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Not running #12

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linwei0763 opened this issue Aug 2, 2023 · 3 comments
Open

Not running #12

linwei0763 opened this issue Aug 2, 2023 · 3 comments

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@linwei0763
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Thank you for your wonderful work!

I am running td3d on my dataset, which only includes one class. I have add necessary modification to the original code, to my best knowledge.

It seems to run normally. However, the training just won't start, showed as the following. The GPU memory is occupied but the GPU is not working (CUDA is available). Instead, the CPU is running. Could you please help me to find the problem? Thank you!

2023-08-03 05:56:47,461 - mmdet - INFO - Start running, host: root@autodl-container-8ce5118fae-93ac1f8e, work_dir: /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class
2023-08-03 05:56:47,461 - 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 ) EmptyCacheHook
(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
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

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

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

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook

after_val_epoch:
(NORMAL ) EmptyCacheHook
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2023-08-03 05:56:47,461 - mmdet - INFO - workflow: [('train', 1)], max: 33 epochs
2023-08-03 05:56:47,461 - mmdet - INFO - Checkpoints will be saved to /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class by HardDiskBackend.

(I waited for half an hour and there is no further log.)
@linwei0763
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Author

Here is the log.

2023-08-03 05:56:37,714 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.8.17 (default, Jul 5 2023, 21:04:15) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA A40
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.1+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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • 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 -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=1.9.1, 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: 0.10.1+cu111
OpenCV: 4.8.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.1
MMDetection: 2.24.1
MMSegmentation: 0.24.1
MMDetection3D: 1.0.0rc3+fd4b4d4
spconv2.0: False

2023-08-03 05:56:38,730 - mmdet - INFO - Distributed training: False
2023-08-03 05:56:39,700 - mmdet - INFO - Config:
voxel_size = 0.02
padding = 0.08
n_points = 100000
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
model = dict(
type='TD3DInstanceSegmentor',
voxel_size=0.02,
backbone=dict(
type='MinkResNet',
in_channels=3,
depth=34,
norm='batch',
return_stem=True,
stride=1),
neck=dict(
type='NgfcTinySegmentationNeck',
in_channels=(64, 128, 256, 512),
out_channels=128),
head=dict(
type='TD3DInstanceHead',
in_channels=128,
n_reg_outs=6,
n_classes=13,
n_levels=4,
padding=0.08,
voxel_size=0.02,
unet=dict(type='MinkUNet14B', in_channels=32, out_channels=14, D=3),
first_assigner=dict(
type='S3DISAssigner',
top_pts_threshold=6,
label2level=[3, 3, 3, 3, 2, 2, 2, 2, 1, 2, 2, 1, 1]),
second_assigner=dict(type='MaxIoU3DAssigner', threshold=0.25),
roi_extractor=dict(
type='Mink3DRoIExtractor',
voxel_size=0.02,
padding=0.08,
min_pts_threshold=10)),
train_cfg=dict(num_rois=1),
test_cfg=dict(
nms_pre=100, iou_thr=0.2, score_thr=0.15, binary_score_thr=0.2))
optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[28, 32])
runner = dict(type='EpochBasedRunner', max_epochs=33)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
checkpoint_config = dict(interval=1, max_keep_ckpts=50)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/td3d_is_s3dis-3d-5class'
load_from = None
resume_from = None
workflow = [('train', 1)]
dataset_type = 'S3DISInstanceSegDataset'
data_root = './data/s3dis/'
train_area = [2]
test_area = 1
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D', with_mask_3d=True, with_seg_3d=True),
dict(type='PointSample', num_points=100000),
dict(
type='PointSegClassMappingV2',
valid_cat_ids=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12),
max_cat_id=13),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(type='BboxRecalculation'),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
'door', 'table', 'chair', 'sofa', 'bookcase', 'board',
'clutter')),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('ceiling', 'floor', 'wall', 'beam', 'column',
'window', 'door', 'table', 'chair', 'sofa',
'bookcase', 'board', 'clutter'),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=6,
train=dict(
type='RepeatDataset',
times=13,
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_2.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_mask_3d=True,
with_seg_3d=True),
dict(type='PointSample', num_points=100000),
dict(
type='PointSegClassMappingV2',
valid_cat_ids=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12),
max_cat_id=13),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(type='BboxRecalculation'),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('ceiling', 'floor', 'wall', 'beam',
'column', 'window', 'door', 'table',
'chair', 'sofa', 'bookcase', 'board',
'clutter')),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_semantic_mask', 'pts_instance_mask'
])
],
filter_empty_gt=True,
classes=('ceiling', 'floor', 'wall', 'beam', 'column',
'window', 'door', 'table', 'chair', 'sofa',
'bookcase', 'board', 'clutter'),
box_type_3d='Depth')
],
separate_eval=False)),
val=dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_1.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('ceiling', 'floor', 'wall', 'beam',
'column', 'window', 'door', 'table',
'chair', 'sofa', 'bookcase', 'board',
'clutter'),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
],
filter_empty_gt=False,
classes=('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
'door', 'table', 'chair', 'sofa', 'bookcase', 'board',
'clutter'),
test_mode=True,
box_type_3d='Depth'),
test=dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_1.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('ceiling', 'floor', 'wall', 'beam',
'column', 'window', 'door', 'table',
'chair', 'sofa', 'bookcase', 'board',
'clutter'),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
],
filter_empty_gt=False,
classes=('ceiling', 'floor', 'wall', 'beam', 'column', 'window',
'door', 'table', 'chair', 'sofa', 'bookcase', 'board',
'clutter'),
test_mode=True,
box_type_3d='Depth'))
gpu_ids = [0]

