NotImplementedError: Got 5D input, but bilinear mode needs 4D input error while training with custom dataset

[Nishantha8]

hello @gkioxari,
I am trying train with my custom dataset with coco format Json. I have modified the dataloader with respect to my dataset. I am getting the following error while training.
[01/24 15:40:57 d2.engine.train_loop]: Starting training from iteration 0 /home/nishantha/test/meshrcnn/meshrcnn/structures/voxel.py:68: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(*, bool as_tuple) (Triggered internally at /opt/conda/conda-bld/pytorch_1595629395347/work/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero() /home/nishantha/test/meshrcnn/meshrcnn/structures/voxel.py:68: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(*, bool as_tuple) (Triggered internally at /opt/conda/conda-bld/pytorch_1595629395347/work/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero() /home/nishantha/test/meshrcnn/meshrcnn/structures/voxel.py:68: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(*, bool as_tuple) (Triggered internally at /opt/conda/conda-bld/pytorch_1595629395347/work/torch/csrc/utils/python_arg_parser.cpp:766.) item = item.nonzero() /home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:217: UserWarning: This overload of nonzero is deprecated: nonzero() Consider using one of the following signatures instead: nonzero(*, bool as_tuple) (Triggered internally at /opt/conda/conda-bld/pytorch_1595629395347/work/torch/csrc/utils/python_arg_parser.cpp:766.) num_fg = fg_inds.nonzero().numel() /home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py:3118: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. warnings.warn("Default upsampling behavior when mode={} is changed " ERROR [01/24 15:41:00 d2.engine.train_loop]: Exception during training: Traceback (most recent call last): File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/train_loop.py", line 134, in train self.run_step() File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/defaults.py", line 423, in run_step self._trainer.run_step() File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/train_loop.py", line 228, in run_step loss_dict = self.model(data) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 166, in forward _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/roi_heads.py", line 153, in forward losses.update(self._forward_mask(features, proposals)) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/roi_heads.py", line 243, in _forward_mask loss_mask, target_masks = mask_rcnn_loss(mask_logits, proposals) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/mask_head.py", line 42, in mask_rcnn_loss gt_masks_per_image = batch_crop_masks_within_box( File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 59, in batch_crop_masks_within_box results = [crop_mask_within_box(mask, box, mask_side_len) for mask, box in zip(masks, boxes)] File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 59, in <listcomp> results = [crop_mask_within_box(mask, box, mask_side_len) for mask, box in zip(masks, boxes)] File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 31, in crop_mask_within_box cropped_mask = F.interpolate(cropped_mask, size=(mask_size, mask_size), mode="bilinear") File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 3169, in interpolate raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input") NotImplementedError: Got 5D input, but bilinear mode needs 4D input [01/24 15:41:00 d2.engine.hooks]: Total training time: 0:00:02 (0:00:00 on hooks) [01/24 15:41:00 d2.utils.events]: iter: 0 lr: N/A max_mem: 2901M Traceback (most recent call last): File "tools/train_net.py", line 102, in <module> launch( File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/launch.py", line 62, in launch main_func(*args) File "tools/train_net.py", line 96, in main return trainer.train() File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/defaults.py", line 413, in train super().train(self.start_iter, self.max_iter) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/train_loop.py", line 134, in train self.run_step() File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/defaults.py", line 423, in run_step self._trainer.run_step() File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/engine/train_loop.py", line 228, in run_step loss_dict = self.model(data) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 166, in forward _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 722, in _call_impl result = self.forward(*input, **kwargs) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/roi_heads.py", line 153, in forward losses.update(self._forward_mask(features, proposals)) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/roi_heads.py", line 243, in _forward_mask loss_mask, target_masks = mask_rcnn_loss(mask_logits, proposals) File "/home/nishantha/test/meshrcnn/meshrcnn/modeling/roi_heads/mask_head.py", line 42, in mask_rcnn_loss gt_masks_per_image = batch_crop_masks_within_box( File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 59, in batch_crop_masks_within_box results = [crop_mask_within_box(mask, box, mask_side_len) for mask, box in zip(masks, boxes)] File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 59, in <listcomp> results = [crop_mask_within_box(mask, box, mask_side_len) for mask, box in zip(masks, boxes)] File "/home/nishantha/test/meshrcnn/meshrcnn/structures/mask.py", line 31, in crop_mask_within_box cropped_mask = F.interpolate(cropped_mask, size=(mask_size, mask_size), mode="bilinear") File "/home/nishantha/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py", line 3169, in interpolate raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input") NotImplementedError: Got 5D input, but bilinear mode needs 4D input

Error is caused by line 31 of mask.py (cropped_mask = F.interpolate(cropped_mask, size=(mask_size, mask_size), mode="bilinear"). My custom dataset contains masks of the object similar to that of Pix3d dataset. Can you please help me resolve this error?