[Bug] dev-1.x分支训练det模型，_draw_border_map 函数出现 ValueError: could not broadcast input array from shape into shape

[yilong2001]

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• The bug has not been fixed in the latest version (0.x) or latest version (1.x).

Task

I'm using the official example scripts/configs for the officially supported tasks/models/datasets.

Branch

1.x branch https://github.com/open-mmlab/mmocr/tree/dev-1.x

Environment

环境变量：
sys.platform: linux
Python: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]
CUDA available: True
MUSA available: False
numpy_random_seed: 2147483648
GPU 0: NVIDIA A10
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 12.2, V12.2.91
GCC: gcc (Debian 10.2.1-6) 10.2.1 20210110
PyTorch: 1.10.2
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX512
• CUDA Runtime 11.3
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.2
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.3
OpenCV: 4.9.0
MMEngine: 0.10.3
MMOCR: 1.0.1+1d3b1ca

Reproduces the problem - code sample

{
"metainfo": {
"category": [
{
"id": 0,
"name": "text"
}
],
"dataset_type": "TextDetDataset",
"task_name": "textdet"
},
"data_list": [
{
"sample_idx": 0,
"img_path": "xxx.png",
"height": 842,
"width": 595,
"seg_map": "gt-img-xxx.txt",
"instances": [ 很多 ]
}
]
}

Reproduces the problem - command or script

python tools/train.py configs/textdet/dbnetpp/mmocr_det_myconfig.py

Reproduces the problem - error message

_draw_border_map

错误出现的地方：
canvas[y_min_valid:y_max_valid + 1,
x_min_valid:x_max_valid + 1] = np.fmax(
1 - distance_map[y_min_valid - y_min:y_max_valid - y_max +
height, x_min_valid - x_min:x_max_valid -
x_max + width],
canvas[y_min_valid:y_max_valid + 1,
x_min_valid:x_max_valid + 1])
错误的原因：
y_min_valid - y_min 小于0 而且 绝对值小于 distance_map.shape[1]
x_min_valid - x_min 小于0 而且 绝对值小于 distance_map.shape[0]

---

此问题规避之后，会有新问题：

File "tools/train.py", line 114, in
main()
File "tools/train.py", line 110, in main
runner.train()
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train
model = self.train_loop.run() # type: ignore
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run
self.run_epoch()
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 112, in run_epoch
self.run_iter(idx, data_batch)
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/runner/loops.py", line 128, in run_iter
outputs = self.runner.model.train_step(
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py", line 114, in train_step
losses = self._run_forward(data, mode='loss') # type: ignore
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/mmengine/model/base_model/base_model.py", line 361, in _run_forward
results = self(**data, mode=mode)
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/projects/mmocr/mmocr/models/textdet/detectors/base.py", line 72, in forward
return self.loss(inputs, data_samples)
File "/opt/projects/mmocr/mmocr/models/textdet/detectors/single_stage_text_detector.py", line 76, in loss
return self.det_head.loss(inputs, data_samples)
File "/opt/projects/mmocr/mmocr/models/textdet/heads/db_head.py", line 139, in loss
losses = self.module_loss(outs, batch_data_samples)
File "/home/beeservice/.conda/envs/open-mmlab/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/projects/mmocr/mmocr/models/textdet/module_losses/db_module_loss.py", line 79, in forward
gt_shrinks, gt_shrink_masks, gt_thrs, gt_thr_masks = self.get_targets(
File "/opt/projects/mmocr/mmocr/models/textdet/module_losses/db_module_loss.py", line 237, in get_targets
gt_shrinks = torch.cat(gt_shrinks)
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 722 but got size 720 for tensor number 1 in the list.

Additional information

data_textdet_train = dict(
type="OCRDataset",
data_root=data_root,
ann_file="mmocrdet_anno.json",
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=None,
)