2023-08-03 05:56:39,701 - mmdet - INFO - Set random seed to 0, deterministic: False
Name of parameter - Initialization information

backbone.conv1.kernel - torch.Size([27, 3, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.1.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.1.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.2.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.2.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv1.kernel - torch.Size([27, 64, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.1.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.1.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.2.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.2.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.3.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.3.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv1.kernel - torch.Size([27, 128, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.0.kernel - torch.Size([1, 128, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.1.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.1.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.2.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.2.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.3.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.3.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.4.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.4.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.5.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.5.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv1.kernel - torch.Size([27, 256, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.0.kernel - torch.Size([1, 256, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.downsample.1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv1.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.1.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.1.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv1.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.2.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.2.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.0.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_0.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.0.kernel - torch.Size([27, 64, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.0.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.0.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.0.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_2.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.0.kernel - torch.Size([27, 256, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.0.kernel - torch.Size([27, 512, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.0.kernel - torch.Size([27, 512, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_3.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.0.kernel - torch.Size([27, 64, 32]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.conv_32_ch.1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv0p1s1.kernel - torch.Size([125, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv1p1s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv1.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv2.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv2p2s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv1.kernel - torch.Size([27, 32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.0.kernel - torch.Size([32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv3p4s2.kernel - torch.Size([8, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.0.kernel - torch.Size([64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv4p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.0.kernel - torch.Size([128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr4p16s2.kernel - torch.Size([8, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv1.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.0.kernel - torch.Size([256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr5p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv1.kernel - torch.Size([27, 192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.0.kernel - torch.Size([192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr6p4s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr7p2s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.kernel - torch.Size([128, 14]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.bias - torch.Size([1, 14]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.kernel - torch.Size([128, 6]):
Initialized by user-defined init_weights in TD3DInstanceHead

head.reg_conv.bias - torch.Size([1, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.kernel - torch.Size([128, 13]):
Initialized by user-defined init_weights in TD3DInstanceHead

head.cls_conv.bias - torch.Size([1, 13]):
Initialized by user-defined init_weights in TD3DInstanceHead
Name of parameter - Initialization information

backbone.conv1.kernel - torch.Size([27, 3, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.0.kernel - torch.Size([1, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv1.kernel - torch.Size([27, 256, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.0.kernel - torch.Size([1, 256, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv1.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv1.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.0.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.0.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.0.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.0.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.0.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.0.kernel - torch.Size([27, 512, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.0.kernel - torch.Size([27, 512, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.0.kernel - torch.Size([27, 64, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv0p1s1.kernel - torch.Size([125, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv1p1s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv1.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv2.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv2p2s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv1.kernel - torch.Size([27, 32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.0.kernel - torch.Size([32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv3p4s2.kernel - torch.Size([8, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.0.kernel - torch.Size([64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv4p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.0.kernel - torch.Size([128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr4p16s2.kernel - torch.Size([8, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv1.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.0.kernel - torch.Size([256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr5p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv1.kernel - torch.Size([27, 192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.0.kernel - torch.Size([192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr6p4s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr7p2s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.kernel - torch.Size([128, 14]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.bias - torch.Size([1, 14]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.kernel - torch.Size([128, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.bias - torch.Size([1, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.kernel - torch.Size([128, 13]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.bias - torch.Size([1, 13]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor
2023-08-03 05:56:40,775 - mmdet - INFO - Model:
TD3DInstanceSegmentor(
(backbone): MinkResNet(
(conv1): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(maxpool): MinkowskiMaxPooling(kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(layer1): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(3): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(3): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(4): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(5): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
)
(neck): NgfcTinySegmentationNeck(
(lateral_block_0): Sequential(
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_0): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_1): Sequential(
(0): MinkowskiConvolutionTranspose(in=128, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(lateral_block_1): Sequential(
(0): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_1): Sequential(
(0): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_2): Sequential(
(0): MinkowskiConvolutionTranspose(in=256, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(lateral_block_2): Sequential(
(0): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_2): Sequential(
(0): MinkowskiConvolution(in=256, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_3): Sequential(
(0): MinkowskiConvolutionTranspose(in=512, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_3): Sequential(
(0): MinkowskiConvolution(in=512, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(upsample_st_4): Sequential(
(0): MinkowskiConvolutionTranspose(in=128, out=64, kernel_size=[3, 3, 3], stride=[4, 4, 4], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(conv_32_ch): Sequential(
(0): MinkowskiConvolution(in=64, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
)
(head): TD3DInstanceHead(
(unet): MinkUNet14B(
(conv0p1s1): MinkowskiConvolution(in=32, out=32, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
(bn0): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1p1s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block1): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(conv2p2s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block2): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=32, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=32, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(conv3p4s2): MinkowskiConvolution(in=64, out=64, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn3): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block3): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(conv4p8s2): MinkowskiConvolution(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block4): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr4p16s2): MinkowskiConvolutionTranspose(in=256, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block5): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr5p8s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr5): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block6): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=192, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=192, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr6p4s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr6): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block7): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=160, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=160, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr7p2s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr7): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block8): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=160, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=160, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(final): MinkowskiConvolution(in=128, out=14, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(relu): MinkowskiReLU()
)
(reg_loss): SmoothL1Loss()
(bbox_loss): AxisAlignedIoULoss()
(cls_loss): FocalLoss()
(inst_loss): CrossEntropyLoss(avg_non_ignore=False)
(reg_conv): MinkowskiConvolution(in=128, out=6, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(cls_conv): MinkowskiConvolution(in=128, out=13, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
)
)
2023-08-03 05:56:47,461 - mmdet - INFO - Start running, host: root@autodl-container-8ce5118fae-93ac1f8e, work_dir: /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class
2023-08-03 05:56:47,461 - 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 ) EmptyCacheHook
(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
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

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

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

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook

after_val_epoch:
(NORMAL ) EmptyCacheHook
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2023-08-03 05:56:47,461 - mmdet - INFO - workflow: [('train', 1)], max: 33 epochs
2023-08-03 05:56:47,461 - mmdet - INFO - Checkpoints will be saved to /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class by HardDiskBackend.

@linwei0763
Copy link
Author

tried another running after more modification to the code. Still won't run.

2023-08-03 06:30:49,865 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.8.17 (default, Jul 5 2023, 21:04:15) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA A40
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.9.1+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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • 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 -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=1.9.1, 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: 0.10.1+cu111
OpenCV: 4.8.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.1
MMDetection: 2.24.1
MMSegmentation: 0.24.1
MMDetection3D: 1.0.0rc3+fd4b4d4
spconv2.0: False

