DensePoseResultsMplContourVisualizer output shape mismatch

[blatr]

1. Full runnable code or full changes you made: None
2. What exact command you run:

   python apply_net.py show \
   configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml \
   https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_101_FPN_DL_s1x/165712116/model_final_844d15.pkl \
   image \
   dp_contour,dp_v,dp_u,dp_segm \
   -v

3. Full logs or other relevant observations: There is a known issue in matplotlib with rounding of floating point figure size: #1, #2. This error leads to unexpected behaviour: while infering Densepose with input image of size (1708, 2560, 3), output image size becomes (1707, 2560,3) The bug appears In DensePoseResultsMplContourVisualizer at lines where

   w, h = map(int, fig.get_size_inches() * fig.get_dpi())

   is executed as

   w, h = map(int, [1707.9999999999998, 2560.0]) #1707.99...8 was obtained just because of a rounding error in matplotlib
   #w, h = 1707, 2560

   Possible workaround: Add additional check if int(h,w)==round(h,w) to context_to_image_bgr function. If it's not, resize resulting image to correct shape. Possible implementation:

   def context_to_image_bgr(self, context):
       fig = context["fig"]
       w, h = map(int, fig.get_size_inches() * fig.get_dpi())
       true_w, true_h = np.round(fig.get_size_inches() * fig.get_dpi()).astype(int)
       canvas = context["canvas"]
       canvas.draw()
       image_1d = np.fromstring(canvas.tostring_rgb(), dtype="uint8")
       image_rgb = image_1d.reshape(h, w, 3)
       image_bgr = image_rgb[:, :, ::-1].copy()
       if (true_w, true_h) == (w,h):
           return image_bgr
       else:
           return cv2.resize(image_bgr, (true_w, true_h))

Environment:

sys.platform            linux
Python                  3.7.10 | packaged by conda-forge | (default, Feb 19 2021, 16:07:37) [GCC 9.3.0]
numpy                   1.19.2
detectron2              0.5 
Compiler                GCC 7.3
CUDA compiler           CUDA 11.0
detectron2 arch flags   3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5, 8.0
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.7.1
PyTorch debug build     False
GPU available           Yes
GPU 0                   Tesla T4 (arch=7.5)
CUDA_HOME               /usr/local/cuda-11.1
Pillow                  8.1.0
torchvision             0.8.2
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5, 8.0
fvcore                  0.1.5.post20211023
iopath                  0.1.8
cv2                     4.5.1
----------------------  ---------------------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.0
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.0.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

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