evaluation/tiny_benchmark/maskrcnn_benchmark/data/transforms/transforms.py (5 lines):
	- line 398: # TODO: should be replace with other policy to remove most ignore.
	- line 603: # TODO: should be replace with other policy to remove most ignore.
	- line 761: # TODO: must change to insert of image(not union) as crop area ratio
	- line 882: # TODO: should be replace with other policy to remove most ignore.
	- line 985: # TODO: must change to insert of image(not union) as crop area ratio


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/inference.py (4 lines):
	- line 96: # TODO most of this can be made out of the loop for
	- line 98: # TODO:Yang: Not easy to do. Because the numbers of detections are
	- line 127: # TODO very similar to filter_results from PostProcessor
	- line 129: # TODO Yang: solve this issue in the future. No good solution


evaluation/tiny_benchmark/maskrcnn_benchmark/structures/keypoint.py (4 lines):
	- line 10: # FIXME remove check once we have better integration with device
	- line 18: # TODO should I split them?
	- line 129: # TODO this doesn't look great
	- line 153: # TODO make this nicer, this is a direct translation from C2 (but removing the inner loop)


evaluation/tiny_benchmark/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu (3 lines):
	- line 10: // TODO make it in a common file
	- line 155: // TODO remove the dependency on input and use instead its sizes -> save memory
	- line 169: // TODO add more checks


evaluation/tiny_benchmark/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu (2 lines):
	- line 9: // TODO make it in a common file
	- line 301: // TODO remove the dependency on input and use instead its sizes -> save memory


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/locnet/inference.py (2 lines):
	- line 160: # TODO very similar to filter_results from PostProcessor
	- line 162: # TODO Yang: solve this issue in the future. No good solution


evaluation/tiny_benchmark/maskrcnn_benchmark/structures/segmentation_mask.py (2 lines):
	- line 95: # TODO chck if necessary
	- line 134: # TODO add squeeze?


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/gaussian_net/inference.py (2 lines):
	- line 207: # TODO very similar to filter_results from PostProcessor
	- line 209: # TODO Yang: solve this issue in the future. No good solution


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/box_coder.py (2 lines):
	- line 32: TO_REMOVE = 1  # TODO remove
	- line 64: TO_REMOVE = 1  # TODO remove


evaluation/tiny_benchmark/maskrcnn_benchmark/structures/boxlist_ops.py (2 lines):
	- line 42: # TODO maybe add an API for querying the ws / hs
	- line 91: # TODO redundant, remove


models/yolo.py (2 lines):
	- line 108: #         # TODO: size-adaptive?
	- line 156: patch_off = torch.cat((offsets[:, :1], offsets[:, 1:] / r), dim=1)  # TODO: from 4 to 32


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/fcos/inference.py (2 lines):
	- line 138: # TODO very similar to filter_results from PostProcessor
	- line 140: # TODO Yang: solve this issue in the future. No good solution


train.py (2 lines):
	- line 165: if pretrained:  # TODO: freeze
	- line 563: opt.epochs, opt.hyp = opt.epochs * 0 + epochs, hyp  # TODO: epochs


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/cascade_fcos/inference.py (2 lines):
	- line 170: # TODO very similar to filter_results from PostProcessor
	- line 172: # TODO Yang: solve this issue in the future. No good solution


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py (2 lines):
	- line 10: # TODO check if want to return a single BoxList or a composite
	- line 187: # TODO:  Is this JIT compatible?


evaluation/tiny_benchmark/maskrcnn_benchmark/csrc/cuda/nms.cu (2 lines):
	- line 83: THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
	- line 126: // TODO improve this part


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/inference.py (2 lines):
	- line 35: # TODO remove and use only the Keypointer
	- line 111: # TODO do this properly


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/retinanet_fa/box_coder.py (2 lines):
	- line 32: TO_REMOVE = 1  # TODO remove
	- line 64: TO_REMOVE = 1  # TODO remove


evaluation/tiny_benchmark/maskrcnn_benchmark/layers/smooth_l1_loss.py (1 line):
	- line 5: # TODO maybe push this to nn?


evaluation/tiny_benchmark/maskrcnn_benchmark/structures/bounding_box.py (1 line):
	- line 181: # TODO should I filter empty boxes here?


utils/loss.py (1 line):
	- line 270: gain[2:6] = torch.tensor([width, height, width, height], dtype=dtype) / (8 * r)  # TODO: from 4 to 32


evaluation/tiny_benchmark/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu (1 line):
	- line 14: // TODO make it in a common file


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/cascade_fcos/cascade_fcos.py (1 line):
	- line 19: # TODO: Implement the sigmoid version first.


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py (1 line):
	- line 25: # TODO rename x to roi_box_features, if it doesn't increase memory consumption


utils/wandb_logging/wandb_utils.py (1 line):
	- line 245: # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/fcos/fcos.py (1 line):
	- line 19: # TODO: Implement the sigmoid version first.


evaluation/tiny_benchmark/maskrcnn_benchmark/structures/image_list.py (1 line):
	- line 50: # TODO Ideally, just remove this and let me model handle arbitrary


models/replknet.py (1 line):
	- line 20: # TODO more efficient PyTorch implementations of large-kernel convolutions. Pull requests are welcomed.


evaluation/tiny_benchmark/maskrcnn_benchmark/csrc/nms.h (1 line):
	- line 16: // TODO raise error if not compiled with CUDA


evaluation/tiny_benchmark/maskrcnn_benchmark/solver/lr_scheduler.py (1 line):
	- line 7: # FIXME ideally this would be achieved with a CombinedLRScheduler,


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/backbone/fpn.py (1 line):
	- line 69: # TODO use size instead of scale to make it robust to different sizes


evaluation/tiny_benchmark/tools/train_net.py (1 line):
	- line 82: torch.cuda.empty_cache()  # TODO check if it helps


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py (1 line):
	- line 58: # TODO think about a representation of batch of boxes


evaluation/tiny_benchmark/maskrcnn_benchmark/layers/smooth_l1_loss2.py (1 line):
	- line 16: # TODO maybe push this to nn?


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/backbone/resnet.py (1 line):
	- line 305: # TODO: specify init for the above


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/inference.py (1 line):
	- line 161: # TODO resolve this difference and make it consistent. It should be per image,


evaluation/tiny_benchmark/tools/train_test_net.py (1 line):
	- line 115: torch.cuda.empty_cache()  # TODO check if it helps


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py (1 line):
	- line 30: # TODO put the proposals on the CPU, as the representation for the


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/retinanet_fa/retinanet.py (1 line):
	- line 27: # TODO: Implement the sigmoid version first.


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/roi_heads/keypoint_head/loss.py (1 line):
	- line 93: # TODO check if this is the right one, as BELOW_THRESHOLD


evaluation/tiny_benchmark/maskrcnn_benchmark/layers/sigmoid_focal_loss.py (1 line):
	- line 8: # TODO: Use JIT to replace CUDA implementation in the future.


evaluation/tiny_benchmark/maskrcnn_benchmark/data/samplers/distributed.py (1 line):
	- line 3: # FIXME remove this once c10d fixes the bug it has


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/retinanet/retinanet.py (1 line):
	- line 25: # TODO: Implement the sigmoid version first.


evaluation/tiny_benchmark/maskrcnn_benchmark/modeling/rpn/locnet/head.py (1 line):
	- line 14: # TODO: Implement the sigmoid version first.