2023-08-03 06:30:50,213 - mmdet - INFO - Distributed training: False
2023-08-03 06:30:50,429 - mmdet - INFO - Config:
voxel_size = 0.02
padding = 0.08
n_points = 100000
class_names = ('floor', )
model = dict(
type='TD3DInstanceSegmentor',
voxel_size=0.02,
backbone=dict(
type='MinkResNet',
in_channels=3,
depth=34,
norm='batch',
return_stem=True,
stride=1),
neck=dict(
type='NgfcTinySegmentationNeck',
in_channels=(64, 128, 256, 512),
out_channels=128),
head=dict(
type='TD3DInstanceHead',
in_channels=128,
n_reg_outs=6,
n_classes=1,
n_levels=4,
padding=0.08,
voxel_size=0.02,
unet=dict(type='MinkUNet14B', in_channels=32, out_channels=2, D=3),
first_assigner=dict(
type='S3DISAssigner', top_pts_threshold=6, label2level=[3]),
second_assigner=dict(type='MaxIoU3DAssigner', threshold=0.25),
roi_extractor=dict(
type='Mink3DRoIExtractor',
voxel_size=0.02,
padding=0.08,
min_pts_threshold=10)),
train_cfg=dict(num_rois=1),
test_cfg=dict(
nms_pre=100, iou_thr=0.2, score_thr=0.15, binary_score_thr=0.2))
optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[28, 32])
runner = dict(type='EpochBasedRunner', max_epochs=33)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)]
checkpoint_config = dict(interval=1, max_keep_ckpts=50)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/td3d_is_s3dis-3d-5class'
load_from = None
resume_from = None
workflow = [('train', 1)]
dataset_type = 'S3DISInstanceSegDataset'
data_root = './data/s3dis/'
train_area = [2]
test_area = 1
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D', with_mask_3d=True, with_seg_3d=True),
dict(type='PointSample', num_points=100000),
dict(type='PointSegClassMappingV2', valid_cat_ids=(0, ), max_cat_id=13),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(type='BboxRecalculation'),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=('floor', )),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
'pts_instance_mask'
])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('floor', ),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=6,
train=dict(
type='RepeatDataset',
times=13,
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_2.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
with_mask_3d=True,
with_seg_3d=True),
dict(type='PointSample', num_points=100000),
dict(
type='PointSegClassMappingV2',
valid_cat_ids=(0, ),
max_cat_id=13),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[0.95, 1.05],
translation_std=[0.1, 0.1, 0.1],
shift_height=False),
dict(type='BboxRecalculation'),
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('floor', )),
dict(
type='Collect3D',
keys=[
'points', 'gt_bboxes_3d', 'gt_labels_3d',
'pts_semantic_mask', 'pts_instance_mask'
])
],
filter_empty_gt=True,
classes=('floor', ),
box_type_3d='Depth')
],
separate_eval=False)),
val=dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_1.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('floor', ),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
],
filter_empty_gt=False,
classes=('floor', ),
test_mode=True,
box_type_3d='Depth'),
test=dict(
type='S3DISInstanceSegDataset',
data_root='./data/s3dis/',
ann_file='./data/s3dis/s3dis_infos_Area_1.pkl',
pipeline=[
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(type='NormalizePointsColor', color_mean=None),
dict(
type='DefaultFormatBundle3D',
class_names=('floor', ),
with_label=False),
dict(type='Collect3D', keys=['points'])
])
],
filter_empty_gt=False,
classes=('floor', ),
test_mode=True,
box_type_3d='Depth'))
gpu_ids = [0]

2023-08-03 06:30:50,429 - mmdet - INFO - Set random seed to 0, deterministic: False
Name of parameter - Initialization information

backbone.conv1.kernel - torch.Size([27, 3, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.1.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.1.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv1.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.2.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv2.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer1.2.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv1.kernel - torch.Size([27, 64, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.1.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.1.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.2.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.2.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv1.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.3.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv2.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer2.3.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv1.kernel - torch.Size([27, 128, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.0.kernel - torch.Size([1, 128, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.1.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.1.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.2.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.2.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.3.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.3.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.4.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.4.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv1.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.5.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv2.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer3.5.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv1.kernel - torch.Size([27, 256, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.0.kernel - torch.Size([1, 256, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.0.downsample.1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv1.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.1.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.1.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv1.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.2.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv2.kernel - torch.Size([27, 512, 512]):
Initialized by user-defined init_weights in MinkResNet

backbone.layer4.2.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.0.kernel - torch.Size([27, 64, 64]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_0.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.0.kernel - torch.Size([27, 64, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.0.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.0.kernel - torch.Size([27, 128, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.0.kernel - torch.Size([27, 256, 256]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.lateral_block_2.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.0.kernel - torch.Size([27, 256, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.0.kernel - torch.Size([27, 512, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.0.kernel - torch.Size([27, 512, 128]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.out_block_3.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.0.kernel - torch.Size([27, 64, 32]):
Initialized by user-defined init_weights in NgfcTinySegmentationNeck

neck.conv_32_ch.1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv0p1s1.kernel - torch.Size([125, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv1p1s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv1.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv2.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv2p2s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv1.kernel - torch.Size([27, 32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.0.kernel - torch.Size([32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv3p4s2.kernel - torch.Size([8, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.0.kernel - torch.Size([64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv4p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.0.kernel - torch.Size([128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr4p16s2.kernel - torch.Size([8, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv1.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.0.kernel - torch.Size([256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr5p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv1.kernel - torch.Size([27, 192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.0.kernel - torch.Size([192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr6p4s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr7p2s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.kernel - torch.Size([128, 2]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.bias - torch.Size([1, 2]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.kernel - torch.Size([128, 6]):
Initialized by user-defined init_weights in TD3DInstanceHead

