, 195,
195, 195, 196, 197, 197, 197, 197, 197, 198, 199, 199, 199,
199, 200, 200, 201, 201, 202, 202, 203, 203, 204, 204, 205,
206, 206, 206, 207, 207, 207, 207, 208, 208, 208, 208, 208,
208, 209, 209, 209, 210, 210, 210, 211, 211, 211, 211, 212,
213, 213, 213, 214, 214, 214, 215, 216, 216, 216, 216, 217,
218, 218, 218, 218, 218, 218, 218, 219, 219, 219, 219, 220,
221, 222, 222, 222, 223, 224, 224, 224, 226, 226, 227, 227,
227, 228, 228, 228, 230, 231, 231, 231, 231, 232, 233, 234,
235, 235, 236, 236, 237, 237, 237, 238, 239, 239, 240, 240,
240, 240, 240, 241, 241, 241, 241, 241, 241, 241, 242, 242,
242, 242, 242, 243, 244, 244, 245, 246, 246, 246, 246, 246,
247, 247, 248, 248, 250, 250, 250, 250, 250, 250, 251, 251,
251, 252, 253, 253, 253, 253, 253, 254, 254, 254, 254, 254,
254, 254, 255]
truth = set([(i, j) for (i, j) in zip(truth_row, truth_col)])
assert(truth == edges)
@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
def test_radius_graph_ndim(dtype, device):
x = tensor([[-0.9750, -0.7160, 0.7150, -0.1510, -0.3660, 0.6140, -1.0340, 2.4950],
[ 0.8540, 0.1110, 1.0520, -1.3900, 0.7570, -0.6300, -0.9550, -0.9350],
[ 0.3710, 0.4610, 0.1620, 1.1370, -1.5830, 0.4100, -0.5710, -0.7760],
[ 0.4200, 0.1240, -1.2870, -0.2300, -1.7480, 0.5890, 0.5710, 0.1670],
[-0.6060, 0.8080, -2.2560, 0.4480, -0.8910, 0.2360, -0.0060, -0.6510],
[-0.6960, 0.7190, -0.7330, 0.4660, 0.4400, -0.0490, -1.1350, -0.5990],
[-0.0080, -0.4770, 0.0980, 1.2000, -0.6110, -0.7410, 0.7410, -0.2800],
[-2.5230, -0.8470, -0.8670, 0.4820, -0.9510, -0.9460, 0.3390, -1.6740],
[ 1.0770, -1.4480, 1.8110, 0.0900, 0.7980, 0.4070, 1.9570, -0.2010],
[ 1.0890, -0.2150, -0.4440, 0.4370, 1.1180, -0.4280, -2.3860, 0.5860],
[ 0.1000, -0.2590, -2.1420, 0.9260, 0.7290, -0.1170, 0.9370, -0.0470],
[-0.3870, -1.7310, -0.6020, -0.1070, 1.7890, 0.5200, 1.2620, 0.6130],
[-0.0740, 0.5270, 0.4090, -0.9120, -0.1690, 1.4970, -2.4540, -1.0430],
[-0.9750, -1.3510, 0.0730, 0.1450, -0.9910, -1.8840, 0.1010, 0.4620],
[ 0.6950, 0.3560, 0.2850, -0.1050, -1.8770, 1.4910, 2.0260, -0.8170],
[-1.3480, 0.1100, 0.8460, -0.1050, -1.9670, -0.0930, 0.2820, 1.7150],
[-0.0340, -0.7420, 0.5450, 1.8170, -0.6030, -0.0990, 0.1650, -0.0450],
[ 0.4490, 1.6170, -1.6880, -0.6180, -0.8350, 1.0560, -0.3860, 0.8380],
[ 0.9530, -0.1970, -0.7030, 1.7750, -1.6860, -1.4290, 0.6280, 0.2730],
[ 0.6630, 1.0780, 1.5650, -0.5490, -0.5530, -0.8070, 0.4100, -2.4380],
[ 0.6350, 0.0490, 0.1990, -1.2340, 0.7630, 0.2670, 1.5810, -0.4250],
[ 1.6700, 0.4440, -2.5800, 0.5020, 0.3520, -0.9110, -1.9960, -0.0000],
[ 0.1970, 0.2390, 2.2290, -0.0910, 1.2710, 0.0280, -0.5530, -1.4650],
[ 0.1270, 2.5150, -0.3450, -0.8340, 1.0130, -1.3680, -0.1990, -0.5480],
[-1.0470, 0.0200, 2.2200, 1.7030, 0.5460, 0.4350, -1.8560, -0.9750],
[ 0.7010, -0.7260, -0.2380, 0.6120, 1.1150, -1.2530, -0.2140, 1.0100],
[-0.2590, -0.2690, 0.1200, 1.0380, -0.8370, -0.0070, -0.0800, 0.2130],
[-0.5460, 0.4000, 0.2040, -0.8370, 1.7400, 1.0940, 0.0930, -0.3370],
[-1.0230, 1.5400, 0.9760, -1.5210, 1.0170, -1.3290, 0.7690, 0.6260],
