Training on custom dataset. [Error No such file or directory] for loading train_annotation.pkl file

[kumarv987]

I'm training on a custom dataset. I followed the steps for custom dataset and created a train_annotation.pkl file. I have saved the images and train_annotation.pkl file in data folder. This data folder is located on the main folder of the repository [OBBDetection/data] and inside data folder looks like this:
OBBDetection/data/
--------images/[Location of png files]
--------train_annotation.pkl
When I run this line "!python tools/train.py configs/obb/faster_rcnn_obb/faster_rcnn_obb_r50_fpn_3x_custom.py", I get the following script with an error:
--------------------------------------------------SCRIPT-Starting --------------------------------------------------

2022-05-21 04:49:33,745 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.7.13 (default, Apr 24 2022, 01:04:09) [GCC 7.5.0]
CUDA available: True
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.1.TC455_06.29190527_0
GPU 0: Tesla T4
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.10.0+cu111
PyTorch compiling details: PyTorch built with:

• GCC 7.3
• C++ Version: 201402
• Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 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 11.1
• 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 8.0.5
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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=1.10.0, 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=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.0+cu111
OpenCV: 4.1.2
MMCV: 1.5.1
MMDetection: 2.2.0+unknown
MMDetection Compiler: GCC 7.5
MMDetection CUDA Compiler: 11.1

2022-05-21 04:49:33,746 - mmdet - INFO - Distributed training: False
2022-05-21 04:49:34,029 - mmdet - INFO - Config:
dataset_type = 'CustomDataset'
data_root = '/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadOBBAnnotations',
with_bbox=True,
with_label=True,
obb_as_mask=True),
dict(type='OBBRandomFlip', h_flip_ratio=0.5, v_flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(
type='RandomOBBRotate',
rotate_after_flip=True,
angles=(0, 0),
vert_rate=0.5),
dict(type='Pad', size_divisor=32),
dict(type='Mask2OBB', obb_type='obb'),
dict(type='OBBDefaultFormatBundle'),
dict(
type='OBBCollect',
keys=['img', 'gt_bboxes', 'gt_obboxes', 'gt_labels'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=4,
train=dict(
type='CustomDataset',
ann_file=
'/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/train_annotation.pkl',
img_prefix=
'/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='LoadOBBAnnotations',
with_bbox=True,
with_label=True,
obb_as_mask=True),
dict(type='OBBRandomFlip', h_flip_ratio=0.5, v_flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(
type='RandomOBBRotate',
rotate_after_flip=True,
angles=(0, 0),
vert_rate=0.5),
dict(type='Pad', size_divisor=32),
dict(type='Mask2OBB', obb_type='obb'),
dict(type='OBBDefaultFormatBundle'),
dict(
type='OBBCollect',
keys=['img', 'gt_bboxes', 'gt_obboxes', 'gt_labels'])
]))
evaluation = None
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[24, 33])
total_epochs = 36
checkpoint_config = dict(interval=3)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
model = dict(
type='FasterRCNNOBB',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='OBBStandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='OBBShared2FCBBoxHead',
start_bbox_type='hbb',
end_bbox_type='obb',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1,
bbox_coder=dict(
type='HBB2OBBDeltaXYWHTCoder',
target_means=[0.0, 0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))))
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
gpu_assign_thr=200,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D')),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='obb_nms', iou_thr=0.1),
max_per_img=2000))
work_dir = './work_dirs/faster_rcnn_obb_r50_fpn_3x_custom'
gpu_ids = range(0, 1)

2022-05-21 04:49:34,389 - mmdet - INFO - load model from: torchvision://resnet50
2022-05-21 04:49:34,389 - mmdet - INFO - load checkpoint from torchvision path: torchvision://resnet50
2022-05-21 04:49:34,497 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: fc.weight, fc.bias

ANN_FILE: /content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/train_annotation.pkl
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 66, in build_from_cfg
return obj_cls(**args)
File "/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/mmdet/datasets/custom.py", line 83, in init
self.data_infos = self.load_annotations(self.ann_file)
File "/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/mmdet/datasets/custom.py", line 110, in load_annotations
return mmcv.load(ann_file)
File "/usr/local/lib/python3.7/dist-packages/mmcv/fileio/io.py", line 60, in load
with BytesIO(file_client.get(file)) as f:
File "/usr/local/lib/python3.7/dist-packages/mmcv/fileio/file_client.py", line 1015, in get
return self.client.get(filepath)
File "/usr/local/lib/python3.7/dist-packages/mmcv/fileio/file_client.py", line 535, in get
with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/train_annotation.pkl'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "tools/train.py", line 153, in
main()
File "tools/train.py", line 128, in main
datasets = [build_dataset(cfg.data.train)]
File "/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/mmdet/datasets/builder.py", line 63, in build_dataset
dataset = build_from_cfg(cfg, DATASETS, default_args)
File "/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py", line 69, in build_from_cfg
raise type(e)(f'{obj_cls.name}: {e}')
FileNotFoundError: CustomDataset: [Errno 2] No such file or directory: '/content/drive/MyDrive/Capstone_project_datasets/OBBDetection-master/data/train_annotation.pkl'
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What is possibly causing this error?

[kumarv987]

All good I figured it out