Fix pytorch1.5 compile error (#2524)

* replace AT_CHECK with TORCH_CHECK to support pytorch1.5

* fix compile warning, delete grid sample op

* fix ci pytorch version

* delete python3.5

* update version of pytorch and torchvision in ci

* change is_cuda to device().is_cuda()

* specify TORCH_CUDA_ARCH_LIST=6.0,7.0

* fix typo

* delete affine grid

* fix typos

* fix setup.py

* remove redundant comment

.travis.yml

@@ -2,11 +2,19 @@ dist: bionic  # ubuntu 18.04
 language: python
 
 python:
-  - "3.5"
   - "3.6"
   - "3.7"
 
-env: CUDA=10.1.105-1 CUDA_SHORT=10.1 UBUNTU_VERSION=ubuntu1804 FORCE_CUDA=1
+env:
+  global:
+    - CUDA=10.1.105-1
+    - CUDA_SHORT=10.1
+    - UBUNTU_VERSION=ubuntu1804
+    - FORCE_CUDA=1
+  matrix:
+    - TORCH=1.3.1 TORCHVISION=0.4.2 CUDA_ARCH=6.0
+    - TORCH=1.5.0 TORCHVISION=0.6.0 CUDA_ARCH=7.0
+
 cache: pip
 
 # Ref to CUDA installation in Travis: https://github.com/jeremad/cuda-travis
@@ -25,7 +33,7 @@ before_install:
 
 install:
   - pip install Pillow==6.2.2  # remove this line when torchvision>=0.5
-  - pip install torch==1.2 torchvision==0.4.0  # TODO: fix CI for pytorch>1.2
+  - pip install torch==${TORCH} torchvision==${TORCHVISION}
   - pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
   - pip install -r requirements.txt
 
@@ -36,7 +44,7 @@ before_script:
 
 script:
   - python setup.py check -m -s
-  - python setup.py build_ext --inplace
+  - TORCH_CUDA_ARCH_LIST="${CUDA_ARCH}" python setup.py build_ext --inplace
   - coverage run --branch --source mmdet -m py.test -v --xdoctest-modules tests mmdet
 
 after_success:

mmdet/models/mask_heads/fcn_mask_head.py

@@ -2,12 +2,12 @@
 import pycocotools.mask as mask_util
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from torch.nn.modules.utils import _pair
 
 from mmdet.core import auto_fp16, force_fp32, mask_target
 from mmdet.ops import Conv2d, ConvModule, build_upsample_layer
 from mmdet.ops.carafe import CARAFEPack
-from mmdet.ops.grid_sampler import grid_sample
 from ..builder import HEADS, build_loss
 
 BYTES_PER_FLOAT = 4
@@ -302,7 +302,7 @@ def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
     gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
     grid = torch.stack([gx, gy], dim=3)
 
-    img_masks = grid_sample(
+    img_masks = F.grid_sample(
         masks.to(dtype=torch.float32), grid, align_corners=False)
 
     if skip_empty:

mmdet/ops/carafe/src/carafe_ext.cpp

@@ -22,7 +22,7 @@ int carafe_forward(at::Tensor features, at::Tensor rfeatures,
                    at::Tensor masks, at::Tensor rmasks, int kernel_size,
                    int group_size, int scale_factor, at::Tensor routput,
                    at::Tensor output) {
-  if (features.type().is_cuda()) {
+  if (features.device().is_cuda()) {
 #ifdef WITH_CUDA
     return carafe_forward_cuda(features, rfeatures, masks, rmasks, kernel_size,
                                group_size, scale_factor, routput, output);
@@ -39,7 +39,7 @@ int carafe_backward(at::Tensor top_grad, at::Tensor rfeatures,
                     at::Tensor rbottom_grad_hs, at::Tensor rbottom_grad,
                     at::Tensor rmask_grad, at::Tensor bottom_grad,
                     at::Tensor mask_grad) {
-  if (top_grad.type().is_cuda()) {
+  if (top_grad.device().is_cuda()) {
 #ifdef WITH_CUDA
     return carafe_backward_cuda(top_grad, rfeatures, masks, kernel_size,
         group_size, scale_factor, rtop_grad, rbottom_grad_hs, rbottom_grad,

mmdet/ops/carafe/src/carafe_naive_ext.cpp

@@ -18,7 +18,7 @@ int carafe_naive_backward_cuda(at::Tensor top_grad, at::Tensor features,
 int carafe_naive_forward(at::Tensor features, at::Tensor masks,
                          int kernel_size, int group_size, int scale_factor,
                          at::Tensor output) {
-  if (features.type().is_cuda()) {
+  if (features.device().is_cuda()) {
 #ifdef WITH_CUDA
     return carafe_naive_forward_cuda(features, masks, kernel_size,
         group_size, scale_factor, output);
@@ -33,7 +33,7 @@ int carafe_naive_backward(at::Tensor top_grad, at::Tensor features,
                                at::Tensor masks, int kernel_size,
                                int group_size, int scale_factor,
                                at::Tensor bottom_grad, at::Tensor mask_grad) {
-  if (top_grad.type().is_cuda()) {
+  if (top_grad.device().is_cuda()) {
 #ifdef WITH_CUDA
     return carafe_naive_backward_cuda(top_grad, features, masks, kernel_size,
         group_size, scale_factor, bottom_grad, mask_grad);

mmdet/ops/carafe/src/cuda/carafe_cuda.cpp

@@ -24,9 +24,9 @@ int CARAFEBackwardLaucher(const at::Tensor top_grad, const at::Tensor rfeatures,
                           at::Tensor rmask_grad, at::Tensor bottom_grad,
                           at::Tensor mask_grad);
 
-#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
+#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ")
 #define CHECK_CONTIGUOUS(x) \
-  AT_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+  TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ")
 #define CHECK_INPUT(x) \
   CHECK_CUDA(x);       \
   CHECK_CONTIGUOUS(x)