Jagged edges in the output the masks

[vanga]

Hi,

Config: mask_rcnn_R_101_FPN_3x_deform.yaml checkpoint: output_3x_transfiner_r101_deform.pth

All other settings are defaults from the yaml config.

I am doing prediction like this (I used the demo.py script as wel and the results are same). I don't see any errors/warning in the logs

im = cv2.imread(src_img_path)
predictor = DefaultPredictor(cfg)
outputs = predictor(im)

I see that the edges of the masks are not smooth even for the sample images in the repo. Is this expected? I am askng this because when I look at the gifs in the readme, they seem to have smooth edges. so, I was expecting smooth edges.

[lkeab]

Hi, have this line triggered during the inference process? You can try to increase the dilation kernel range to reduce the artifacts. This is the result of the woman running on my local machine.

[vanga]

Hi @lkeab without the background removed, these edges are not noticeable. I don't see the jagged edges even with my inference, atleast they are not noticeable this way I think.

I simply do this to remove the background, so I don't think it is an issue with the background removal process.

from PIL import Image
img = Image.open(src_img_path)

np_img = np.copy(np.asarray(img))
neg_mask = ~mask
np_img[neg_mask] = 255
plt.imshow(np_img)

I wasn't setting VIS_PERIOD if it is not the default. I have set it now and I can confirm that the code is going to dilating step. Though, I am not sure if the output is any different.. I also tried with 7x7 kernel.. not much improvement.

I see below logs during inference, not sure if they are of any significance

fpn input shapes: {'res2': ShapeSpec(channels=256, height=None, width=None, stride=4), 'res3': ShapeSpec(channels=512, height=None, width=None, stride=8), 'res4': ShapeSpec(channels=1024, height=None, width=None, stride=16), 'res5': ShapeSpec(channels=2048, height=None, width=None, stride=32)}
/home/ubuntu/vangap/transfiner/detectron2/structures/image_list.py:99: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/home/ubuntu/miniconda3/envs/transfiner/lib/python3.7/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
dilating---------------------------------------
/home/ubuntu/vangap/transfiner/detectron2/modeling/roi_heads/mask_head.py:60: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)

[lkeab]

I did't observe this warning when running my code.

[vanga]

Could there be an issue with the pytorch/cuda versions? I am using all the latest versions, and on A100.

[lkeab]

Not sure. I am using the NVIDIA TITAN RTX, Pytorch 1.7.1 and Cuda 11.0.

[vanga]

If you don't mind, is it possible to provide mask object for one of these images(and details on which model and setttings are used) or provide an option in huggingspaces to download it?

I would like to compare it with what I am seeing and confirm if it's an env issue or something else.

[lkeab]

Hi, I am currently out for a traveling. I think one validation method is to that you report your AP results for our pretrained model (for example r50-fpn-1x) running on your machine. If it is exactly the same, then I think it can exclude the environment factors.

[anl13]

Hi @lkeab without the background removed, these edges are not noticeable. I don't see the jagged edges even with my inference, atleast they are not noticeable this way I think.

I simply do this to remove the background, so I don't think it is an issue with the background removal process.

from PIL import Image
img = Image.open(src_img_path)

np_img = np.copy(np.asarray(img))
neg_mask = ~mask
np_img[neg_mask] = 255
plt.imshow(np_img)

I wasn't setting VIS_PERIOD if it is not the default. I have set it now and I can confirm that the code is going to dilating step. Though, I am not sure if the output is any different.. I also tried with 7x7 kernel.. not much improvement.

I see below logs during inference, not sure if they are of any significance

fpn input shapes: {'res2': ShapeSpec(channels=256, height=None, width=None, stride=4), 'res3': ShapeSpec(channels=512, height=None, width=None, stride=8), 'res4': ShapeSpec(channels=1024, height=None, width=None, stride=16), 'res5': ShapeSpec(channels=2048, height=None, width=None, stride=32)}
/home/ubuntu/vangap/transfiner/detectron2/structures/image_list.py:99: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  max_size = (max_size + (stride - 1)) // stride * stride
/home/ubuntu/miniconda3/envs/transfiner/lib/python3.7/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
dilating---------------------------------------
/home/ubuntu/vangap/transfiner/detectron2/modeling/roi_heads/mask_head.py:60: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)

I encountered the same warnings! My env setting: ubuntu 20.04, python 3.7.13, pytorch 1.11.0, cuda 11.3.

[vanga]

@anl13 Do you also see the jagged edges?

[anl13]

@vanga Yes, I observed exactly the same results. In fact, I have removed these warnings by replacing a//b with torch.div(a, b, rounding_mode='floor'). However, there is no help. See result below. By the way, my GPU for test is Nvidia Titan X.

[vanga]

@lkeab Can we keep this open until thee is a closure? considering it is not just me?

w.r.t checking AP results, could you please share some pointers on how I can do that?

[lkeab]

Hi, for checking AP results, you can report the inference results on coco val set using our provided pretrained model, such as R50-FPN-1x or 3X. I will also look into this to see whether can help fix it. It's not that noticeable when drawing masks directly on images.

[lkeab]

Hi, we also updated the code. Please try the latest version to see whether it helps or not. These are the results obtained by me.

[anl13]

Here are my results with updated code. There are improvements compared with previous code. I test three pretrained models for human segmentation.

model: output_3x_transfiner_r101_deform.pth:

model: output_3x_transfiner_r101.pth:

model: output_3x_transfiner_r50.pth: