Prerequisite

[X] I have searched the existing and past issues but cannot get the expected help.
[X] I have read the FAQ documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version.

🐞 Describe the bug

I am trying the replicate mmyolo instance segmentation using balloon data set as per the document (https://mmyolo.readthedocs.io/en/latest/get_started/15_minutes_instance_segmentation.html#) I have followed all the instruction as per the document. After execution "import os

os.system('python tools/train.py configs/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_balloon_instance.py')"

I am getting this error "ValueError: Key img_path is not in available keys."

The error in details:

06/20 02:47:12 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 06/20 02:47:12 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 06/20 02:47:12 - mmengine - INFO - Checkpoints will be saved to /raid/home/dgxuser3/ChanchalBiswas/mmyolo/work_dirs/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_balloon_instance. /home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/albumentations/core/composition.py:144: UserWarning: Got processor for bboxes, but no transform to process it. self._set_keys() Traceback (most recent call last): File "tools/train.py", line 123, in main() File "tools/train.py", line 119, in main runner.train() File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmengine/runner/runner.py", line 1777, in train model = self.train_loop.run() # type: ignore File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmengine/runner/loops.py", line 96, in run self.run_epoch() File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmengine/runner/loops.py", line 112, in run_epoch for idx, data_batch in enumerate(self.dataloader): File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in next data = self._next_data() File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data return self._process_data(data) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data data.reraise() File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise raise exception ValueError: Caught ValueError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 410, in getitem data = self.prepare_data(idx) File "/raid/home/dgxuser3/ChanchalBiswas/mmyolo/mmyolo/datasets/yolov5_coco.py", line 53, in prepare_data return self.pipeline(data_info) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmengine/dataset/base_dataset.py", line 60, in call data = t(data) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmcv/transforms/base.py", line 12, in call return self.transform(results) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmdet/structures/bbox/box_type.py", line 267, in wrapper return func(self, results) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/mmdet/datasets/transforms/transforms.py", line 1699, in transform results = self.aug(results) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/albumentations/core/composition.py", line 255, in call self._check_args(data) File "/home/dgxuser3/anaconda3/envs/mmyolo2/lib/python3.8/site-packages/albumentations/core/composition.py", line 324, in _check_args raise ValueError(msg) ValueError: Key img_path is not in available keys.

Could you please provide me a solution

Thank you!

Environment

import os os.system('python mmyolo/utils/collect_env.py')

Result 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 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 12.4, V12.4.131 GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 PyTorch: 1.10.1 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: AVX2
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=1.10.1, 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: 0.11.2 OpenCV: 4.10.0 MMEngine: 0.10.4 MMCV: 2.0.1 MMDetection: 3.3.0 MMYOLO: 0.6.0+8c4d9dc

Additional information

Step 1 conda create -n mmyolo2 python=3.8 -y conda activate mmyolo2

If you have GPU

conda install pytorch torchvision -c pytorch

Step 2 Installed jupyter notebook conda jupyterlab

Step 3 Open jupyter notebook

Step 4 Clone mmyolo

!git clone https://github.com/open-mmlab/mmyolo.git !cd mmyolo pip install -r requirements/albu.txt !mim install -v -e .

Step 5 Downloaded the data

import os os.system('python tools/misc/download_dataset.py --dataset-name balloon --save-dir data/balloon --unzip --delete')

Step 6 Converted to coco

import os os.system('python ./tools/dataset_converters/balloon2coco.py')

Step 7 train yolov5

import os os.system('python tools/train.py configs/yolov5/ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_balloon_instance.py')

Step 8

Change in ins_seg/yolov5_ins_s-v61_syncbn_fast_8xb16-300e_balloon_instance.py

base = './yolov5_ins_s-v61_syncbn_fast_8xb16-300e_coco_instance.py' # noqa

data_root = 'data/balloon/balloon/'

Path of train annotation file

train_ann_file = 'train.json' train_data_prefix = 'train/' # Prefix of train image path

Path of val annotation file

val_ann_file = 'val.json' val_data_prefix = 'val/' # Prefix of val image path metainfo = { 'classes': ('balloon', ), 'palette': [ (220, 20, 60), ] } num_classes = 1

train_batch_size_per_gpu = 4 train_num_workers = 2 log_interval = 1 ##################### train_dataloader = dict( batch_size=train_batch_size_per_gpu, num_workers=train_num_workers, dataset=dict( data_root=data_root, metainfo=metainfo, data_prefix=dict(img=train_data_prefix), ann_file=train_ann_file)) val_dataloader = dict( dataset=dict( data_root=data_root, metainfo=metainfo, data_prefix=dict(img=val_data_prefix), ann_file=val_ann_file)) test_dataloader = val_dataloader val_evaluator = dict(ann_file=data_root + val_ann_file) test_evaluator = val_evaluator default_hooks = dict(logger=dict(interval=log_interval)) #####################

model = dict(bbox_head=dict(head_module=dict(num_classes=num_classes)))

open-mmlab / mmyolo

MMYOLO for yolov5 instance segmentation on balloon dataset getting this error "ValueError: Key img_path is not in available keys." #1018