[-0.7560, 0.1360, -0.2640, -0.6130, -0.2830, 0.6830, -0.8700, -0.5610],
[ 0.4060, 0.3830, 2.4530, -0.4910, -1.3110, -0.0980, -0.0630, 0.3200],
[ 0.1450, 0.5810, -0.7310, 0.8190, -1.3600, -0.6780, -0.3360, -0.2570]],
dtype, device)
batch = tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6,
6, 6, 6, 6, 7, 7, 8, 9], torch.long, device)
row_02, col_02 = radius_graph(x, r=4.4, flow='source_to_target', batch=batch)
edges_02 = set([(i,j) for (i,j) in zip(row_02.cpu().numpy(),col_02.cpu().numpy())])
truth_row_02 = [ 2, 3, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3, 6, 7, 9, 10,
5, 7, 8, 9, 10, 5, 6, 10, 6, 5, 6, 10, 5, 6, 7, 9, 13, 11,
16, 17, 18, 15, 17, 18, 15, 16, 18, 15, 16, 17, 20, 22, 19, 22, 19, 20,
25, 26, 27, 26, 27, 23, 26, 27, 23, 24, 25, 27, 23, 24, 25, 26, 29, 28]
truth_col_02 = [ 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5,
6, 6, 6, 6, 6, 7, 7, 7, 8, 9, 9, 9, 10, 10, 10, 10, 11, 13,
15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 20, 20, 22, 22,
23, 23, 23, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 29]
truth_02 = set([(i,j) for (i,j) in zip(truth_row_02, truth_col_02)])
#print(edges_02.symmetric_difference(truth_02))
#print('===========')
#print(edges_02)
#print(truth_02)
assert(truth_02==edges_02)
\ No newline at end of file
x = tensor([[-0.9750, -0.7160, 0.7150, -0.1510, -0.3660, 0.6140, -1.0340,
2.4950],
[0.8540, 0.1110, 1.0520, -1.3900, 0.7570, -0.6300, -0.9550,
-0.9350],
[0.3710, 0.4610, 0.1620, 1.1370, -1.5830, 0.4100, -0.5710,
-0.7760],
[0.4200, 0.1240, -1.2870, -0.2300, -1.7480, 0.5890, 0.5710,
0.1670],
[-0.6060, 0.8080, -2.2560, 0.4480, -0.8910, 0.2360, -0.0060,
-0.6510],
[-0.6960, 0.7190, -0.7330, 0.4660, 0.4400, -0.0490, -1.1350,
-0.5990],
[-0.0080, -0.4770, 0.0980, 1.2000, -0.6110, -0.7410, 0.7410,
-0.2800],
[-2.5230, -0.8470, -0.8670, 0.4820, -0.9510, -0.9460, 0.3390,
-1.6740],
[1.0770, -1.4480, 1.8110, 0.0900, 0.7980, 0.4070, 1.9570,
-0.2010],
[1.0890, -0.2150, -0.4440, 0.4370, 1.1180, -0.4280, -2.3860,
0.5860],
[0.1000, -0.2590, -2.1420, 0.9260, 0.7290, -0.1170, 0.9370,
-0.0470],
[-0.3870, -1.7310, -0.6020, -0.1070, 1.7890, 0.5200, 1.2620,
0.6130],
[-0.0740, 0.5270, 0.4090, -0.9120, -0.1690, 1.4970, -2.4540,
-1.0430],
[-0.9750, -1.3510, 0.0730, 0.1450, -0.9910, -1.8840, 0.1010,
0.4620],
[0.6950, 0.3560, 0.2850, -0.1050, -1.8770, 1.4910, 2.0260,
-0.8170],
[-1.3480, 0.1100, 0.8460, -0.1050, -1.9670, -0.0930, 0.2820,
1.7150],
[-0.0340, -0.7420, 0.5450, 1.8170, -0.6030, -0.0990, 0.1650,
-0.0450],
[0.4490, 1.6170, -1.6880, -0.6180, -0.8350, 1.0560, -0.3860,
0.8380],
[0.9530, -0.1970, -0.7030, 1.7750, -1.6860, -1.4290, 0.6280,
0.2730],
[0.6630, 1.0780, 1.5650, -0.5490, -0.5530, -0.8070, 0.4100,
-2.4380],
[0.6350, 0.0490, 0.1990, -1.2340, 0.7630, 0.2670, 1.5810,
-0.4250],
[1.6700, 0.4440, -2.5800, 0.5020, 0.3520, -0.9110, -1.9960,
-0.0000],
[0.1970, 0.2390, 2.2290, -0.0910, 1.2710, 0.0280, -0.5530,
-1.4650],
[0.1270, 2.5150, -0.3450, -0.8340, 1.0130, -1.3680, -0.1990,
-0.5480],
[-1.0470, 0.0200, 2.2200, 1.7030, 0.5460, 0.4350, -1.8560,
