FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/xyz.png'

[Resham-Sundar]

I have searched related issues but cannot get the expected help.

I want to train my custom dataset for instance segmentation using Improved HTC with DB-Swin-L as backbone. But I am facing the above error. Since it is an instance segmentation dataset, I don't have stuffthingmaps. Kindly help me as to how should I go about it.

I get the following upon training on Google colab:

2021-08-03 18:28:25,774 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.7.11 (default, Jul 3 2021, 18:01:19) [GCC 7.5.0]
CUDA available: True
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.0_bu.TC445_37.28845127_0
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.0+cu102
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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 10.2
• 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
• CuDNN 7.6.5
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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 -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=1.9.0, 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: 0.10.0+cu102
OpenCV: 4.1.2
MMCV: 1.3.9
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.0
MMDetection: 2.14.0+900f7bd

2021-08-03 18:28:26,338 - mmdet - INFO - Distributed training: False
2021-08-03 18:28:26,893 - mmdet - INFO - Config:
model = dict(
type='HybridTaskCascade',
pretrained=None,
backbone=dict(
type='CBSwinTransformer',
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
use_checkpoint=False),
neck=dict(
type='CBFPN',
in_channels=[192, 384, 768, 1536],
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='HybridTaskCascadeRoIHead',
interleaved=True,
mask_info_flow=True,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
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)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.05, 0.05, 0.1, 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)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.033, 0.033, 0.067, 0.067]),
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))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=[
dict(
type='HTCMaskHead',
with_conv_res=False,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
],
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semantic_head=dict(
type='FusedSemanticHead',
num_ins=5,
fusion_level=1,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=183,
ignore_label=255,
loss_weight=0.2)),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
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_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=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,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.001,
nms=dict(type='soft_nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
dataset_type = 'COCODataset'
data_root = 'data/coco/'
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='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(
type='Resize',
img_scale=[(1600, 400), (1600, 1400)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', 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='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=0.125),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 1400),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='CocoDataset',
ann_file='data/trainval.json',
img_prefix='data/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(
type='Resize',
img_scale=[(1600, 400), (1600, 1400)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', 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='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=0.125),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_masks',
'gt_semantic_seg'
])
],
seg_prefix='data/coco/stuffthingmaps/train2017/',
classes=('date', 'fig', 'hazelnut')),
val=dict(
type='CocoDataset',
ann_file='data/trainval.json',
img_prefix='data/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 1400),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
classes=('date', 'fig', 'hazelnut')),
test=dict(
type='CocoDataset',
ann_file='data/trainval.json',
img_prefix='data/images/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 1400),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
classes=('date', 'fig', 'hazelnut')))
evaluation = dict(metric=['bbox', 'segm'])
optimizer = dict(
type='AdamW',
lr=5e-05,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'htc_cbv2_swin_large22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.pth'
resume_from = None
workflow = [('train', 1)]
samples_per_gpu = 1
classes = ('date', 'fig', 'hazelnut')
work_dir = './work_dirs/nuts'
gpu_ids = range(0, 1)

