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2024/03/15 14:08:48 - patchstitcher - INFO -
------------------------------------------------------------
System environment:
sys.platform: linux
Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]
CUDA available: True
numpy_random_seed: 621
GPU 0,1,2,3: NVIDIA A100-SXM4-80GB
CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2)
PyTorch: 2.1.2
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.8
- NVCC architecture flags: -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_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.7
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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, USE_ROCM=OFF,
TorchVision: 0.16.2
OpenCV: 4.8.1
MMEngine: 0.10.2
Runtime environment:
cudnn_benchmark: True
mp_cfg: {'mp_start_method': 'forkserver'}
dist_cfg: {'backend': 'nccl'}
seed: 621
Distributed launcher: pytorch
Distributed training: True
GPU number: 4
------------------------------------------------------------
2024/03/15 14:08:49 - patchstitcher - INFO - Config:
collect_input_args = [
'image_lr',
'crops_image_hr',
'depth_gt',
'crop_depths',
'bboxs',
'image_hr',
]
convert_syncbn = True
debug = False
env_cfg = dict(
cudnn_benchmark=True,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='forkserver'))
find_unused_parameters = True
general_dataloader = dict(
batch_size=1,
dataset=dict(
dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'),
num_workers=2)
launcher = 'pytorch'
log_name = 'fine_pretrain'
max_depth = 80
min_depth = 0.001
model = dict(
coarse_branch=dict(
attractor_alpha=1000,
attractor_gamma=2,
attractor_kind='mean',
attractor_type='inv',
aug=True,
bin_centers_type='softplus',
bin_embedding_dim=128,
clip_grad=0.1,
dataset='nyu',
depth_anything=True,
distributed=True,
do_resize=False,
force_keep_ar=True,
freeze_midas_bn=True,
gpu='NULL',
img_size=[
392,
518,
],
inverse_midas=False,
log_images_every=0.1,
max_depth=80,
max_temp=50.0,
max_translation=100,
memory_efficient=True,
midas_model_type='vitl',
min_depth=0.001,
min_temp=0.0212,
model='zoedepth',
n_attractors=[
16,
8,
4,
1,
],
n_bins=64,
name='ZoeDepth',
notes='',
output_distribution='logbinomial',
prefetch=False,
pretrained_resource='local::./work_dir/DepthAnything_vitl.pt',
print_losses=False,
project='ZoeDepth',
random_crop=False,
random_translate=False,
root='.',
save_dir='',
shared_dict='NULL',
tags='',
train_midas=True,
translate_prob=0.2,
type='DA-ZoeDepth',
uid='NULL',
use_amp=False,
use_pretrained_midas=True,
use_shared_dict=False,
validate_every=0.25,
version_name='v1',
workers=16),
fine_branch=dict(
attractor_alpha=1000,
attractor_gamma=2,
attractor_kind='mean',
attractor_type='inv',
aug=True,
bin_centers_type='softplus',
bin_embedding_dim=128,
clip_grad=0.1,
dataset='nyu',
depth_anything=True,
distributed=True,
do_resize=False,
force_keep_ar=True,
freeze_midas_bn=True,
gpu='NULL',
img_size=[
392,
518,
],
inverse_midas=False,
log_images_every=0.1,
max_depth=80,
max_temp=50.0,
max_translation=100,
memory_efficient=True,
midas_model_type='vitl',
min_depth=0.001,
min_temp=0.0212,
model='zoedepth',
n_attractors=[
16,
8,
4,
1,
],
n_bins=64,
name='ZoeDepth',
notes='',
output_distribution='logbinomial',
prefetch=False,
pretrained_resource='local::./work_dir/DepthAnything_vitl.pt',
print_losses=False,
project='ZoeDepth',
random_crop=False,
random_translate=False,
root='.',
save_dir='',
shared_dict='NULL',
tags='',
train_midas=True,
translate_prob=0.2,
type='DA-ZoeDepth',
uid='NULL',
use_amp=False,