-0.9750],
[0.7010, -0.7260, -0.2380, 0.6120, 1.1150, -1.2530, -0.2140,
1.0100],
[-0.2590, -0.2690, 0.1200, 1.0380, -0.8370, -0.0070, -0.0800,
0.2130],
[-0.5460, 0.4000, 0.2040, -0.8370, 1.7400, 1.0940, 0.0930,
-0.3370],
[-1.0230, 1.5400, 0.9760, -1.5210, 1.0170, -1.3290, 0.7690,
0.6260],
[-0.7560, 0.1360, -0.2640, -0.6130, -0.2830, 0.6830, -0.8700,
-0.5610],
[0.4060, 0.3830, 2.4530, -0.4910, -1.3110, -0.0980, -0.0630,
0.3200],
[0.1450, 0.5810, -0.7310, 0.8190, -1.3600, -0.6780, -0.3360,
-0.2570]], dtype, device)
batch = tensor([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5,
5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 8, 9], torch.long, device)
row, col = radius_graph(x, r=4.4, flow='source_to_target', batch=batch)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
truth_row = [2, 3, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3, 6, 7, 9, 10, 5, 7,
8, 9, 10, 5, 6, 10, 6, 5, 6, 10, 5, 6, 7, 9, 13, 11, 16, 17,
18, 15, 17, 18, 15, 16, 18, 15, 16, 17, 20, 22, 19, 22, 19,
20, 25, 26, 27, 26, 27, 23, 26, 27, 23, 24, 25, 27, 23, 24,
25, 26, 29, 28]
truth_col = [0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 6, 6,
6, 6, 6, 7, 7, 7, 8, 9, 9, 9, 10, 10, 10, 10, 11, 13, 15, 15,
15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 20, 20, 22,
22, 23, 23, 23, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27,
27, 27, 28, 29]
truth = set([(i, j) for (i, j) in zip(truth_row, truth_col)])
assert(truth == edges)
torch_cluster/radius.py
View file @ d7f704c5
from typing import Optional
import torch
import scipy
def sample(col, count):
if col.size(0) > count:
col = col[torch.randperm(col.size(0))][:count]
return col
def radius(x: torch.Tensor, y: torch.Tensor, r: float,
batch_x: Optional[torch.Tensor] = None,
......@@ -55,7 +50,7 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
ptr_x = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_x[1:])
else:
ptr_x = None#torch.tensor([0, x.size(0)], device=x.device)
ptr_x = None
if batch_y is not None:
assert y.size(0) == batch_y.numel()
......@@ -66,33 +61,25 @@ def radius(x: torch.Tensor, y: torch.Tensor, r: float,
ptr_y = deg.new_zeros(batch_size + 1)
torch.cumsum(deg, 0, out=ptr_y[1:])
else:
ptr_y = None#torch.tensor([0, y.size(0)], device=y.device)
ptr_y = None
result = torch.ops.torch_cluster.radius(x, y, ptr_x, ptr_y, r,
max_num_neighbors)
else:
#if batch_x is None:
# batch_x = x.new_zeros(x.size(0), dtype=torch.long)
#if batch_y is None:
# batch_y = y.new_zeros(y.size(0), dtype=torch.long)
#batch_x = batch_x.to(x.dtype)
#batch_y = batch_y.to(y.dtype)
assert x.dim() == 2
if batch_x is not None:
if batch_x is not None:
assert batch_x.dim() == 1
assert x.size(0) == batch_x.size(0)
assert y.dim() == 2
if batch_y is not None:
if batch_y is not None:
assert batch_y.dim() == 1
assert y.size(0) == batch_y.size(0)
assert x.size(1) == y.size(1)
result = torch.ops.torch_cluster.radius(x, y, batch_x, batch_y, r,
max_num_neighbors)
max_num_neighbors)
return result
......
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