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Some faster/better kernels. Tensors with points are now kept in (b, c, ...) format as this is easier for pytorch

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This commit is contained in:
erikwijmans
2018-02-10 20:32:52 -05:00
parent 8bce353da4
commit 65a127f3d2
19 changed files with 372 additions and 296 deletions
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models/Pointnet2Cls.py
+16 -13
View File
@@ -40,7 +40,7 @@ def model_fn_decorator(criterion):
class Pointnet2SSG(nn.Module):
def __init__(self, num_classes, input_channels=9):
def __init__(self, num_classes, input_channels=3):
super().__init__()
self.SA_modules = nn.ModuleList()
@@ -54,13 +54,10 @@ class Pointnet2SSG(nn.Module):
)
self.SA_modules.append(
PointnetSAModule(
npoint=128,
radius=0.4,
nsample=64,
mlp=[128 + 3, 128, 128, 256]
npoint=128, radius=0.4, nsample=64, mlp=[128, 128, 128, 256]
)
)
self.SA_modules.append(PointnetSAModule(mlp=[256 + 3, 256, 512, 1024]))
self.SA_modules.append(PointnetSAModule(mlp=[256, 256, 512, 1024]))
self.FC_layer = nn.Sequential(
pt_utils.FC(1024, 512, bn=True),
@@ -71,15 +68,18 @@ class Pointnet2SSG(nn.Module):
)
def forward(self, xyz, points=None):
xyz = xyz.contiguous()
points = points.transpose(1, 2
).contiguous() if points is not None else None
for module in self.SA_modules:
xyz, points = module(xyz, points)
return self.FC_layer(points.squeeze(1))
return self.FC_layer(points.squeeze(-1))
class Pointnet2MSG(nn.Module):
def __init__(self, num_classes, input_channels=9):
def __init__(self, num_classes, input_channels=3):
super().__init__()
self.SA_modules = nn.ModuleList()
@@ -93,7 +93,7 @@ class Pointnet2MSG(nn.Module):
)
)
input_channels = 64 + 128 + 128 + 3
input_channels = 64 + 128 + 128
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=128,
@@ -104,7 +104,7 @@ class Pointnet2MSG(nn.Module):
)
)
self.SA_modules.append(
PointnetSAModule(mlp=[128 + 256 + 256 + 3, 256, 512, 1024])
PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024])
)
self.FC_layer = nn.Sequential(
@@ -116,10 +116,13 @@ class Pointnet2MSG(nn.Module):
)
def forward(self, xyz, points=None):
xyz = xyz.contiguous()
points = points.transpose(1, 2
).contiguous() if points is not None else None
for module in self.SA_modules:
xyz, points = module(xyz, points)
return self.FC_layer(points.squeeze(1))
return self.FC_layer(points.squeeze(-1))
if __name__ == "__main__":
@@ -129,9 +132,9 @@ if __name__ == "__main__":
import torch.autograd.profiler as profiler
B = 2
N = 2048
inputs = torch.randn(B, N, 9).cuda()
inputs = torch.randn(B, N, 6).cuda()
labels = torch.from_numpy(np.random.randint(0, 3, size=B)).cuda()
model = Pointnet2MSG(3)
model = Pointnet2MSG(3, input_channels=3)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=1e-2)
models/Pointnet2SemSeg.py
+16 -30
View File
@@ -42,8 +42,6 @@ class Pointnet2SSG(nn.Module):
def __init__(self, num_classes, input_channels=9):
super().__init__()
self.initial_dropout = RandomDropout(0.4)
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModule(
@@ -83,27 +81,22 @@ class Pointnet2SSG(nn.Module):
)
def forward(self, xyz, points=None):
if points is not None:
tmp = self.initial_dropout(torch.cat([points, xyz], dim=-1))
l0_points, l0_xyz = tmp.split(points.size(-1), dim=-1)
else:
l0_xyz = self.initial_dropout(xyz)
l0_points = None
xyz = xyz.contiguous()
points = points.transpose(1, 2
).contiguous() if points is not None else None
l_xyz, l_points = [l0_xyz], [l0_points]
l_xyz, l_points = [xyz], [points]
for i in range(len(self.SA_modules)):
li_xyz, li_points = self.SA_modules[i](l_xyz[i], l_points[i])
l_xyz.append(li_xyz)
l_points.append(li_points)
for i in range(-1, -(len(self.FP_modules + 1) - 1), -1):
for i in range(-1, -(len(self.FP_modules) + 1), -1):
l_points[i - 1] = self.FP_modules[i](
l_xyz[i - 1], l_xyz[i], l_points[i - 1], l_points[i]
)
return self.FC_layer(l_points[0].transpose(1,
2)).transpose(1,
2).contiguous()
return self.FC_layer(l_points[0]).transpose(1, 2).contiguous()
class Pointnet2MSG(nn.Module):
@@ -111,9 +104,6 @@ class Pointnet2MSG(nn.Module):
def __init__(self, num_classes, input_channels=9):
super().__init__()
self.initial_dropout = RandomDropout(0.95, inplace=True)
self.initial_dropout = None
self.SA_modules = nn.ModuleList()
c_in = input_channels
self.SA_modules.append(
@@ -126,7 +116,7 @@ class Pointnet2MSG(nn.Module):
)
c_out_0 = 32 + 64
c_in = c_out_0 + 3
c_in = c_out_0
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=256,
@@ -137,7 +127,7 @@ class Pointnet2MSG(nn.Module):
)
c_out_1 = 128 + 128
c_in = c_out_1 + 3
c_in = c_out_1
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=64,
@@ -148,7 +138,7 @@ class Pointnet2MSG(nn.Module):
)
c_out_2 = 256 + 256
c_in = c_out_2 + 3
c_in = c_out_2
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=16,