head.reg_conv.bias - torch.Size([1, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.kernel - torch.Size([128, 1]):
Initialized by user-defined init_weights in TD3DInstanceHead

head.cls_conv.bias - torch.Size([1, 1]):
Initialized by user-defined init_weights in TD3DInstanceHead
Name of parameter - Initialization information

backbone.conv1.kernel - torch.Size([27, 3, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.0.kernel - torch.Size([1, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.1.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv1.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer1.2.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.0.kernel - torch.Size([1, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.1.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.2.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv1.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer2.3.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.0.kernel - torch.Size([1, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.1.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.2.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.3.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.4.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv1.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer3.5.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv1.kernel - torch.Size([27, 256, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.0.kernel - torch.Size([1, 256, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.0.downsample.1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv1.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.1.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv1.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm1.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.conv2.kernel - torch.Size([27, 512, 512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.weight - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

backbone.layer4.2.norm2.bn.bias - torch.Size([512]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.0.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_0.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.0.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_0.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_1.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.0.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.0.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_1.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.0.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.lateral_block_2.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.0.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_2.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.0.kernel - torch.Size([27, 512, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.up_block_3.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.0.kernel - torch.Size([27, 512, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.out_block_3.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.0.kernel - torch.Size([27, 128, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.upsample_st_4.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.0.kernel - torch.Size([27, 64, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

neck.conv_32_ch.1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv0p1s1.kernel - torch.Size([125, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn0.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv1p1s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv1.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm1.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.conv2.kernel - torch.Size([27, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block1.0.norm2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv2p2s2.kernel - torch.Size([8, 32, 32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.weight - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn2.bn.bias - torch.Size([32]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv1.kernel - torch.Size([27, 32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.conv2.kernel - torch.Size([27, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.norm2.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.0.kernel - torch.Size([32, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block2.0.downsample.1.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv3p4s2.kernel - torch.Size([8, 64, 64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.weight - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn3.bn.bias - torch.Size([64]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv1.kernel - torch.Size([27, 64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.0.kernel - torch.Size([64, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block3.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.conv4p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bn4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv1.kernel - torch.Size([27, 128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.conv2.kernel - torch.Size([27, 256, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.norm2.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.0.kernel - torch.Size([128, 256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.weight - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block4.0.downsample.1.bn.bias - torch.Size([256]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr4p16s2.kernel - torch.Size([8, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr4.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv1.kernel - torch.Size([27, 256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.0.kernel - torch.Size([256, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block5.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr5p8s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr5.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv1.kernel - torch.Size([27, 192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.0.kernel - torch.Size([192, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block6.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr6p4s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr6.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block7.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.convtr7p2s2.kernel - torch.Size([8, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.bntr7.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv1.kernel - torch.Size([27, 160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.conv2.kernel - torch.Size([27, 128, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.norm2.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.0.kernel - torch.Size([160, 128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.weight - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.block8.0.downsample.1.bn.bias - torch.Size([128]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.kernel - torch.Size([128, 2]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.unet.final.bias - torch.Size([1, 2]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.kernel - torch.Size([128, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.reg_conv.bias - torch.Size([1, 6]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.kernel - torch.Size([128, 1]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor

