Description
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 have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
Branch
main branch https://github.com/open-mmlab/mmocr
Environment
sys.platform: linux
Python: 3.10.7 (main, Nov 24 2022, 19:45:47) [GCC 12.2.0]
CUDA available: True
numpy_random_seed: 2147483648
GPU 0,1: GeForce RTX 3090
GPU 2,3: GeForce RTX 3080 Ti
CUDA_HOME: /usr/local/cuda-11.8
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: x86_64-linux-gnu-gcc (Ubuntu 12.2.0-3ubuntu1) 12.2.0
PyTorch: 2.0.0+cu117
PyTorch compiling details: PyTorch built with:
GCC 9.3
C++ Version: 201703
Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 11.7
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_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
CuDNN 8.5
Magma 2.6.1
Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.1+cu117
OpenCV: 4.7.0
MMEngine: 0.7.3
MMOCR: 1.0.0+964172a
Reproduces the problem - code sample
def add_datasample(self,
name: str,
image: np.ndarray,
data_sample: Optional['TextDetDataSample'] = None,
draw_gt: bool = True,
draw_pred: bool = True,
show: bool = False,
wait_time: int = 0,
pred_score_thr: float = 0.5,
out_file: Optional[str] = None,
step: int = 0) -> None:
"""Draw datasample and save to all backends.
- If GT and prediction are plotted at the same time, they are
displayed in a stitched image where the left image is the
ground truth and the right image is the prediction.
- If ``show`` is True, all storage backends are ignored, and
the images will be displayed in a local window.
- If ``out_file`` is specified, the drawn image will be
saved to ``out_file``. This is usually used when the display
is not available.
Args:
name (str): The image identifier.
image (np.ndarray): The image to draw.
data_sample (:obj:`TextSpottingDataSample`, optional):
TextDetDataSample which contains gt and prediction. Defaults
to None.
draw_gt (bool): Whether to draw GT TextDetDataSample.
Defaults to True.
draw_pred (bool): Whether to draw Predicted TextDetDataSample.
Defaults to True.
show (bool): Whether to display the drawn image. Default to False.
wait_time (float): The interval of show (s). Defaults to 0.
out_file (str): Path to output file. Defaults to None.
pred_score_thr (float): The threshold to visualize the bboxes
and masks. Defaults to 0.3.
step (int): Global step value to record. Defaults to 0.
"""
cat_images = []
if data_sample is not None:
if draw_gt and 'gt_instances' in data_sample:
gt_bboxes = data_sample.gt_instances.get('bboxes', None)
gt_texts = data_sample.gt_instances.texts
gt_polygons = data_sample.gt_instances.get('polygons', None)
gt_img_data = self._draw_instances(image, gt_bboxes,
gt_polygons, gt_texts)
cat_images.append(gt_img_data)
if draw_pred and 'pred_instances' in data_sample:
pred_instances = data_sample.pred_instances
# pred_instances = pred_instances[
# pred_instances.scores > pred_score_thr].cpu().numpy()
pred_instances = pred_instances[
[i for i, x in enumerate(pred_instances) if any(score > 0.3 for score in x.text_scores)]].cpu().numpy()
pred_bboxes = pred_instances.get('bboxes', None)
pred_texts = pred_instances.texts
pred_polygons = pred_instances.get('polygons', None)
if pred_bboxes is None and pred_polygons is not None:
pred_bboxes = [poly2bbox(poly) for poly in pred_polygons]
pred_bboxes = np.array(pred_bboxes)
if pred_bboxes is not None:
pred_img_data = self._draw_instances(image, pred_bboxes,
pred_polygons, pred_texts)
cat_images.append(pred_img_data)
cat_images = self._cat_image(cat_images, axis=0)
if cat_images is None:
cat_images = image
if show:
self.show(cat_images, win_name=name, wait_time=wait_time)
else:
self.add_image(name, cat_images, step)
if out_file is not None:
mmcv.imwrite(cat_images[..., ::-1], out_file)
self.set_image(cat_images)
return self.get_image()
Reproduces the problem - command or script
from mmocr.apis import TextSpotInferencer
Load models into memory
inferencer = TextSpotInferencer(model='projects/SPTS/config/spts/spts_resnet50_8xb8-200e_icdar2015.py',weights='model/best_generic_hmean.pth')
inferencer('/root/icdar2015/textdet_imgs/test/image_7000.jpg', save_vis=True,return_vis=True)
Reproduces the problem - error message
please see the datastruct in the pred_instances, here is not have the bbox and polygons data, and the type of scores is 'list' not 'tensor'，so i had to change the code to adapter the datastruct.
Additional information
1.the datastruct should be consist with inferring from (det and recog)
2.get the infer result image to visualizer.