Increasing segmentation mask resolution

[predrag12]

Hello,

Trying to train Detectron1 Mask R-CNN with R-50-FPN backbone at regular 1333x800 resolution but higher segmentation masks resolution, higher than 28. Training at defaults works fine, but modifying configuration in yaml file MRCNN section, produces either

[E net_async_base.cc:377] [enforce fail at sigmoid_cross_entropy_loss_op.cu:81] X.size() == T.size(). Logit and target must have the same size(636608 vs. 159152)
or
RuntimeError: [enforce fail at context_gpu.cu:415] error == cudaSuccess. 2 vs 0. Error at: /tmp/pytorch/caffe2/core/context_gpu.cu:415: out of memory
even for batch 1.

Could you provide a pointer to description of usage of following fields or pairs of fields that need to be modified together in order to increase the mask resolution?

Thanks.

MRCNN:
CONV_INIT: MSRAFill
DILATION: 1
DIM_REDUCED: 256
RESOLUTION: 28
ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14
ROI_XFORM_SAMPLING_RATIO: 2
THRESH_BINARIZE: 0.5
UPSAMPLE_RATIO: 1
USE_FC_OUTPUT: false
WEIGHT_LOSS_MASK: 1.0

[JulietteMoreau]

Hi,
I encounter the same problem, so I wanted to know if you found the solution as it was more than one year ago...
Thanks.