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connecting_the_dots
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added validation set and pre-trained model; tested on pytorch1.8

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master
Yiyi Liao 4 years ago
parent bdcc50a88f
commit 4617525f61
5 changed files with 68 additions and 60 deletions
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  1. 13
      readme.md
  2. 2
      renderer/Makefile
  3. 56
      torchext/ext/ext_cpu.cpp
  4. 54
      torchext/ext/ext_cuda.cpp
  5. 3
      torchext/setup.py

13
readme.md
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@ -32,6 +32,7 @@ The network training/evaluation code is based on `Pytorch`.
PyTorch>=1.1
Cuda>=10.0
```
Updated on 07.06.2021: The code is now compatible with the latest Pytorch version (1.8).
The other python packages can be installed with `anaconda`:
```
@ -65,11 +66,12 @@ python setup.py build_ext --inplace
## Running
### Creating synthetic data
### Creating Synthetic Data
To create synthetic data and save it locally, download [ShapeNet V2](https://www.shapenet.org/) and correct `SHAPENET_ROOT` in `config.json`. Then the data can be generated and saved to `DATA_ROOT` in `config.json` by running
```
./create_syn_data.sh
```
If you are only interested in evaluating our pre-trained model, [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip) is a validation set that contains a small amount of images.
### Training Network
@ -90,6 +92,15 @@ To evaluate a specific checkpoint, e.g. the 50th epoch, one can run
python train_val.py --cmd retest --epoch 50
```
### Evaluating a Pre-trained Model
We provide a model pre-trained using the photometric loss. Once you have prepared the synthetic dataset and changed `DATA_ROOT` in `config.json`, the pre-trained model can be evaluated on the validation set by running:
```
mkdir -p output
mkdir -p output/exp_syn
wget -O output/exp_syn/net_0099.params https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/net_0099.params
python train_val.py --cmd retest --epoch 99
```
You can also download our validation set from [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip).
## Acknowledgement
This work was supported by the Intel Network on Intelligent Systems.

2
renderer/Makefile
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@ -4,7 +4,7 @@ C_FLAGS = -O3 -msse -msse2 -msse3 -msse4.2 -fPIC -Wall
CXX = g++ -c
CXX_FLAGS = -O3 -std=c++11 -msse -msse2 -msse3 -msse4.2 -fPIC -Wall
CUDA = nvcc -c
CUDA_FLAGS = -x cu -Xcompiler -fPIC -arch=sm_30 -std=c++11 --expt-extended-lambda
CUDA_FLAGS = -x cu -Xcompiler -fPIC -std=c++11 --expt-extended-lambda
PYRENDER_DEPENDENCIES = setup.py \

56
torchext/ext/ext_cpu.cpp
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@ -12,17 +12,17 @@ void iterate_cpu(FunctorT functor, int N) {
}
at::Tensor nn_cpu(at::Tensor in0, at::Tensor in1) {
CHECK_INPUT_CPU(in0)
CHECK_INPUT_CPU(in1)
CHECK_INPUT_CPU(in0);
CHECK_INPUT_CPU(in1);
auto nelem0 = in0.size(0);
auto nelem1 = in1.size(0);
auto dim = in0.size(1);
AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape")
AT_ASSERTM(dim == 3, "dim hast to be 3")
AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3")
AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3")
AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape");
AT_ASSERTM(dim == 3, "dim hast to be 3");
AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3");
AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3");
auto out = at::empty({nelem0}, torch::CPU(at::kLong));