@@ -161,7 +151,7 @@ class Pointnet2MSG(nn.Module):
self.FP_modules = nn.ModuleList()
self.FP_modules.append(
PointnetFPModule(mlp=[256 + input_channels - 3, 128, 128])
PointnetFPModule(mlp=[256 + input_channels, 128, 128])
)
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512]))
@@ -175,11 +165,9 @@ class Pointnet2MSG(nn.Module):
)
def forward(self, xyz, points=None):
if points is not None and self.initial_dropout is not None:
tmp = self.initial_dropout(torch.cat([points, xyz], dim=-1))
points, xyz = tmp.split(points.size(-1), dim=-1)
elif self.initial_dropout is not None:
xyz = self.initial_dropout(xyz)
xyz = xyz.contiguous()
points = points.transpose(1, 2
).contiguous() if points is not None else None
l_xyz, l_points = [xyz], [points]
for i in range(len(self.SA_modules)):
@@ -192,9 +180,7 @@ class Pointnet2MSG(nn.Module):
l_xyz[i - 1], l_xyz[i], l_points[i - 1], l_points[i]
)
return self.FC_layer(l_points[0].transpose(1,
2)).transpose(1,
2).contiguous()
return self.FC_layer(l_points[0]).transpose(1, 2).contiguous()
if __name__ == "__main__":
@@ -203,10 +189,10 @@ if __name__ == "__main__":
import torch.optim as optim
B = 2
N = 32
inputs = torch.randn(B, N, 9).cuda()
inputs = torch.randn(B, N, 6).cuda()
labels = torch.from_numpy(np.random.randint(0, 3,
size=B * N)).view(B, N).cuda()
model = Pointnet2MSG(3)
model = Pointnet2MSG(3, input_channels=3)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=1e-2)
train_cls.py
+2 -2
View File
@@ -144,8 +144,8 @@ if __name__ == "__main__":
model,
model_fn,
optimizer,
checkpoint_name="checkpoints/cls_xyz",
best_name="checkpoints/cls_xyz_best",
checkpoint_name="checkpoints/single_layer",
best_name="checkpoints/single_layer_best",
lr_scheduler=lr_scheduler,
bnm_scheduler=bnm_scheduler
)
utils/cinclude/cuda_utils.h
+12 -1
View File
@@ -3,10 +3,21 @@
#include <cmath>
#define TOTAL_THREADS 512
inline int opt_n_threads(int work_size) {
const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
return max(min(1 << pow_2, 512), 32);
return max(min(1 << pow_2, TOTAL_THREADS), 1);
}
inline dim3 opt_block_config(int x, int y) {
const int x_threads = opt_n_threads(x);
const int y_threads =
max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);
dim3 block_config(x_threads, y_threads, 1);
return block_config;
}
#endif
utils/cinclude/group_points_gpu.h
+2 -2
View File
@@ -5,11 +5,11 @@
extern "C" {
#endif
void group_points_kernel_wrapper(int b, int n, int c, int npoints, int nsample,
void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out, cudaStream_t stream);
void group_points_grad_kernel_wrapper(int b, int n, int c, int npoints,
void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points,
cudaStream_t stream);
utils/cinclude/group_points_wrapper.h
+2 -3
View File
@@ -1,8 +1,7 @@
int group_points_wrapper(int b, int n, int c, int npoints, int nsample,
int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor, THCudaTensor *out);
int group_points_grad_wrapper(int b, int n, int c, int npoints, int nsample,
int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *grad_points_tensor);
utils/cinclude/interpolate_gpu.h
+2 -2
View File
@@ -9,12 +9,12 @@ void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx,
cudaStream_t stream);
void three_interpolate_kernel_wrapper(int b, int m, int c, int n,
void three_interpolate_kernel_wrapper(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out,
cudaStream_t stream);
void three_interpolate_grad_kernel_wrapper(int b, int n, int c, int m,
void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m,
const float *grad_out,
const int *idx, const float *weight,
float *grad_points,
utils/cinclude/interpolate_wrapper.h
+2 -2
View File
@@ -3,13 +3,13 @@
void three_nn_wrapper(int b, int n, int m, THCudaTensor *unknown_tensor,
THCudaTensor *known_tensor, THCudaTensor *dist2_tensor,
THCudaIntTensor *idx_tensor);
void three_interpolate_wrapper(int b, int m, int c, int n,
void three_interpolate_wrapper(int b, int c, int m, int n,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *weight_tensor,
THCudaTensor *out_tensor);
void three_interpolate_grad_wrapper(int b, int n, int c, int m,
void three_interpolate_grad_wrapper(int b, int c, int n, int m,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *weight_tensor,
utils/cinclude/sampling_gpu.h
+5 -1
View File
@@ -5,10 +5,14 @@
extern "C" {
#endif
void gather_points_kernel_wrapper(int b, int n, int c, int npoints,
void gather_points_kernel_wrapper(int b, int c, int n, int npoints,
const float *points, const int *idx,
float *out, cudaStream_t stream);
void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
const float *grad_out, const int *idx,
float *grad_points, cudaStream_t stream);
void furthest_point_sampling_kernel_wrapper(int b, int n, int m,
const float *dataset, float *temp,