head.cls_conv.bias - torch.Size([1, 1]):
The value is the same before and after calling init_weights of TD3DInstanceSegmentor
2023-08-03 06:30:51,427 - mmdet - INFO - Model:
TD3DInstanceSegmentor(
(backbone): MinkResNet(
(conv1): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(maxpool): MinkowskiMaxPooling(kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(layer1): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(3): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(3): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(4): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(5): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
(2): BasicBlock(
(conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
)
(neck): NgfcTinySegmentationNeck(
(lateral_block_0): Sequential(
(0): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_0): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_1): Sequential(
(0): MinkowskiConvolutionTranspose(in=128, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(lateral_block_1): Sequential(
(0): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_1): Sequential(
(0): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_2): Sequential(
(0): MinkowskiConvolutionTranspose(in=256, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(lateral_block_2): Sequential(
(0): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_2): Sequential(
(0): MinkowskiConvolution(in=256, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(up_block_3): Sequential(
(0): MinkowskiConvolutionTranspose(in=512, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(out_block_3): Sequential(
(0): MinkowskiConvolution(in=512, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(upsample_st_4): Sequential(
(0): MinkowskiConvolutionTranspose(in=128, out=64, kernel_size=[3, 3, 3], stride=[4, 4, 4], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
(conv_32_ch): Sequential(
(0): MinkowskiConvolution(in=64, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MinkowskiReLU()
)
)
(head): TD3DInstanceHead(
(unet): MinkUNet14B(
(conv0p1s1): MinkowskiConvolution(in=32, out=32, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
(bn0): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv1p1s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block1): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
)
)
(conv2p2s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block2): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=32, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=32, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(conv3p4s2): MinkowskiConvolution(in=64, out=64, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn3): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block3): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(conv4p8s2): MinkowskiConvolution(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bn4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block4): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr4p16s2): MinkowskiConvolutionTranspose(in=256, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block5): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=256, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr5p8s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr5): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block6): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=192, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=192, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr6p4s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr6): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block7): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=160, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=160, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(convtr7p2s2): MinkowskiConvolutionTranspose(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
(bntr7): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(block8): Sequential(
(0): BasicBlock(
(conv1): MinkowskiConvolution(in=160, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
(norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): MinkowskiReLU()
(downsample): Sequential(
(0): MinkowskiConvolution(in=160, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(final): MinkowskiConvolution(in=128, out=2, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(relu): MinkowskiReLU()
)
(reg_loss): SmoothL1Loss()
(bbox_loss): AxisAlignedIoULoss()
(cls_loss): FocalLoss()
(inst_loss): CrossEntropyLoss(avg_non_ignore=False)
(reg_conv): MinkowskiConvolution(in=128, out=6, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
(cls_conv): MinkowskiConvolution(in=128, out=1, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
)
)
2023-08-03 06:30:54,769 - mmdet - INFO - Start running, host: root@autodl-container-8ce5118fae-93ac1f8e, work_dir: /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class
2023-08-03 06:30:54,769 - 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 ) EmptyCacheHook
(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
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

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

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

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(NORMAL ) EmptyCacheHook
(LOW ) IterTimerHook

after_val_epoch:
(NORMAL ) EmptyCacheHook
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2023-08-03 06:30:54,769 - mmdet - INFO - workflow: [('train', 1)], max: 33 epochs
2023-08-03 06:30:54,769 - mmdet - INFO - Checkpoints will be saved to /root/autodl-tmp/td3d/work_dirs/td3d_is_s3dis-3d-5class by HardDiskBackend.

@linwei0763
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I try to provide as much as information. Here is the status when I terminated the training. It seems that it was loading the data, but took quite a long time.

File "tools/train.py',line 263,in
main()File "tools/train.py", line 252, in main
train model(
File "/root/autodl-tmp/td3d/mmdet3d/apis/train.py”, line 344, in train modeltrain detector(File "/root/autodl-tmp/td3d/mmdet3d/apis/train.py”, line 319, in train detectorrunner.run(data loaders, cfg.workflow)File "/root/miniconda3/envs/td3d/lib/python3.8/site packages/mmcv/runner/epoch based runner.py", line 136, in runepoch runner(data loaders[i],**kwargs)File "/root/miniconda3/envs/td3d/lib/python3.8/site packages/mmcv/runner/epoch based runner.py, line 49,in trainfor i. data batch in enumerate(self.data loader) :line 521, in nextFile "/root/miniconda3/envs/td3d/lib/python3.8/site packages/torch/utilslataloader.py
data= self. next data()File"/root/miniconda3/envs/td3d/lib/python3.8/site packagestorch/utils/data/dataloader.py1186,in next datalineidx,data = self. get data()File"/root/miniconda3/envs/td3d/lib/python3.8/site packages/1152.inget datatorch/utis/data/dataloader.pyine
success, data = self. try get data()File "/root/miniconda3/envs/td3d/lib/python3.8/site packages/torch/utils/data/dataloader.py, line 990, in try get datadata = self. data queue.get(timeout=timeout)File "/root/miniconda3/envs/td3d/lib/python3.8/multiprocessing/queues.py”, line 107, in getif not self. poll(timeout):File "/root/miniconda3/envs/td3d/lib/python3.8/multiprocessing/connection.py" line 257, in pollreturn self. poll(timeout)File "/root/miniconda3/envs/td3d/lib/python3.8/multiprocessing/connection.py",line 424,in polwait([self], timeout)5File "/root/miniconda3/envs/td3d/lib/python3.8/multiprocessing/connection.py", line 931, in waitready = selector.select(timeout)File "/root/miniconda3/envs/td3d/lib/python3.8/selectors.py”line 415, in selectfd event list= self. selector.poll(timeout)KeyboardInterrupt

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