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

[Chanchalsrm]

Prerequisite

• I have searched the existing and past issues but cannot get the expected help.
• I have read the FAQ documentation but cannot get the expected help.
• 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)))

[Lazy-coder-9527]

pip install albumentations==1.3.1
this maybe work for your problem.

[upupupfei]

pip install albumentations==1.3.1

it works,thank you!