int *idxs, cudaStream_t stream);
utils/cinclude/sampling_wrapper.h
+5 -1
View File
@@ -1,8 +1,12 @@
int gather_points_wrapper(int b, int n, int c, int npoints,
int gather_points_wrapper(int b, int c, int n, int npoints,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *out_tensor);
int gather_points_grad_wrapper(int b, int c, int n, int npoints,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *grad_points_tensor);
int furthest_point_sampling_wrapper(int b, int n, int m,
THCudaTensor *points_tensor,
utils/csrc/group_points.c
+4 -4
View File
@@ -4,7 +4,7 @@
extern THCState *state;
int group_points_wrapper(int b, int n, int c, int npoints, int nsample,
int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *out_tensor) {
@@ -15,12 +15,12 @@ int group_points_wrapper(int b, int n, int c, int npoints, int nsample,
cudaStream_t stream = THCState_getCurrentStream(state);
group_points_kernel_wrapper(b, n, c, npoints, nsample, points, idx, out,
group_points_kernel_wrapper(b, c, n, npoints, nsample, points, idx, out,
stream);
return 1;
}
int group_points_grad_wrapper(int b, int n, int c, int npoints, int nsample,
int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *grad_points_tensor) {
@@ -31,7 +31,7 @@ int group_points_grad_wrapper(int b, int n, int c, int npoints, int nsample,
cudaStream_t stream = THCState_getCurrentStream(state);
group_points_grad_kernel_wrapper(b, n, c, npoints, nsample, grad_out, idx,
group_points_grad_kernel_wrapper(b, c, n, npoints, nsample, grad_out, idx,
grad_points, stream);
return 1;
}
utils/csrc/group_points_gpu.cu
+27 -27
View File
@@ -4,9 +4,9 @@
#include "cuda_utils.h"
#include "group_points_gpu.h"
// input: points(b, n, c) idx(b, npoints, nsample)
// output: out(b, npoints, nsample, c)
__global__ void group_points_kernel(int b, int n, int c, int npoints,
// input: points(b, c, n) idx(b, npoints, nsample)
// output: out(b, c, npoints, nsample)
__global__ void group_points_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ points,
const int *__restrict__ idx,
@@ -16,25 +16,25 @@ __global__ void group_points_kernel(int b, int n, int c, int npoints,
idx += batch_index * npoints * nsample;
out += batch_index * npoints * nsample * c;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j = index; j < npoints; j += stride) {
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * npoints; i += stride) {
const int l = i / npoints;
const int j = i % npoints;
for (int k = 0; k < nsample; ++k) {
int ii = idx[j * nsample + k];
for (int l = 0; l < c; ++l) {
out[j * nsample * c + k * c + l] = points[ii * c + l];
}
out[(l * npoints + j) * nsample + k] = points[l * n + ii];
}
}
}
void group_points_kernel_wrapper(int b, int n, int c, int npoints, int nsample,
void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out, cudaStream_t stream) {
cudaError_t err;
group_points_kernel<<<b, opt_n_threads(npoints), 0, stream>>>(
b, n, c, npoints, nsample, points, idx, out);
group_points_kernel<<<b, opt_block_config(npoints, c), 0, stream>>>(
b, c, n, npoints, nsample, points, idx, out);
err = cudaGetLastError();
if (cudaSuccess != err) {
@@ -43,38 +43,38 @@ void group_points_kernel_wrapper(int b, int n, int c, int npoints, int nsample,
}
}
// input: grad_out(b, npoints, nsample, c), idx(b, npoints, nsample)
// output: grad_points(b, n, c)
__global__ void group_points_grad_kernel(int b, int n, int c, int npoints,
// input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample)
// output: grad_points(b, c, n)
__global__ void group_points_grad_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ grad_out,
const int *__restrict__ idx,
float *__restrict__ grad_points) {
int batch_index = blockIdx.x;
grad_points += batch_index * n * c;
idx += batch_index * npoints * nsample;
grad_out += batch_index * npoints * nsample * c;
idx += batch_index * npoints * nsample;
grad_points += batch_index * n * c;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j = index; j < npoints; j += stride) {
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * npoints; i += stride) {
const int l = i / npoints;
const int j = i % npoints;
for (int k = 0; k < nsample; ++k) {
int ii = idx[j * nsample + k];
for (int l = 0; l < c; ++l) {
atomicAdd(grad_points + ii * c + l,
grad_out[j * nsample * c + k * c + l]);
}
atomicAdd(grad_points + l * n + ii,
grad_out[(l * npoints + j) * nsample + k]);
}
}
}
void group_points_grad_kernel_wrapper(int b, int n, int c, int npoints,
void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points,
cudaStream_t stream) {
cudaError_t err;
group_points_grad_kernel<<<b, opt_n_threads(npoints), 0, stream>>>(
b, n, c, npoints, nsample, grad_out, idx, grad_points);
group_points_grad_kernel<<<b, opt_block_config(npoints, c), 0, stream>>>(
b, c, n, npoints, nsample, grad_out, idx, grad_points);
err = cudaGetLastError();