/content/CBNetV2/mmdet/core/anchor/builder.py:16: UserWarning: build_anchor_generator would be deprecated soon, please use build_prior_generator
'build_anchor_generator would be deprecated soon, please use '
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
2021-08-03 18:28:38,797 - mmdet - INFO - load checkpoint from htc_cbv2_swin_large22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.pth
2021-08-03 18:28:38,798 - mmdet - INFO - Use load_from_local loader
2021-08-03 18:29:29,361 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for roi_head.bbox_head.0.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.0.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.bbox_head.1.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.1.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.bbox_head.2.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.2.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.mask_head.0.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for roi_head.mask_head.0.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([3]).
size mismatch for roi_head.mask_head.1.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for roi_head.mask_head.1.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([3]).
size mismatch for roi_head.mask_head.2.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for roi_head.mask_head.2.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([3]).
unexpected key in source state_dict: roi_head.bbox_head.0.shared_convs.0.conv.weight, roi_head.bbox_head.0.shared_convs.0.bn.weight, roi_head.bbox_head.0.shared_convs.0.bn.bias, roi_head.bbox_head.0.shared_convs.0.bn.running_mean, roi_head.bbox_head.0.shared_convs.0.bn.running_var, roi_head.bbox_head.0.shared_convs.0.bn.num_batches_tracked, roi_head.bbox_head.0.shared_convs.1.conv.weight, roi_head.bbox_head.0.shared_convs.1.bn.weight, roi_head.bbox_head.0.shared_convs.1.bn.bias, roi_head.bbox_head.0.shared_convs.1.bn.running_mean, roi_head.bbox_head.0.shared_convs.1.bn.running_var, roi_head.bbox_head.0.shared_convs.1.bn.num_batches_tracked, roi_head.bbox_head.0.shared_convs.2.conv.weight, roi_head.bbox_head.0.shared_convs.2.bn.weight, roi_head.bbox_head.0.shared_convs.2.bn.bias, roi_head.bbox_head.0.shared_convs.2.bn.running_mean, roi_head.bbox_head.0.shared_convs.2.bn.running_var, roi_head.bbox_head.0.shared_convs.2.bn.num_batches_tracked, roi_head.bbox_head.0.shared_convs.3.conv.weight, roi_head.bbox_head.0.shared_convs.3.bn.weight, roi_head.bbox_head.0.shared_convs.3.bn.bias, roi_head.bbox_head.0.shared_convs.3.bn.running_mean, roi_head.bbox_head.0.shared_convs.3.bn.running_var, roi_head.bbox_head.0.shared_convs.3.bn.num_batches_tracked, roi_head.bbox_head.1.shared_convs.0.conv.weight, roi_head.bbox_head.1.shared_convs.0.bn.weight, roi_head.bbox_head.1.shared_convs.0.bn.bias, roi_head.bbox_head.1.shared_convs.0.bn.running_mean, roi_head.bbox_head.1.shared_convs.0.bn.running_var, roi_head.bbox_head.1.shared_convs.0.bn.num_batches_tracked, roi_head.bbox_head.1.shared_convs.1.conv.weight, roi_head.bbox_head.1.shared_convs.1.bn.weight, roi_head.bbox_head.1.shared_convs.1.bn.bias, roi_head.bbox_head.1.shared_convs.1.bn.running_mean, roi_head.bbox_head.1.shared_convs.1.bn.running_var, roi_head.bbox_head.1.shared_convs.1.bn.num_batches_tracked, roi_head.bbox_head.1.shared_convs.2.conv.weight, roi_head.bbox_head.1.shared_convs.2.bn.weight, roi_head.bbox_head.1.shared_convs.2.bn.bias, roi_head.bbox_head.1.shared_convs.2.bn.running_mean, roi_head.bbox_head.1.shared_convs.2.bn.running_var, roi_head.bbox_head.1.shared_convs.2.bn.num_batches_tracked, roi_head.bbox_head.1.shared_convs.3.conv.weight, roi_head.bbox_head.1.shared_convs.3.bn.weight, roi_head.bbox_head.1.shared_convs.3.bn.bias, roi_head.bbox_head.1.shared_convs.3.bn.running_mean, roi_head.bbox_head.1.shared_convs.3.bn.running_var, roi_head.bbox_head.1.shared_convs.3.bn.num_batches_tracked, roi_head.bbox_head.2.shared_convs.0.conv.weight, roi_head.bbox_head.2.shared_convs.0.bn.weight, roi_head.bbox_head.2.shared_convs.0.bn.bias, roi_head.bbox_head.2.shared_convs.0.bn.running_mean, roi_head.bbox_head.2.shared_convs.0.bn.running_var, roi_head.bbox_head.2.shared_convs.0.bn.num_batches_tracked, roi_head.bbox_head.2.shared_convs.1.conv.weight, roi_head.bbox_head.2.shared_convs.1.bn.weight, roi_head.bbox_head.2.shared_convs.1.bn.bias, roi_head.bbox_head.2.shared_convs.1.bn.running_mean, roi_head.bbox_head.2.shared_convs.1.bn.running_var, roi_head.bbox_head.2.shared_convs.1.bn.num_batches_tracked, roi_head.bbox_head.2.shared_convs.2.conv.weight, roi_head.bbox_head.2.shared_convs.2.bn.weight, roi_head.bbox_head.2.shared_convs.2.bn.bias, roi_head.bbox_head.2.shared_convs.2.bn.running_mean, roi_head.bbox_head.2.shared_convs.2.bn.running_var, roi_head.bbox_head.2.shared_convs.2.bn.num_batches_tracked, roi_head.bbox_head.2.shared_convs.3.conv.weight, roi_head.bbox_head.2.shared_convs.3.bn.weight, roi_head.bbox_head.2.shared_convs.3.bn.bias, roi_head.bbox_head.2.shared_convs.3.bn.running_mean, roi_head.bbox_head.2.shared_convs.3.bn.running_var, roi_head.bbox_head.2.shared_convs.3.bn.num_batches_tracked

