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20230210_150232.log
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2023-02-10 15:02:34,336 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: None
GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)
PyTorch: 1.12.1+cu116
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- 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 v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.6
- 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 8.3.2 (built against CUDA 11.5)
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.13.1+cu116
OpenCV: 4.7.0
MMCV: 1.7.0
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.6
MMDetection: 2.28.1+
------------------------------------------------------------
2023-02-10 15:02:34,644 - mmdet - INFO - Distributed training: False
2023-02-10 15:02:34,956 - mmdet - INFO - Config:
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 = 'checkpoints/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
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,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
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='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
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=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type='CocoDataset',
ann_file='./data/balloon/train/annotation_coco.json',
img_prefix='./data/balloon/train/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
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='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
],
classes=('balloon', ))),
val=dict(
type='CocoDataset',
ann_file='./data/balloon/val/annotation_coco.json',
img_prefix='./data/balloon/val/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
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=('balloon', )),
test=dict(
type='CocoDataset',
ann_file='./data/balloon/val/annotation_coco.json',
img_prefix='./data/balloon/val/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
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=('balloon', )))
evaluation = dict(interval=1, metric=['bbox', 'segm'])
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[9, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
model = dict(
type='MaskRCNN',
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',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
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='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
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=1,
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='L1Loss', 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='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=1,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, 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,
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=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
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,
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=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5)))
classes = ('balloon', )
work_dir = './work_dirs/mask_rcnn_r50_fpn_mstrain-poly_3x_balloon'
auto_resume = False
gpu_ids = range(0, 1)
2023-02-10 15:02:34,989 - mmdet - INFO - Set random seed to 1772798473, deterministic: False
2023-02-10 15:02:35,665 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2023-02-10 15:02:36,801 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-02-10 15:02:36,819 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2023-02-10 15:02:36,823 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
Name of parameter - Initialization information
backbone.conv1.weight - torch.Size([64, 3, 7, 7]):
PretrainedInit: load from torchvision://resnet50
backbone.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.downsample.1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.0.downsample.1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.1.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer1.2.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.downsample.1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.0.downsample.1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.1.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.2.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer2.3.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.downsample.1.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.0.downsample.1.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.1.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.2.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.3.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.4.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer3.5.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.downsample.1.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.0.downsample.1.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.1.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
backbone.layer4.2.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50
neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.lateral_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0
neck.fpn_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_conv.bias - torch.Size([256]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_cls.bias - torch.Size([3]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]):
NormalInit: mean=0, std=0.01, bias=0
rpn_head.rpn_reg.bias - torch.Size([12]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.weight - torch.Size([2, 1024]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_cls.bias - torch.Size([2]):
NormalInit: mean=0, std=0.01, bias=0
roi_head.bbox_head.fc_reg.weight - torch.Size([4, 1024]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.fc_reg.bias - torch.Size([4]):
NormalInit: mean=0, std=0.001, bias=0
roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.bbox_head.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=uniform, bias=0
roi_head.mask_head.convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined `init_weights` in ConvModule
roi_head.mask_head.convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
roi_head.mask_head.convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined `init_weights` in ConvModule
roi_head.mask_head.convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
roi_head.mask_head.convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined `init_weights` in ConvModule
roi_head.mask_head.convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
roi_head.mask_head.convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined `init_weights` in ConvModule
roi_head.mask_head.convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of MaskRCNN
roi_head.mask_head.upsample.weight - torch.Size([256, 256, 2, 2]):
Initialized by user-defined `init_weights` in FCNMaskHead
roi_head.mask_head.upsample.bias - torch.Size([256]):
Initialized by user-defined `init_weights` in FCNMaskHead
roi_head.mask_head.conv_logits.weight - torch.Size([1, 256, 1, 1]):
Initialized by user-defined `init_weights` in FCNMaskHead
roi_head.mask_head.conv_logits.bias - torch.Size([1]):
Initialized by user-defined `init_weights` in FCNMaskHead
2023-02-10 15:02:40,567 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2023-02-10 15:02:40,631 - mmdet - INFO - load checkpoint from local path: checkpoints/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
2023-02-10 15:02:41,298 - mmdet - WARNING - The model and loaded state dict do not match exactly
size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).
size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).
size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.mask_head.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 256, 1, 1]).
size mismatch for roi_head.mask_head.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).
2023-02-10 15:02:41,300 - mmdet - INFO - Start running, host: scz0a5r@g0012, work_dir: /data/run01/scz0a5r/second/work_dirs/mask_rcnn_r50_fpn_mstrain-poly_3x_balloon
2023-02-10 15:02:41,301 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) 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
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
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2023-02-10 15:02:41,301 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
2023-02-10 15:02:41,301 - mmdet - INFO - Checkpoints will be saved to /data/run01/scz0a5r/second/work_dirs/mask_rcnn_r50_fpn_mstrain-poly_3x_balloon by HardDiskBackend.
2023-02-10 15:03:04,294 - mmdet - INFO - Epoch [1][50/92] lr: 1.978e-03, eta: 0:08:03, time: 0.459, data_time: 0.060, memory: 3498, loss_rpn_cls: 0.0380, loss_rpn_bbox: 0.0136, loss_cls: 0.2766, acc: 86.7773, loss_bbox: 0.3344, loss_mask: 0.2506, loss: 0.9133
2023-02-10 15:03:11,609 - mmdet - INFO - Saving checkpoint at 1 epochs
2023-02-10 15:03:13,790 - mmdet - INFO - Evaluating bbox...
2023-02-10 15:03:13,852 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.716
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.899
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.851
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.623
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.779
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.748
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.748
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.748
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.692
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.808
2023-02-10 15:03:13,852 - mmdet - INFO - Evaluating segm...
2023-02-10 15:03:13,902 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.798
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.879
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.879
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.745