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dpt_vitb_kitti_24e.txt
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dpt_vitb_kitti_24e.txt
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2022-06-05 00:25:59,134 - depth - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.1, V10.1.243
GCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
PyTorch: 1.6.0
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.1
- 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
- CuDNN 7.6.3
- Magma 2.5.2
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,
TorchVision: 0.7.0
OpenCV: 4.5.5
MMCV: 1.3.13
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.1
Depth: 0.0.0+37d5bde
------------------------------------------------------------
2022-06-05 00:25:59,135 - depth - INFO - Distributed training: True
2022-06-05 00:25:59,350 - depth - INFO - Config:
model = dict(
type='DepthEncoderDecoder',
pretrained='nfs/checkpoints/jx_vit_base_p16_224-80ecf9dd.pth',
backbone=dict(
type='VisionTransformer',
img_size=224,
embed_dims=768,
num_layers=12,
num_heads=12,
out_indices=(2, 5, 8, 11),
final_norm=False,
with_cls_token=True,
output_cls_token=True),
decode_head=dict(
type='DPTHead',
in_channels=(768, 768, 768, 768),
channels=256,
embed_dims=768,
post_process_channels=[96, 192, 384, 768],
readout_type='project',
norm_cfg=None,
min_depth=0.001,
max_depth=80,
loss_decode=dict(
type='SigLoss', valid_mask=True, loss_weight=1.0, warm_up=True)),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
dataset_type = 'KITTIDataset'
data_root = 'data/kitti'
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
crop_size = (352, 704)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='DepthLoadAnnotations'),
dict(type='LoadKITTICamIntrinsic'),
dict(type='KBCrop', depth=True),
dict(type='RandomRotate', prob=0.5, degree=2.5),
dict(type='RandomFlip', prob=0.5),
dict(type='RandomCrop', crop_size=(352, 704)),
dict(
type='ColorAug',
prob=0.5,
gamma_range=[0.9, 1.1],
brightness_range=[0.9, 1.1],
color_range=[0.9, 1.1]),
dict(
type='Normalize',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'depth_gt'],
meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'flip', 'flip_direction',
'img_norm_cfg', 'cam_intrinsic'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadKITTICamIntrinsic'),
dict(type='KBCrop', depth=False),
dict(
type='MultiScaleFlipAug',
img_scale=(1216, 352),
flip=True,
flip_direction='horizontal',
transforms=[
dict(type='RandomFlip', direction='horizontal'),
dict(
type='Normalize',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg', 'cam_intrinsic'))
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='KITTIDataset',
data_root='data/kitti',
img_dir='input',
ann_dir='gt_depth',
depth_scale=256,
split='kitti_eigen_train.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='DepthLoadAnnotations'),
dict(type='LoadKITTICamIntrinsic'),
dict(type='KBCrop', depth=True),
dict(type='RandomRotate', prob=0.5, degree=2.5),
dict(type='RandomFlip', prob=0.5),
dict(type='RandomCrop', crop_size=(352, 704)),
dict(
type='ColorAug',
prob=0.5,
gamma_range=[0.9, 1.1],
brightness_range=[0.9, 1.1],
color_range=[0.9, 1.1]),
dict(
type='Normalize',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'depth_gt'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor', 'flip',
'flip_direction', 'img_norm_cfg', 'cam_intrinsic'))
],
garg_crop=True,
eigen_crop=False,
min_depth=0.001,
max_depth=80),
val=dict(
type='KITTIDataset',
data_root='data/kitti',
img_dir='input',
ann_dir='gt_depth',
depth_scale=256,
split='kitti_eigen_test.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadKITTICamIntrinsic'),
dict(type='KBCrop', depth=False),
dict(
type='MultiScaleFlipAug',
img_scale=(1216, 352),
flip=True,
flip_direction='horizontal',
transforms=[
dict(type='RandomFlip', direction='horizontal'),
dict(
type='Normalize',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor',
'flip', 'flip_direction', 'img_norm_cfg',
'cam_intrinsic'))
])
],
garg_crop=True,
eigen_crop=False,
min_depth=0.001,
max_depth=80),
test=dict(
type='KITTIDataset',
data_root='data/kitti',
img_dir='input',
ann_dir='gt_depth',
depth_scale=256,
split='kitti_eigen_test.txt',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadKITTICamIntrinsic'),
dict(type='KBCrop', depth=False),
dict(
type='MultiScaleFlipAug',
img_scale=(1216, 352),
flip=True,
flip_direction='horizontal',
transforms=[
dict(type='RandomFlip', direction='horizontal'),
dict(
type='Normalize',
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
to_rgb=True),
dict(type='ImageToTensor', keys=['img']),
dict(
type='Collect',
keys=['img'],
meta_keys=('filename', 'ori_filename', 'ori_shape',
'img_shape', 'pad_shape', 'scale_factor',
'flip', 'flip_direction', 'img_norm_cfg',
'cam_intrinsic'))
])
],
garg_crop=True,
eigen_crop=False,
min_depth=0.001,
max_depth=80))
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=True),
dict(type='TensorboardImageLoggerHook', by_epoch=True)
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True
max_lr = 0.0001
optimizer = dict(
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.01,
paramwise_cfg=dict(
custom_keys=dict(
pos_embed=dict(decay_mult=0.0),
cls_token=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0),
backbone=dict(lr_mult=0.1))))
lr_config = dict(
policy='OneCycle',
max_lr=0.0001,
div_factor=25,
final_div_factor=100,
by_epoch=False)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
runner = dict(type='EpochBasedRunner', max_epochs=24)
checkpoint_config = dict(by_epoch=True, max_keep_ckpts=2, interval=1)
evaluation = dict(by_epoch=True, interval=1, pre_eval=True)
momentum_config = dict(policy='OneCycle')
work_dir = 'nfs/saves/dpt/kitti'
gpu_ids = range(0, 1)
2022-06-05 00:26:00,312 - depth - INFO - Use load_from_local loader
2022-06-05 00:26:00,748 - depth - WARNING - The model and loaded state dict do not match exactly
unexpected key in source state_dict: ln1.bias, ln1.weight
Name of parameter - Initialization information
backbone.cls_token - torch.Size([1, 1, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.pos_embed - torch.Size([1, 197, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.patch_embed.projection.weight - torch.Size([768, 3, 16, 16]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.patch_embed.projection.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.0.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.1.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.2.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.3.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.4.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.5.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.6.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.7.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.8.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.9.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.10.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ln1.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ln1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.attn.attn.in_proj_weight - torch.Size([2304, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.attn.attn.in_proj_bias - torch.Size([2304]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.attn.attn.out_proj.weight - torch.Size([768, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.attn.attn.out_proj.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ln2.weight - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ln2.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ffn.layers.0.0.weight - torch.Size([3072, 768]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ffn.layers.0.0.bias - torch.Size([3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ffn.layers.1.weight - torch.Size([768, 3072]):