if (cudaSuccess != err) {
utils/csrc/interpolate.c
+4 -4
View File
@@ -19,7 +19,7 @@ void three_nn_wrapper(int b, int n, int m, THCudaTensor *unknown_tensor,
three_nn_kernel_wrapper(b, n, m, unknown, known, dist2, idx, stream);
}
void three_interpolate_wrapper(int b, int m, int c, int n,
void three_interpolate_wrapper(int b, int c, int m, int n,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *weight_tensor,
@@ -31,11 +31,11 @@ void three_interpolate_wrapper(int b, int m, int c, int n,
const int *idx = THCudaIntTensor_data(state, idx_tensor);
cudaStream_t stream = THCState_getCurrentStream(state);
three_interpolate_kernel_wrapper(b, m, c, n, points, idx, weight, out,
three_interpolate_kernel_wrapper(b, c, m, n, points, idx, weight, out,
stream);
}
void three_interpolate_grad_wrapper(int b, int n, int c, int m,
void three_interpolate_grad_wrapper(int b, int c, int n, int m,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *weight_tensor,
@@ -47,6 +47,6 @@ void three_interpolate_grad_wrapper(int b, int n, int c, int m,
const int *idx = THCudaIntTensor_data(state, idx_tensor);
cudaStream_t stream = THCState_getCurrentStream(state);
three_interpolate_grad_kernel_wrapper(b, n, c, m, grad_out, idx, weight,
three_interpolate_grad_kernel_wrapper(b, c, n, m, grad_out, idx, weight,
grad_points, stream);
}
utils/csrc/interpolate_gpu.cu
+25 -25
View File
@@ -77,9 +77,9 @@ void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown,
}
}
// input: points(b, m, c), idx(b, n, 3), weight(b, n, 3)
// output: out(b, n, c)
__global__ void three_interpolate_kernel(int b, int m, int c, int n,
// input: points(b, c, m), idx(b, n, 3), weight(b, n, 3)
// output: out(b, c, n)
__global__ void three_interpolate_kernel(int b, int c, int m, int n,
const float *__restrict__ points,
const int *__restrict__ idx,
const float *__restrict__ weight,
@@ -92,9 +92,11 @@ __global__ void three_interpolate_kernel(int b, int m, int c, int n,
out += batch_index * n * c;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j = index; j < n; j += stride) {
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * n; i += stride) {
const int l = i / n;
const int j = i % n;
float w1 = weight[j * 3 + 0];
float w2 = weight[j * 3 + 1];
float w3 = weight[j * 3 + 2];
@@ -103,21 +105,19 @@ __global__ void three_interpolate_kernel(int b, int m, int c, int n,
int i2 = idx[j * 3 + 1];
int i3 = idx[j * 3 + 2];
for (int l = 0; l < c; ++l) {
out[j * c + l] = points[i1 * c + l] * w1 + points[i2 * c + l] * w2 +
points[i3 * c + l] * w3;
}
out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 +
points[l * m + i3] * w3;
}
}
void three_interpolate_kernel_wrapper(int b, int m, int c, int n,
void three_interpolate_kernel_wrapper(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out,
cudaStream_t stream) {
cudaError_t err;
three_interpolate_kernel<<<b, opt_n_threads(n) / 4, 0, stream>>>(
b, m, c, n, points, idx, weight, out);
three_interpolate_kernel<<<b, opt_block_config(n, c), 0, stream>>>(
b, c, m, n, points, idx, weight, out);
err = cudaGetLastError();
if (cudaSuccess != err) {
@@ -128,11 +128,11 @@ void three_interpolate_kernel_wrapper(int b, int m, int c, int n,
}
}
// input: grad_out(b, n, c), idx(b, n, 3), weight(b, n, 3)
// output: grad_points(b, m, c)
// input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3)
// output: grad_points(b, c, m)
__global__ void three_interpolate_grad_kernel(
int b, int n, int c, int m, const float *__restrict__ grad_out,
int b, int c, int n, int m, const float *__restrict__ grad_out,
const int *__restrict__ idx, const float *__restrict__ weight,
float *__restrict__ grad_points) {
int batch_index = blockIdx.x;
@@ -141,9 +141,11 @@ __global__ void three_interpolate_grad_kernel(
weight += batch_index * n * 3;
grad_points += batch_index * m * c;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j = index; j < n; j += stride) {
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * n; i += stride) {
const int l = i / n;
const int j = i % n;
float w1 = weight[j * 3 + 0];
float w2 = weight[j * 3 + 1];
float w3 = weight[j * 3 + 2];
@@ -152,11 +154,9 @@ __global__ void three_interpolate_grad_kernel(
int i2 = idx[j * 3 + 1];
int i3 = idx[j * 3 + 2];
for (int l = 0; l < c; ++l) {
atomicAdd(grad_points + i1 * c + l, grad_out[j * c + l] * w1);
atomicAdd(grad_points + i2 * c + l, grad_out[j * c + l] * w2);
atomicAdd(grad_points + i3 * c + l, grad_out[j * c + l] * w3);
}
atomicAdd(grad_points + l * m + i1, grad_out[i] * w1);
atomicAdd(grad_points + l * m + i2, grad_out[i] * w2);
atomicAdd(grad_points + l * m + i3, grad_out[i] * w3);
}
}
@@ -167,7 +167,7 @@ void three_interpolate_grad_kernel_wrapper(int b, int n, int c, int m,
cudaStream_t stream) {
cudaError_t err;
three_interpolate_grad_kernel<<<b, opt_n_threads(n) / 4, 0, stream>>>(
