Pytorch 1.11

README.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-### Good news! This repo supports pytorch-1.0 now!!! We borrowed some code and techniques from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark).
+### Good news! This repo supports pytorch-1.11 now!!! We borrowed some code and techniques from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark).
 
 This project is a *faster* pytorch implementation of faster R-CNN, aimed to accelerating the training of faster R-CNN object detection models. Recently, there are a number of good implementations:
 
@@ -34,7 +34,8 @@ During our implementing, we referred the above implementations, especially [long
 - [x] Add deformable pooling layer (mainly supported by [Xander](https://github.com/xanderchf)).
 - [x] Support pytorch-0.4.0 (go to master branch).
 - [x] Support tensorboardX.
-- [x] Support pytorch-1.0 (this branch).
+- [x] Support pytorch-1.0.
+- [x] Support pytorch-1.11 (this branch).
 
 ## Other Implementations
 
@@ -108,8 +109,8 @@ cd faster-rcnn.pytorch && mkdir data
 ### prerequisites
 
 * Python 2.7 or 3.6
-* Pytorch 1.0 (for Pytorch 0.4.0 go to master branch)
-* CUDA 8.0 or higher
+* Pytorch 1.11+ (for Pytorch 0.4.0 go to master branch)
+* CUDA 11.1 or higher
 
 ### Data Preparation
 

lib/model/csrc/cuda/ROIAlign_cuda.cu

@@ -1,10 +1,7 @@
 // Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 #include <ATen/ATen.h>
 #include <ATen/cuda/CUDAContext.h>
-
-#include <THC/THC.h>
-#include <THC/THCAtomics.cuh>
-#include <THC/THCDeviceUtils.cuh>
+#include <ATen/ceil_div.h>
 
 // TODO make it in a common file
 #define CUDA_1D_KERNEL_LOOP(i, n)                            \
@@ -272,11 +269,11 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
   auto output_size = num_rois * pooled_height * pooled_width * channels;
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::ceil_div(output_size, 512L), 4096L));
   dim3 block(512);
 
   if (output.numel() == 0) {
-    THCudaCheck(cudaGetLastError());
+    C10_CUDA_CHECK(cudaGetLastError());
     return output;
   }
 
@@ -294,7 +291,7 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
          rois.contiguous().data<scalar_t>(),
          output.data<scalar_t>());
   });
-  THCudaCheck(cudaGetLastError());
+  C10_CUDA_CHECK(cudaGetLastError());
   return output;
 }
 
@@ -317,12 +314,12 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::ceil_div(grad.numel(), 512L), 4096L));
   dim3 block(512);
 
   // handle possibly empty gradients
   if (grad.numel() == 0) {
-    THCudaCheck(cudaGetLastError());
+    C10_CUDA_CHECK(cudaGetLastError());
     return grad_input;
   }
 
@@ -341,6 +338,6 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
          grad_input.data<scalar_t>(),
          rois.contiguous().data<scalar_t>());
   });
-  THCudaCheck(cudaGetLastError());
+  C10_CUDA_CHECK(cudaGetLastError());
   return grad_input;
 }

lib/model/csrc/cuda/ROIPool_cuda.cu

@@ -1,10 +1,7 @@
 // Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 #include <ATen/ATen.h>
 #include <ATen/cuda/CUDAContext.h>
-
-#include <THC/THC.h>
-#include <THC/THCAtomics.cuh>
-#include <THC/THCDeviceUtils.cuh>
+#include <ATen/ceil_div.h>
 
 
 // TODO make it in a common file
@@ -126,11 +123,11 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(THCCeilDiv(output_size, 512L), 4096L));
+  dim3 grid(std::min(at::ceil_div(output_size, 512L), 4096L));
   dim3 block(512);
 
   if (output.numel() == 0) {
-    THCudaCheck(cudaGetLastError());
+    C10_CUDA_CHECK(cudaGetLastError());
     return std::make_tuple(output, argmax);
   }
 