Initialized by user-defined `init_weights` in VisionTransformer
backbone.layers.11.ffn.layers.1.bias - torch.Size([768]):
Initialized by user-defined `init_weights` in VisionTransformer
decode_head.conv_depth.head.0.weight - torch.Size([128, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.conv_depth.head.0.bias - torch.Size([128]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.conv_depth.head.2.weight - torch.Size([32, 128, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.conv_depth.head.2.bias - torch.Size([32]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.conv_depth.head.4.weight - torch.Size([1, 32, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.conv_depth.head.4.bias - torch.Size([1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.0.conv.weight - torch.Size([96, 768, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.0.conv.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.1.conv.weight - torch.Size([192, 768, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.1.conv.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.2.conv.weight - torch.Size([384, 768, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.2.conv.bias - torch.Size([384]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.3.conv.weight - torch.Size([768, 768, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.projects.3.conv.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.0.weight - torch.Size([96, 96, 4, 4]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.0.bias - torch.Size([96]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.1.weight - torch.Size([192, 192, 2, 2]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.1.bias - torch.Size([192]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.3.weight - torch.Size([768, 768, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.resize_layers.3.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.0.0.weight - torch.Size([768, 1536]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.0.0.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.1.0.weight - torch.Size([768, 1536]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.1.0.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.2.0.weight - torch.Size([768, 1536]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.2.0.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.3.0.weight - torch.Size([768, 1536]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.reassemble_blocks.readout_projects.3.0.bias - torch.Size([768]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.convs.0.conv.weight - torch.Size([256, 96, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.convs.1.conv.weight - torch.Size([256, 192, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.convs.2.conv.weight - torch.Size([256, 384, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.convs.3.conv.weight - torch.Size([256, 768, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.0.project.conv.weight - torch.Size([256, 256, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.0.project.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.0.res_conv_unit2.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.0.res_conv_unit2.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.project.conv.weight - torch.Size([256, 256, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.project.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.res_conv_unit1.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.res_conv_unit1.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.res_conv_unit2.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.1.res_conv_unit2.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.project.conv.weight - torch.Size([256, 256, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.project.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.res_conv_unit1.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.res_conv_unit1.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.res_conv_unit2.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.2.res_conv_unit2.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.project.conv.weight - torch.Size([256, 256, 1, 1]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.project.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.res_conv_unit1.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.res_conv_unit1.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.res_conv_unit2.conv1.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.fusion_blocks.3.res_conv_unit2.conv2.conv.weight - torch.Size([256, 256, 3, 3]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
decode_head.project.conv.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined `init_weights` in ConvModule
decode_head.project.conv.bias - torch.Size([256]):
The value is the same before and after calling `init_weights` of DepthEncoderDecoder
2022-06-05 00:26:00,824 - depth - INFO - DepthEncoderDecoder(
(backbone): VisionTransformer(
(patch_embed): PatchEmbed(
(adap_padding): AdaptivePadding()
(projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
)
(drop_after_pos): Dropout(p=0.0, inplace=False)
(layers): ModuleList(
(0): TransformerEncoderLayer(
(ln1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
)
(proj_drop): Dropout(p=0.0, inplace=False)
(dropout_layer): DropPath()
)
(ln2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU()
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU()
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(1): TransformerEncoderLayer(
(ln1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
)
(proj_drop): Dropout(p=0.0, inplace=False)
(dropout_layer): DropPath()
)
(ln2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU()
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU()
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(2): TransformerEncoderLayer(
(ln1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
)
(proj_drop): Dropout(p=0.0, inplace=False)
(dropout_layer): DropPath()
)
(ln2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU()
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU()
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(3): TransformerEncoderLayer(
(ln1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
)
(proj_drop): Dropout(p=0.0, inplace=False)
(dropout_layer): DropPath()
)
(ln2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(ffn): FFN(
(activate): GELU()
(layers): Sequential(
(0): Sequential(
(0): Linear(in_features=768, out_features=3072, bias=True)
(1): GELU()
(2): Dropout(p=0.0, inplace=False)
)
(1): Linear(in_features=3072, out_features=768, bias=True)
(2): Dropout(p=0.0, inplace=False)
)
(dropout_layer): DropPath()
)
)
(4): TransformerEncoderLayer(
(ln1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(attn): MultiheadAttention(
(attn): MultiheadAttention(
(out_proj): _LinearWithBias(in_features=768, out_features=768, bias=True)
)
(proj_drop): Dropout(p=0.0, inplace=False)