three_interpolate_grad_kernel<<<b, opt_block_config(n, c), 0, stream>>>(
b, n, c, m, grad_out, idx, weight, grad_points);
err = cudaGetLastError();
utils/csrc/sampling.c
+18 -2
View File
@@ -4,7 +4,7 @@
extern THCState *state;
int gather_points_wrapper(int b, int n, int c, int npoints,
int gather_points_wrapper(int b, int c, int n, int npoints,
THCudaTensor *points_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *out_tensor) {
@@ -15,7 +15,23 @@ int gather_points_wrapper(int b, int n, int c, int npoints,
cudaStream_t stream = THCState_getCurrentStream(state);
gather_points_kernel_wrapper(b, n, c, npoints, points, idx, out, stream);
gather_points_kernel_wrapper(b, c, n, npoints, points, idx, out, stream);
return 1;
}
int gather_points_grad_wrapper(int b, int c, int n, int npoints,
THCudaTensor *grad_out_tensor,
THCudaIntTensor *idx_tensor,
THCudaTensor *grad_points_tensor) {
const float *grad_out = THCudaTensor_data(state, grad_out_tensor);
const int *idx = THCudaIntTensor_data(state, idx_tensor);
float *grad_points = THCudaTensor_data(state, grad_points_tensor);
cudaStream_t stream = THCState_getCurrentStream(state);
gather_points_grad_kernel_wrapper(b, c, n, npoints, grad_out, idx,
grad_points, stream);
return 1;
}
utils/csrc/sampling_gpu.cu
+44 -11
View File
@@ -4,30 +4,63 @@
#include "cuda_utils.h"
#include "sampling_gpu.h"
// input: points(b, n, c) idx(b, m)
// output: out(b, m, c)
__global__ void gather_points_kernel(int b, int n, int c, int m,
// input: points(b, c, n) idx(b, m)
// output: out(b, c, m)
__global__ void gather_points_kernel(int b, int c, int n, int m,
const float *__restrict__ points,
const int *__restrict__ idx,
float *__restrict__ out) {
for (int i = blockIdx.x; i < b; i += gridDim.x) {
for (int j = blockIdx.y * blockDim.x + threadIdx.x; j < m;
j += blockDim.x * gridDim.y) {
const int jj = idx[i * m + j];
for (int l = 0; l < c; ++l) {
out[(i * m + j) * c + l] = points[(i * n + jj) * c + l];
for (int l = blockIdx.y; l < c; l += gridDim.y) {
for (int j = threadIdx.x; j < m; j += blockDim.x) {
int a = idx[i * m + j];
out[(i * c + l) * m + j] = points[(i * c + l) * n + a];
}
}
}
}
void gather_points_kernel_wrapper(int b, int n, int c, int npoints,
void gather_points_kernel_wrapper(int b, int c, int n, int npoints,
const float *points, const int *idx,
float *out, cudaStream_t stream) {
cudaError_t err;
gather_points_kernel<<<dim3(b, 8, 1), opt_n_threads(npoints), 0,
stream>>>(b, n, c, npoints, points, idx, out);
gather_points_kernel<<<dim3(b, c, 1), opt_n_threads(npoints), 0, stream>>>(
b, c, n, npoints, points, idx, out);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
// input: grad_out(b, c, m) idx(b, m)
// output: grad_points(b, c, n)
__global__ void gather_points_grad_kernel(int b, int c, int n, int m,
const float *__restrict__ grad_out,
const int *__restrict__ idx,
float *__restrict__ grad_points) {
for (int i = blockIdx.x; i < b; i += gridDim.x) {
for (int l = blockIdx.y; l < c; l += gridDim.y) {
for (int j = threadIdx.x; j < m; j += blockDim.x) {
int a = idx[i * m + j];
atomicAdd(grad_points + (i * c + l) * n + a,
grad_out[(i * c + l) * m + j]);
}
}
}
}
void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
const float *grad_out, const int *idx,
float *grad_points,
cudaStream_t stream) {
cudaError_t err;
gather_points_grad_kernel<<<dim3(b, c, 1), opt_n_threads(npoints), 0,
stream>>>(b, c, n, npoints, grad_out, idx,
grad_points);
err = cudaGetLastError();
if (cudaSuccess != err) {
utils/pointnet2_modules.py
+71 -93
View File
@@ -7,7 +7,59 @@ import pytorch_utils as pt_utils
from typing import List
class PointnetSAModuleMSG(nn.Module):
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super().__init__()
self.npoint = None
self.groupers = None
self.mlps = None
def forward(self, xyz: torch.Tensor,
points: torch.Tensor = None) -> (torch.Tensor, torch.Tensor):
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the points
point : torch.Tensor
(B, N, C) tensor of the descriptors of the the points
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new points' xyz
new_points : torch.Tensor
(B, npoint, \sum_k(mlps[k][-1])) tensor of the new_points descriptors
"""
new_points_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = pointnet2_utils.gather_points(
xyz_flipped,
pointnet2_utils.furthest_point_sample(xyz, self.npoint)
).transpose(1, 2).contiguous() if self.npoint is not None else None
for i in range(len(self.groupers)):
new_points = self.groupers[i](
xyz, new_xyz, points
) # (B, C, npoint, nsample)
new_points = self.mlps[i](
new_points
) # (B, mlp[-1], npoint, nsample)
new_points = F.max_pool2d(
new_points, kernel_size=[1, new_points.size(3)]
) # (B, mlp[-1], npoint, 1)
new_points = new_points.squeeze(-1) # (B, mlp[-1], npoint)
new_points_list.append(new_points)
return new_xyz, torch.cat(new_points_list, dim=1)