missing keys in source state_dict: roi_head.bbox_head.0.shared_fcs.1.weight, roi_head.bbox_head.0.shared_fcs.1.bias, roi_head.bbox_head.1.shared_fcs.1.weight, roi_head.bbox_head.1.shared_fcs.1.bias, roi_head.bbox_head.2.shared_fcs.1.weight, roi_head.bbox_head.2.shared_fcs.1.bias

2021-08-03 18:29:29,409 - mmdet - INFO - Start running, host: root@d8f8e57ec13b, work_dir: /content/CBNetV2/work_dirs/nuts
2021-08-03 18:29:29,409 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) EvalHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) EvalHook
(LOW ) IterTimerHook

after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch:
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook

2021-08-03 18:29:29,409 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
Traceback (most recent call last):
File "tools/train.py", line 188, in
main()
File "tools/train.py", line 184, in main
meta=meta)
File "/content/CBNetV2/mmdet/apis/train.py", line 185, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
for i, data_batch in enumerate(self.data_loader):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/usr/local/lib/python3.7/dist-packages/torch/_utils.py", line 425, in reraise
raise self.exc_type(msg)
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/content/CBNetV2/mmdet/datasets/custom.py", line 194, in getitem
data = self.prepare_train_img(idx)
File "/content/CBNetV2/mmdet/datasets/custom.py", line 217, in prepare_train_img
return self.pipeline(results)
File "/content/CBNetV2/mmdet/datasets/pipelines/compose.py", line 40, in call
data = t(data)
File "/content/CBNetV2/mmdet/datasets/pipelines/loading.py", line 373, in call
results = self._load_semantic_seg(results)
File "/content/CBNetV2/mmdet/datasets/pipelines/loading.py", line 347, in _load_semantic_seg
img_bytes = self.file_client.get(filename)
File "/usr/local/lib/python3.7/dist-packages/mmcv/fileio/file_client.py", line 306, in get
return self.client.get(filepath)
File "/usr/local/lib/python3.7/dist-packages/mmcv/fileio/file_client.py", line 184, in get
with open(filepath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'data/coco/stuffthingmaps/train2017/11.png'

My config file:
base = '../cbnet/htc_cbv2_swin_large_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.py'

model = dict(
roi_head=dict(
bbox_head=[
dict(
type='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
dict(
type='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
dict(
type='ConvFCBBoxHead',
num_shared_convs=4,
num_shared_fcs=1,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
reg_decoded_bbox=True,
norm_cfg=dict(type='SyncBN', requires_grad=True),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
]
)
)

model = dict(
type='HybridTaskCascade',
pretrained=None,
roi_head=dict(
type='HybridTaskCascadeRoIHead',
interleaved=True,
mask_info_flow=True,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
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)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 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)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
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))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=[
dict(
type='HTCMaskHead',
with_conv_res=False,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=3,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
],
))

dataset_type = 'COCODataset'
classes = ('date','fig','hazelnut',)
data = dict(
train=dict(
img_prefix='data/images/',
classes=classes,
ann_file='data/trainval.json'),
val=dict(
img_prefix='data/images/',
classes=classes,
ann_file='data/trainval.json'),
test=dict(
img_prefix='data/images/',
classes=classes,
ann_file='data/trainval.json'))

load_from = 'htc_cbv2_swin_large22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.pth'

[mangoyuan]

Want to train HTC with DB-Swin-B in custom dataset without instance segmentation masks. Is there any solution?

[yestaehyung]

Want to train HTC with DB-Swin-B in custom dataset without instance segmentation masks. Is there any solution?

I have same problems. (sorry for my english is not good)

In my case i changed htc_cbv2_swin_base_patch4_window7_mstrain_400-1400_adamw_20e_coco.py
there are data root and seg_prefix, you must change these things to your local

[ttrungtin2910]

Want to train HTC with DB-Swin-B in custom dataset without instance segmentation masks. Is there any solution?

Do you have a solution? Please help me