@@ -148,7 +145,7 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
          output.data<scalar_t>(),
          argmax.data<int>());
   });
-  THCudaCheck(cudaGetLastError());
+  C10_CUDA_CHECK(cudaGetLastError());
   return std::make_tuple(output, argmax);
 }
 
@@ -173,12 +170,12 @@ at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  dim3 grid(std::min(THCCeilDiv(grad.numel(), 512L), 4096L));
+  dim3 grid(std::min(at::ceil_div(grad.numel(), 512L), 4096L));
   dim3 block(512);
 
   // handle possibly empty gradients
   if (grad.numel() == 0) {
-    THCudaCheck(cudaGetLastError());
+    C10_CUDA_CHECK(cudaGetLastError());
     return grad_input;
   }
 
@@ -197,6 +194,6 @@ at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
          grad_input.data<scalar_t>(),
          rois.contiguous().data<scalar_t>());
   });
-  THCudaCheck(cudaGetLastError());
+  C10_CUDA_CHECK(cudaGetLastError());
   return grad_input;
 }

lib/model/csrc/cuda/nms.cu

@@ -1,9 +1,8 @@
 // Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 #include <ATen/ATen.h>
 #include <ATen/cuda/CUDAContext.h>
-
-#include <THC/THC.h>
-#include <THC/THCDeviceUtils.cuh>
+#include <ATen/ceil_div.h>
+#include <ATen/cuda/ThrustAllocator.h>
 
 #include <vector>
 #include <iostream>
@@ -61,7 +60,7 @@ __global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
         t |= 1ULL << i;
       }
     }
-    const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock);
+    const int col_blocks = at::ceil_div(n_boxes, threadsPerBlock);
     dev_mask[cur_box_idx * col_blocks + col_start] = t;
   }
 }
@@ -76,28 +75,25 @@ at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) {
 
   int boxes_num = boxes.size(0);
 
-  const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock);
+  const int col_blocks = at::ceil_div(boxes_num, threadsPerBlock);
 
   scalar_t* boxes_dev = boxes_sorted.data<scalar_t>();
 
-  THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
 
   unsigned long long* mask_dev = NULL;
-  //THCudaCheck(THCudaMalloc(state, (void**) &mask_dev,
-  //                      boxes_num * col_blocks * sizeof(unsigned long long)));
 
-  mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long));
+  mask_dev = (unsigned long long*) c10::cuda::CUDACachingAllocator::raw_alloc(boxes_num * col_blocks * sizeof(unsigned long long));
 
-  dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock),
-              THCCeilDiv(boxes_num, threadsPerBlock));
+  dim3 blocks(at::ceil_div(boxes_num, threadsPerBlock),
+              at::ceil_div(boxes_num, threadsPerBlock));
   dim3 threads(threadsPerBlock);
   nms_kernel<<<blocks, threads>>>(boxes_num,
                                   nms_overlap_thresh,
                                   boxes_dev,
                                   mask_dev);
 
   std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
-  THCudaCheck(cudaMemcpy(&mask_host[0],
+  C10_CUDA_CHECK(cudaMemcpy(&mask_host[0],
                         mask_dev,
                         sizeof(unsigned long long) * boxes_num * col_blocks,
                         cudaMemcpyDeviceToHost));
@@ -122,7 +118,7 @@ at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) {
     }
   }
 
-  THCudaFree(state, mask_dev);
+  c10::cuda::CUDACachingAllocator::raw_delete(mask_dev);
   // TODO improve this part
   return std::get<0>(order_t.index({
                        keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to(

lib/model/utils/config.py

@@ -371,7 +371,7 @@ def cfg_from_file(filename):
   """Load a config file and merge it into the default options."""
   import yaml
   with open(filename, 'r') as f:
-    yaml_cfg = edict(yaml.load(f))
+    yaml_cfg = edict(yaml.safe_load(f))
 
   _merge_a_into_b(yaml_cfg, __C)