class PointnetSAModuleMSG(_PointnetSAModuleBase):
r"""Pointnet set abstrction layer with multiscale grouping
Parameters
@@ -48,49 +100,13 @@ class PointnetSAModuleMSG(nn.Module):
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn))
def forward(self, xyz: torch.Tensor,
points: torch.Tensor = None) -> (torch.Tensor, torch.Tensor):
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the points
point : torch.Tensor
(B, N, C) tensor of the descriptors of the the points
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new points' xyz
new_points : torch.Tensor
(B, npoint, \sum_k(mlps[k][-1])) tensor of the new_points descriptors
"""
new_points_list = []
new_xyz = pointnet2_utils.gather_points(
xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
)
for i in range(len(self.groupers)):
new_points = self.groupers[i](xyz, new_xyz, points)
new_points = self.mlps[i](new_points.permute(0, 3, 1, 2)
) # (B, mlp[-1], npoint, nsample)
new_points = F.max_pool2d(
new_points, kernel_size=[1, new_points.size(3)]
) # (B, mlp[-1], npoint, 1)
new_points = new_points.squeeze(-1) # (B, mlp[-1], npoint)
new_points = new_points.transpose(
1, 2
).contiguous() # (B, npoint, mlp[-1])
new_points_list.append(new_points)
return new_xyz, torch.cat(new_points_list, dim=-1)
class PointnetSAModule(nn.Module):
class PointnetSAModule(_PointnetSAModuleBase):
r"""Pointnet set abstrction layer
Parameters
@@ -119,57 +135,22 @@ class PointnetSAModule(nn.Module):
):
super().__init__()
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
if self.npoint is not None:
assert radius is not None
assert nsample is not None
self.grouper = pointnet2_utils.QueryAndGroup(
radius, nsample, use_xyz=use_xyz
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
)
else:
self.grouper = pointnet2_utils.GroupAll(use_xyz=use_xyz)
self.groupers.append(pointnet2_utils.GroupAll(use_xyz=use_xyz))
self.mlp = pt_utils.SharedMLP(mlp, bn=bn)
if use_xyz:
mlp[0] += 3
def forward(self, xyz: torch.Tensor,
points: torch.Tensor = None) -> (torch.Tensor, torch.Tensor):
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the points
point : torch.Tensor
(B, N, C) tensor of the descriptors of the the points
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new points' xyz
new_points : torch.Tensor
(B, npoint, mlp[-1]) tensor of the new_points descriptors
"""
if self.npoint is not None:
new_xyz = pointnet2_utils.gather_points(
xyz, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
)
else:
new_xyz = xyz.data.new([[[0, 0, 0]]]).expand(xyz.size(0), 1, 3)
new_points = self.grouper(
xyz, new_xyz, points
) # (B, npoint, nsample, 3 + C)
new_points = self.mlp(new_points.permute(0, 3, 1, 2)
) # (B, mlp[-1], npoint, nsample)
new_points = F.max_pool2d(
new_points, kernel_size=[1, new_points.size(3)]
) # (B, mlp[-1], npoint, 1)
new_points = new_points.squeeze(-1) # (B, mlp[-1], npoint)
new_points = new_points.transpose(1, 2
).contiguous() # (B, npoint, mlp[-1])
return new_xyz, new_points
self.mlps.append(pt_utils.SharedMLP(mlp, bn=bn))
class PointnetFPModule(nn.Module):
@@ -199,14 +180,14 @@ class PointnetFPModule(nn.Module):
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known points
unknow_feats : torch.Tensor
(B, n, C1) tensor of the features to be propigated to
(B, C1, n) tensor of the features to be propigated to
known_feats : torch.Tensor
(B, m, C2) tensor of features to be propigated
(B, C2, m) tensor of features to be propigated
Returns
-------
new_points : torch.Tensor
(B, n, mlp[-1]) tensor of the features of the unknown points
(B, mlp[-1], n) tensor of the features of the unknown points
"""
dist, idx = pointnet2_utils.three_nn(unknown, known)
@@ -219,17 +200,14 @@ class PointnetFPModule(nn.Module):
)
if unknow_feats is not None:
new_points = torch.cat([interpolated_feats, unknow_feats],
dim=-1) #(B, n, C2 + C1)
dim=1) #(B, C2 + C1, n)
else:
new_points = interpolated_feats
new_points = new_points.unsqueeze(-1).transpose(
1, 2
) #(B, C2 + C1, n, 1)
new_points = new_points.unsqueeze(-1)
new_points = self.mlp(new_points)
return new_points.squeeze(-1).transpose(1, 2
).contiguous() #(B, n, mlp[-1])
return new_points.squeeze(-1)
if __name__ == "__main__":
utils/pointnet2_utils.py
+69 -72
View File
@@ -45,15 +45,13 @@ class FurthestPointSampling(Function):
torch.Tensor
(B, npoint) tensor containing the set
"""
assert xyz.is_contiguous()
B, N, _ = xyz.size()
output = torch.cuda.IntTensor(B, npoint)
temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
xyz = xyz.contiguous()
temp = temp.contiguous()
output = output.contiguous()
pointnet2.furthest_point_sampling_wrapper(
B, N, npoint, xyz, temp, output
)
@@ -73,13 +71,11 @@ class GatherPoints(Function):
@staticmethod
def forward(ctx, points: torch.Tensor, idx: torch.Tensor) -> torch.Tensor:
r"""