@ -37,11 +37,11 @@ at::Tensor nn_cpu(at::Tensor in0, at::Tensor in1) {
at::Tensor crosscheck_cpu(at::Tensor in0, at::Tensor in1) {
CHECK_INPUT_CPU(in0)
CHECK_INPUT_CPU(in1)
CHECK_INPUT_CPU(in0);
CHECK_INPUT_CPU(in1);
AT_ASSERTM(in0.dim() == 1, "")
AT_ASSERTM(in1.dim() == 1, "")
AT_ASSERTM(in0.dim() == 1, "");
AT_ASSERTM(in1.dim() == 1, "");
auto nelem0 = in0.size(0);
auto nelem1 = in1.size(0);
@ -57,21 +57,21 @@ at::Tensor crosscheck_cpu(at::Tensor in0, at::Tensor in1) {
at::Tensor proj_nn_cpu(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size) {
CHECK_INPUT_CPU(xyz0)
CHECK_INPUT_CPU(xyz1)
CHECK_INPUT_CPU(K)
CHECK_INPUT_CPU(xyz0);
CHECK_INPUT_CPU(xyz1);
CHECK_INPUT_CPU(K);
auto batch_size = xyz0.size(0);
auto height = xyz0.size(1);
auto width = xyz0.size(2);
AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "")
AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "")
AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "")
AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "")
AT_ASSERTM(xyz0.size(3) == 3, "")
AT_ASSERTM(xyz0.dim() == 4, "")
AT_ASSERTM(xyz1.dim() == 4, "")
AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "");
AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "");
AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "");
AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "");
AT_ASSERTM(xyz0.size(3) == 3, "");
AT_ASSERTM(xyz0.dim() == 4, "");
AT_ASSERTM(xyz1.dim() == 4, "");
auto out = at::empty({batch_size, height, width}, torch::CPU(at::kLong));
@ -86,8 +86,8 @@ at::Tensor proj_nn_cpu(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch
at::Tensor xcorrvol_cpu(at::Tensor in0, at::Tensor in1, int n_disps, int block_size) {
CHECK_INPUT_CPU(in0)
CHECK_INPUT_CPU(in1)
CHECK_INPUT_CPU(in0);
CHECK_INPUT_CPU(in1);
auto channels = in0.size(0);
auto height = in0.size(1);
@ -108,8 +108,8 @@ at::Tensor xcorrvol_cpu(at::Tensor in0, at::Tensor in1, int n_disps, int block_s
at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size, int type, float eps) {
CHECK_INPUT_CPU(es)
CHECK_INPUT_CPU(ta)
CHECK_INPUT_CPU(es);
CHECK_INPUT_CPU(ta);
auto batch_size = es.size(0);
auto channels = es.size(1);
@ -145,16 +145,16 @@ at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size
}
at::Tensor photometric_loss_backward(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps) {
CHECK_INPUT_CPU(es)
CHECK_INPUT_CPU(ta)
CHECK_INPUT_CPU(grad_out)
CHECK_INPUT_CPU(es);
CHECK_INPUT_CPU(ta);
CHECK_INPUT_CPU(grad_out);
auto batch_size = es.size(0);
auto channels = es.size(1);
auto height = es.size(2);
auto width = es.size(3);
CHECK_INPUT_CPU(ta)
CHECK_INPUT_CPU(ta);
auto grad_in = at::zeros({batch_size, channels, height, width}, grad_out.options());
AT_DISPATCH_FLOATING_TYPES(es.scalar_type(), "photometric_loss_backward_cpu", ([&] {

54
torchext/ext/ext_cuda.cpp
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@ -7,16 +7,16 @@
void nn_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out);
at::Tensor nn_cuda(at::Tensor in0, at::Tensor in1) {
CHECK_INPUT_CUDA(in0)
CHECK_INPUT_CUDA(in1)
CHECK_INPUT_CUDA(in0);
CHECK_INPUT_CUDA(in1);
auto nelem0 = in0.size(0);
auto dim = in0.size(1);
AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape")
AT_ASSERTM(dim == 3, "dim hast to be 3")
AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3")
AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3")
AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape");
AT_ASSERTM(dim == 3, "dim hast to be 3");
AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3");
AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3");
auto out = at::empty({nelem0}, torch::CUDA(at::kLong));
@ -29,11 +29,11 @@ at::Tensor nn_cuda(at::Tensor in0, at::Tensor in1) {
void crosscheck_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out);
at::Tensor crosscheck_cuda(at::Tensor in0, at::Tensor in1) {
CHECK_INPUT_CUDA(in0)
CHECK_INPUT_CUDA(in1)
CHECK_INPUT_CUDA(in0);
CHECK_INPUT_CUDA(in1);
AT_ASSERTM(in0.dim() == 1, "")
AT_ASSERTM(in1.dim() == 1, "")
AT_ASSERTM(in0.dim() == 1, "");
AT_ASSERTM(in1.dim() == 1, "");
auto nelem0 = in0.size(0);
auto out = at::empty({nelem0}, torch::CUDA(at::kByte));
@ -45,21 +45,21 @@ at::Tensor crosscheck_cuda(at::Tensor in0, at::Tensor in1) {
void proj_nn_kernel(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size, at::Tensor out);
at::Tensor proj_nn_cuda(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size) {
CHECK_INPUT_CUDA(xyz0)
CHECK_INPUT_CUDA(xyz1)
CHECK_INPUT_CUDA(K)
CHECK_INPUT_CUDA(xyz0);
CHECK_INPUT_CUDA(xyz1);
CHECK_INPUT_CUDA(K);
auto batch_size = xyz0.size(0);
auto height = xyz0.size(1);
auto width = xyz0.size(2);
AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "")
AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "")
AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "")
AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "")
AT_ASSERTM(xyz0.size(3) == 3, "")
AT_ASSERTM(xyz0.dim() == 4, "")
AT_ASSERTM(xyz1.dim() == 4, "")
AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "");
AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "");
AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "");
AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "");
AT_ASSERTM(xyz0.size(3) == 3, "");
AT_ASSERTM(xyz0.dim() == 4, "");
AT_ASSERTM(xyz1.dim() == 4, "");
auto out = at::empty({batch_size, height, width}, torch::CUDA(at::kLong));
@ -71,8 +71,8 @@ at::Tensor proj_nn_cuda(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patc
void xcorrvol_kernel(at::Tensor in0, at::Tensor in1, int n_disps, int block_size, at::Tensor out);
at::Tensor xcorrvol_cuda(at::Tensor in0, at::Tensor in1, int n_disps, int block_size) {
CHECK_INPUT_CUDA(in0)
CHECK_INPUT_CUDA(in1)
CHECK_INPUT_CUDA(in0);
CHECK_INPUT_CUDA(in1);
// auto channels = in0.size(0);
auto height = in0.size(1);
@ -90,8 +90,8 @@ at::Tensor xcorrvol_cuda(at::Tensor in0, at::Tensor in1, int n_disps, int block_
void photometric_loss_forward_kernel(at::Tensor es, at::Tensor ta, int block_size, int type, float eps, at::Tensor out);
at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size, int type, float eps) {
CHECK_INPUT_CUDA(es)
CHECK_INPUT_CUDA(ta)
CHECK_INPUT_CUDA(es);
CHECK_INPUT_CUDA(ta);
auto batch_size = es.size(0);
auto height = es.size(2);
@ -107,9 +107,9 @@ at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size
void photometric_loss_backward_kernel(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps, at::Tensor grad_in);
at::Tensor photometric_loss_backward(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps) {
CHECK_INPUT_CUDA(es)
CHECK_INPUT_CUDA(ta)
CHECK_INPUT_CUDA(grad_out)
CHECK_INPUT_CUDA(es);
CHECK_INPUT_CUDA(ta);
CHECK_INPUT_CUDA(grad_out);
auto batch_size = es.size(0);
auto channels = es.size(1);

3
torchext/setup.py
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@ -8,9 +8,6 @@ include_dirs = [
]
nvcc_args = [
'-arch=sm_30',
'-gencode=arch=compute_30,code=sm_30',
'-gencode=arch=compute_35,code=sm_35',
]
setup(

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