train with 1 gpu

[ahmedzioudi]

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[ahmedzioudi]

2023-07-06 23:45:29,047 - davarocr - INFO - Environment info:

sys.platform: linux
Python: 3.8.10 (default, May 26 2023, 14:05:08) [GCC 9.4.0]
CUDA available: True
GPU 0: NVIDIA GeForce GTX 1650 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.6.r11.6/compiler.31057947_0
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.7.1
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 v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
• 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;-gencode;arch=compute_75,code=sm_75
• CuDNN 7.6.5
• Magma 2.5.2
• Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.2
OpenCV: 4.7.0
MMCV: 1.3.4
MMCV Compiler: GCC 9.4
MMCV CUDA Compiler: 11.6
DAVAROCR: 0.6.0+49614e4

2023-07-06 23:45:29,943 - davarocr - INFO - Distributed training: False
2023-07-06 23:45:30,754 - davarocr - INFO - Config:
model = dict(
type='LGPMA',
pretrained=
'/home/zioudiahmed/Desktop/projet/DAVAR-Lab-OCR/resnet50-19c8e357.pth',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
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=[4, 8, 16],
ratios=[0.05, 0.1, 0.2, 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='LGPMARoIHead',
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=2,
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=False,
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='LPMAMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=2,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
loss_lpma=dict(type='L1Loss', loss_weight=1.0))),
global_seg_head=dict(
type='GPMAMaskHead',
in_channels=256,
conv_out_channels=256,
num_classes=1,
loss_mask=dict(type='DiceLoss', loss_weight=1),
loss_reg=dict(
type='SmoothL1Loss', beta=0.1, loss_weight=0.01, reduction='sum')),
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,
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_post=2000,
nms=dict(type='nms', iou_threshold=0.5),
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=True,
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=2000,
nms_post=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.5),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.1),
max_per_img=1000,
mask_thr_binary=0.5),
postprocess=dict(type='PostLGPMA', refine_bboxes=False)))
train_cfg = None
test_cfg = None
dataset_type = 'TableRcgDataset'
data_root = ''
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='DavarLoadImageFromFile'),
dict(
type='DavarLoadTableAnnotations',
with_bbox=True,
with_enlarge_bbox=True,
with_label=True,
with_poly_mask=True,
with_empty_bbox=True),
dict(
type='DavarResize',
img_scale=[(360, 480), (960, 1080)],
keep_ratio=True,
multiscale_mode='range'),
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='GPMADataGeneration',
lib_name='gpma_data.so',
lib_dir=
'/home/zioudiahmed/Desktop/projet/DAVAR-Lab-OCR/davarocr/davar_table/datasets/pipelines/lib/'
),
dict(type='DavarDefaultFormatBundle'),
dict(
type='DavarCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
]
val_pipeline = [
dict(type='DavarLoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
scale_factor=1.5,
flip=False,
transforms=[
dict(type='DavarResize', keep_ratio=True),
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='DavarDefaultFormatBundle'),
dict(type='DavarCollect', keys=['img'])
])
]
test_pipeline = [
dict(type='DavarLoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
scale_factor=1.5,
flip=False,
transforms=[
dict(type='DavarResize', keep_ratio=True),
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='DavarDefaultFormatBundle'),
dict(type='DavarCollect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='TableRcgDataset',
ann_file=
'/home/zioudiahmed/Desktop/projet/ann_file_train/ann_file_train.json',
img_prefix='/home/zioudiahmed/Desktop/projet/img_prefix_train',
pipeline=[
dict(type='DavarLoadImageFromFile'),
dict(
type='DavarLoadTableAnnotations',
with_bbox=True,
with_enlarge_bbox=True,
with_label=True,
with_poly_mask=True,
with_empty_bbox=True),
dict(
type='DavarResize',
img_scale=[(360, 480), (960, 1080)],
keep_ratio=True,
multiscale_mode='range'),
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='GPMADataGeneration',
lib_name='gpma_data.so',
lib_dir=
'/home/zioudiahmed/Desktop/projet/DAVAR-Lab-OCR/davarocr/davar_table/datasets/pipelines/lib/'
),
dict(type='DavarDefaultFormatBundle'),
dict(
type='DavarCollect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_masks',
'gt_semantic_seg'
])
]),
val=dict(
type='TableRcgDataset',
ann_file=
'/home/zioudiahmed/Desktop/projet/ann_file_valann_file_val.json',
img_prefix='/home/zioudiahmed/Desktop/projetimg_prefix_val',
pipeline=[
dict(type='DavarLoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
scale_factor=1.5,
flip=False,
transforms=[
dict(type='DavarResize', keep_ratio=True),
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='DavarDefaultFormatBundle'),
dict(type='DavarCollect', keys=['img'])
])
]),
test=dict(
type='TableRcgDataset',
ann_file=
'/home/zioudiahmed/Desktop/projet/ann_file_test/ann_file_test.json',
img_prefix='/home/zioudiahmed/Desktop/projet/img_prefix_test',
pipeline=[
dict(type='DavarLoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
scale_factor=1.5,
flip=False,
transforms=[
dict(type='DavarResize', keep_ratio=True),
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='DavarDefaultFormatBundle'),
dict(type='DavarCollect', keys=['img'])
])
],
samples_per_gpu=1))
optimizer = dict(type='SGD', lr=0.01, 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=1000,
warmup_ratio=0.3333333333333333,
step=[6, 10])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(
interval=1, filename_tmpl='checkpoint/maskrcnn-lgpma-pub-e{}.pth')
log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = 'projet'
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 1)
evaluation_metric = 'TEDS'
evaluation = dict(
eval_func_params=dict(ENLARGE_ANN_BBOXES=True, IOU_CONSTRAINT=0.5),
metric='TEDS',
by_epoch=True,
interval=1,
eval_mode='general',
save_best='TEDS',
rule='greater')

2023-07-06 23:45:31,005 - mmdet - INFO - load model from: /home/zioudiahmed/Desktop/projet/DAVAR-Lab-OCR/resnet50-19c8e357.pth
2023-07-06 23:45:31,005 - mmdet - INFO - Use load_from_local loader
2023-07-06 23:45:31,153 - mmdet - WARNING - The model and loaded state dict do not match exactly

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

Traceback (most recent call last):
File "tools/train.py", line 255, in
main()
File "tools/train.py", line 244, in main
train_model(
File "/home/zioudiahmed/Desktop/projet/DAVAR-Lab-OCR/davarocr/davar_common/apis/train.py", line 80, in train_model
model = MMDistributedDataParallel(
File "/home/zioudiahmed/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 379, in init
self.process_group = _get_default_group()
File "/home/zioudiahmed/.local/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 286, in _get_default_group
raise RuntimeError("Default process group has not been initialized, "
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.