Uses iterative furthest point sampling to select a set of npoint points that have the largest
minimum distance
Parameters
----------
points : torch.Tensor
(B, N, 3) tensor
(B, C, N) tensor
idx : torch.Tensor
(B, npoint) tensor of the points to gather
@@ -87,25 +83,34 @@ class GatherPoints(Function):
Returns
-------
torch.Tensor
(B, npoint, 3) tensor
(B, C, npoint) tensor
"""
assert points.is_contiguous()
assert idx.is_contiguous()
B, N, C = points.size()
npoint = idx.size(1)
B, npoint = idx.size()
_, C, N = points.size()
output = torch.cuda.FloatTensor(B, npoint, C)
output = torch.cuda.FloatTensor(B, C, npoint)
points = points.contiguous()
idx = idx.contiguous()
output = output.contiguous()
pointnet2.gather_points_wrapper(B, C, N, npoint, points, idx, output)
pointnet2.gather_points_wrapper(B, N, C, npoint, points, idx, output)
ctx.for_backwards = (idx, C, N)
return output
@staticmethod
def backward(ctx, a=None):
return None, None
def backward(ctx, grad_out):
idx, C, N = ctx.for_backwards
B, npoint = idx.size()
grad_points = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
grad_out_data = grad_out.data.contiguous()
pointnet2.gather_points_grad_wrapper(
B, C, N, npoint, grad_out_data, idx, grad_points.data
)
return grad_points, None
gather_points = GatherPoints.apply
@@ -132,15 +137,14 @@ class ThreeNN(Function):
idx : torch.Tensor
(B, n, 3) index of 3 nearest neighbors
"""
assert unknown.is_contiguous()
assert known.is_contiguous()
B, N, _ = unknown.size()
m = known.size(1)
dist2 = torch.cuda.FloatTensor(B, N, 3)
idx = torch.cuda.IntTensor(B, N, 3)
unknown = unknown.contiguous()
known = known.contiguous()
dist2 = dist2.contiguous()
idx = idx.contiguous()
pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx)
return torch.sqrt(dist2), idx
@@ -164,7 +168,7 @@ class ThreeInterpolate(Function):
Parameters
----------
points : torch.Tensor
(B, m, c) Points to be interpolated from
(B, c, m) Points to be interpolated from
idx : torch.Tensor
(B, n, 3) three nearest neighbors of the target points in points
weight : torch.Tensor
@@ -173,22 +177,21 @@ class ThreeInterpolate(Function):
Returns
-------
torch.Tensor
(B, n, c) tensor of the interpolated points
(B, c, n) tensor of the interpolated points
"""
assert points.is_contiguous()
assert idx.is_contiguous()
assert weight.is_contiguous()
B, m, c = points.size()
B, c, m = points.size()
n = idx.size(1)
ctx.three_interpolate_for_backward = (idx, weight, m)
output = torch.cuda.FloatTensor(B, n, c)
output = torch.cuda.FloatTensor(B, c, n)
points = points.contiguous()
idx = idx.contiguous()
weight = weight.contiguous()
output = output.contiguous()
pointnet2.three_interpolate_wrapper(
B, m, c, n, points, idx, weight, output
B, c, m, n, points, idx, weight, output
)
return output
@@ -200,28 +203,25 @@ class ThreeInterpolate(Function):
Parameters
----------
grad_out : torch.Tensor
(B, n, c) tensor with gradients of ouputs
(B, c, n) tensor with gradients of ouputs
Returns
-------
grad_points : torch.Tensor
(B, m, c) tensor with gradients of points
(B, c, m) tensor with gradients of points
None
None
"""
idx, weight, m = ctx.three_interpolate_for_backward
B, n, c = grad_out.size()
B, c, n = grad_out.size()
grad_points = Variable(torch.cuda.FloatTensor(B, m, c).zero_())
grad_points = Variable(torch.cuda.FloatTensor(B, c, m).zero_())
grad_out = grad_out.contiguous()
idx = idx.contiguous()
weight = weight.contiguous()
grad_points = grad_points.contiguous()
grad_out_data = grad_out.data.contiguous()
pointnet2.three_interpolate_grad_wrapper(
B, n, c, m, grad_out.data, idx, weight, grad_points.data
B, c, n, m, grad_out_data, idx, weight, grad_points.data
)
return grad_points, None, None
@@ -239,28 +239,28 @@ class GroupPoints(Function):
Parameters
----------
points : torch.Tensor
(B, N, C) tensor of points to group
(B, C, N) tensor of points to group
idx : torch.Tensor
(B, npoint, nsample) tensor containing the indicies of points to group with
Returns
-------
torch.Tensor
(B, npoint, nsample, C) tensor
(B, C, npoint, nsample) tensor
"""
assert points.is_contiguous()
assert idx.is_contiguous()
B, npoints, nsample = idx.size()
_, N, C = points.size()
_, C, N = points.size()
output = torch.cuda.FloatTensor(B, npoints, nsample, C)
output = torch.cuda.FloatTensor(B, C, npoints, nsample)
points = points.contiguous()
idx = idx.contiguous()
output = output.contiguous()
pointnet2.group_points_wrapper(
B, N, C, npoints, nsample, points, idx, output
B, C, N, npoints, nsample, points, idx, output
)
ctx.idx_N_C_for_backward = (idx, N, C)
ctx.for_backwards = (idx, N)
return output
@staticmethod
@@ -271,23 +271,22 @@ class GroupPoints(Function):
Parameters
----------
grad_out : torch.Tensor
(B, npoint, nsample, C) tensor of the gradients of the output from forward
(B, C, npoint, nsample) tensor of the gradients of the output from forward
Returns
-------
torch.Tensor
(B, N, C) gradient of the points
(B, C, N) gradient of the points
None
"""
idx, N, C = ctx.idx_N_C_for_backward
idx, N = ctx.for_backwards
B, npoint, nsample, _ = grad_out.size()
grad_points = Variable(torch.cuda.FloatTensor(B, N, C).zero_())
B, C, npoint, nsample = grad_out.size()
grad_points = Variable(torch.cuda.FloatTensor(B, C, N).zero_())
grad_out = grad_out.contiguous()
grad_points = grad_points.contiguous()
grad_out_data = grad_out.data.contiguous()
pointnet2.group_points_grad_wrapper(
B, N, C, npoint, nsample, grad_out.data, idx, grad_points.data
B, C, N, npoint, nsample, grad_out_data, idx, grad_points.data
)
return grad_points, None
@@ -321,14 +320,13 @@ class BallQuery(Function):
torch.Tensor
(B, npoint, nsample) tensor with the indicies of the points that form the query balls
"""
assert new_xyz.is_contiguous()
assert xyz.is_contiguous()
B, N, _ = xyz.size()
npoint = new_xyz.size(1)
idx = torch.cuda.IntTensor(B, npoint, nsample).zero_()
new_xyz = new_xyz.contiguous()
xyz = xyz.contiguous()
idx = idx.contiguous()
pointnet2.ball_query_wrapper(
B, N, npoint, radius, nsample, new_xyz, xyz, idx
)
@@ -373,23 +371,24 @@ class QueryAndGroup(nn.Module):
new_xyz : torch.Tensor
centriods (B, npoint, 3)
points : torch.Tensor
Descriptors of the points (B, N, C)
Descriptors of the points (B, C, N)
Returns
-------
new_points : torch.Tensor
(B, npoint, nsample, 3 + C) tensor
(B, 3 + C, npoint, nsample) tensor
"""
idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
grouped_xyz = group_points(xyz, idx) # (B, npoint, nsample, 3)
grouped_xyz -= new_xyz.unsqueeze(2)
xyz_trans = xyz.transpose(1, 2).contiguous()
grouped_xyz = group_points(xyz_trans, idx) # (B, 3, npoint, nsample)
grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
if points is not None:
grouped_points = group_points(points, idx)
if self.use_xyz:
new_points = torch.cat([grouped_xyz, grouped_points],
dim=-1) # (B, npoint, nsample, 3 + C)
dim=1) # (B, C + 3, npoint, nsample)
else:
new_points = group_points
else:
@@ -422,24 +421,22 @@ class GroupAll(nn.Module):
xyz : torch.Tensor
xyz coordinates of the points (B, N, 3)
new_xyz : torch.Tensor
centriods (B, npoint, 3)
Ignored
points : torch.Tensor
Descriptors of the points (B, N, C)
Descriptors of the points (B, C, N)
Returns
-------
new_points : torch.Tensor
(B, npoint, nsample, 3 + C) tensor
(B, C + 3, 1, N) tensor
"""
grouped_xyz = xyz.view(xyz.size(0), 1, xyz.size(1), xyz.size(2))
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if points is not None:
grouped_points = points.view(
points.size(0), 1, points.size(1), points.size(2)
)
grouped_points = points.unsqueeze(2)
if self.use_xyz:
new_points = torch.cat([grouped_xyz, grouped_points],
dim=-1) # (B, npoint, nsample, 3 + C)
dim=1) # (B, 3 + C, 1, N)
else:
new_points = group_points
else:
utils/pytorch_utils.py
+46 -1
View File
@@ -561,6 +561,8 @@ class Trainer(object):
self.checkpoint_name, self.best_name = checkpoint_name, best_name
self.eval_frequency = eval_frequency
self.training_best = {}
self.eval_best = {}
if log_name is not None:
tb_log.configure(log_name)
@@ -617,6 +619,21 @@ class Trainer(object):
self._print("Train", epoch, total_loss, eval_dict, count)
if 'loss' in self.training_best:
self.training_best['loss'] = np.min(
self.training_best['loss'], total_loss / count
)
else:
self.training_best['loss'] = total_loss / count
for k, v in eval_dict.items():
if k in self.training_best:
self.training_best[k] = np.max(
self.training_best[k], stats.means(v)
)
else:
self.training_best[k] = stats.mean(v)
def eval_epoch(self, epoch, d_loader):
if d_loader is None:
return
@@ -650,6 +667,19 @@ class Trainer(object):
self._print("Eval", epoch, total_loss, eval_dict, count)
if 'loss' in self.eval_best:
self.eval_best['loss'] = np.min(
self.eval_best['loss'], total_loss / count
)
else:
self.eval_best['loss'] = total_loss / count
for k, v in eval_dict.items():
if k in self.eval_best:
self.eval_best[k] = np.max(self.eval_best[k], stats.means(v))
else:
self.eval_best[k] = stats.mean(v)
return total_loss / count, eval_dict
def train(
@@ -689,11 +719,26 @@ class Trainer(object):
best_loss = min(best_loss, val_loss)
save_checkpoint(
checkpoint_state(
self.model, self.optimizer, val_loss, epoch
self.model, self.optimizer, val_loss, epoch + 1
),
is_best,
filename=self.checkpoint_name,
bestname=self.best_name
)
print("{0} Summary {0}".format("-" * 5))
print("** Training Stats **")
for k, v in natsorted(self.training_best.items(), key=itemgetter(0)):
if k == 'loss':
print("Best loss: {:.4e}".format(v))
else:
print("Best {}: {:2.3f}%".format(k, v * 1e2))
print("\n** Eval Stats **")
for k, v in natsorted(self.eval_best.items(), key=itemgetter(0)):
if k == 'loss':
print("Best loss: {:.4e}".format(v))
else:
print("Best {}: {:2.3f}%".format(k, v * 1e2))
return best_loss