BoxInst: High-Performance Instance Segmentation with Box Annotations

Related tags

Deep Learning OpenMMLab-BoxInst
Overview

Introduction

This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performance Instance Segmentation with Box Annotations

License

This project is released under the Apache 2.0 license.

Benchmark and model zoo

BoxInst

Name box AP mask AP log download
BoxInst_MS_R_50_1x 0.390 0.304 log model
BoxInst_MS_R_50_90k 0.388 0.302 log model
BoxInst_MS_R_101_90k 0.410 0.318 - model

Some other methods in MMDetection are also supported.

Getting Started

Our project is totally based on MMCV and MMDetection. Please see get_started.md for the basic usage of MMDetection.

Train

Please see doc to start training. Example,

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh configs/boxinst/boxinst_r50_caffe_fpn_coco_mstrain_1x.py 4

please following linear linear scaling rule to adjust batch size, learning rate and iterations.

Inference and Eval

python tools/test.py configs/boxinst/boxinst_r50_caffe_fpn_coco_mstrain_1x.py work_dirs/boxinst_r50_caffe_fpn_coco_mstrain_1x.py/latest.pth --eval bbox segm

Acknowledgement

  • MMCV: OpenMMLab foundational library for computer vision.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
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Comments
  • bug in get_bitmasks_from_boxes

    bug in get_bitmasks_from_boxes

    If gt_bbox is empty or have empty. https://github.com/wangbo-zhao/OpenMMLab-BoxInst/blob/main/mmdet/models/dense_heads/condinst_head.py get_bitmasks_from_boxes have a bug. In this func, if image have no bbox, this time, 'per_img_gt_bboxes' will be torch.empty(0,4), 'per_im_bitmasks' and 'per_im_bitmasks_full' will be empty list, and torch.stack() will raise 'RuntimeError: stack expects a non-empty TensorList'. In this time, 'torch.cat([image_color_similarity for _ in range(len(per_img_gt_bboxes))' will raise 'RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradNestedTensor, UNKNOWN_TENSOR_TYPE_ID, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].', because 'len(per_img_gt_bboxes)' is 0.

    opened by SongJgit 0
  • onnx bug

    onnx bug

    very fantistic work! But there is still bugs when i convert pth into onnx file, which is follow:

    return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) Traceback (most recent call last): File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/onnx/utils.py", line 689, in _export _model_to_graph(model, args, verbose, input_names, File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/onnx/utils.py", line 458, in _model_to_graph graph, params, torch_out, module = _create_jit_graph(model, args, File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/onnx/utils.py", line 422, in _create_jit_graph graph, torch_out = _trace_and_get_graph_from_model(model, args) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/onnx/utils.py", line 373, in _trace_and_get_graph_from_model torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/jit/_trace.py", line 1160, in _get_trace_graph outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/jit/_trace.py", line 127, in forward graph, out = torch._C._create_graph_by_tracing( File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/jit/_trace.py", line 118, in wrapper outs.append(self.inner(*trace_inputs)) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward result = self.forward(*input, **kwargs) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func return old_func(*args, **kwargs) File "/Users/xt.xie/Documents/workspace/code/mmdetection/mmdet/models/detectors/base.py", line 169, in forward return self.onnx_export(img[0], img_metas[0]) File "/Users/xt.xie/Documents/workspace/code/mmdetection/mmdet/models/detectors/single_stage.py", line 153, in onnx_export outs = self.bbox_head(x) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/Users/xt.xie/opt/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward result = self.forward(*input, **kwargs) TypeError: forward() missing 1 required positional argument: 'top_module'

    opened by Kenneth-X 0
  • Does the training of BoxInst need the

    Does the training of BoxInst need the "segmentation" annotation?

    Hi,

    The BoxInst is a weakly-supervised segmentation method. So I am wondering whether the "segmentation" annotation should be included in your coco-format annotation file?

    opened by flyingfQiu 0
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