use_pretrained_midas=True,
use_shared_dict=False,
validate_every=0.25,
version_name='v1',
workers=16),
max_depth=80,
min_depth=0.001,
patch_process_shape=(
392,
518,
),
sigloss=dict(type='SILogLoss'),
target='fine',
type='BaselinePretrain')
optim_wrapper = dict(
clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'),
optimizer=dict(lr=4e-06, type='AdamW', weight_decay=0.01),
paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict()))
param_scheduler = dict(
base_momentum=0.85,
cycle_momentum=True,
div_factor=1,
final_div_factor=10000,
max_momentum=0.95,
pct_start=0.5,
three_phase=False)
project = 'patchfusion'
tags = [
'fine',
'da',
'vitl',
]
test_in_dataloader = dict(
batch_size=1,
dataset=dict(
data_root='./data/u4k',
max_depth=80,
min_depth=0.001,
mode='infer',
split='./data/u4k/splits/test.txt',
transform_cfg=dict(network_process_size=[
384,
512,
]),
type='UnrealStereo4kDataset'),
num_workers=2)
test_out_dataloader = dict(
batch_size=1,
dataset=dict(
data_root='./data/u4k',
max_depth=80,
min_depth=0.001,
mode='infer',
split='./data/u4k/splits/test_out.txt',
transform_cfg=dict(network_process_size=[
384,
512,
]),
type='UnrealStereo4kDataset'),
num_workers=2)
train_cfg = dict(
eval_start=0,
log_interval=100,
max_epochs=24,
save_checkpoint_interval=24,
train_log_img_interval=500,
val_interval=2,
val_log_img_interval=50,
val_type='epoch_base')
train_dataloader = dict(
batch_size=4,
dataset=dict(
data_root='./data/u4k',
max_depth=80,
min_depth=0.001,
mode='train',
resize_mode='depth-anything',
split='./data/u4k/splits/train.txt',
transform_cfg=dict(
degree=1.0, network_process_size=[
392,
518,
], random_crop=True),
type='UnrealStereo4kDataset'),
num_workers=4)
val_dataloader = dict(
batch_size=1,
dataset=dict(
data_root='./data/u4k',
max_depth=80,
min_depth=0.001,
mode='infer',
resize_mode='depth-anything',
split='./data/u4k/splits/val.txt',
transform_cfg=dict(degree=1.0, network_process_size=[
392,
518,
]),
type='UnrealStereo4kDataset'),
num_workers=2)
work_dir = './work_dir/depthanything_vitl_u4k/fine_pretrain'
zoe_depth_config = dict(
attractor_alpha=1000,
attractor_gamma=2,
attractor_kind='mean',
attractor_type='inv',
aug=True,
bin_centers_type='softplus',
bin_embedding_dim=128,
clip_grad=0.1,
dataset='nyu',
depth_anything=True,
distributed=True,
do_resize=False,
force_keep_ar=True,
freeze_midas_bn=True,
gpu='NULL',
img_size=[
392,
518,
],
inverse_midas=False,
log_images_every=0.1,
max_depth=80,
max_temp=50.0,
max_translation=100,
memory_efficient=True,
midas_model_type='vitl',
min_depth=0.001,
min_temp=0.0212,
model='zoedepth',
n_attractors=[
16,
8,
4,
1,
],
n_bins=64,
name='ZoeDepth',
notes='',
output_distribution='logbinomial',
prefetch=False,
pretrained_resource='local::./work_dir/DepthAnything_vitl.pt',
print_losses=False,
project='ZoeDepth',
random_crop=False,
random_translate=False,
root='.',
save_dir='',
shared_dict='NULL',
tags='',
train_midas=True,
translate_prob=0.2,
type='DA-ZoeDepth',
uid='NULL',
use_amp=False,
use_pretrained_midas=True,
use_shared_dict=False,
validate_every=0.25,
version_name='v1',
workers=16)
2024/03/15 14:08:53 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitl.pt
2024/03/15 14:08:53 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 14:08:53 - patchstitcher - INFO - DistributedDataParallel(
(module): BaselinePretrain(
(fine_branch): ZoeDepth(
(core): DepthAnythingCore(
(core): DPT_DINOv2(
(pretrained): DinoVisionTransformer(
(patch_embed): PatchEmbed(
(proj): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14))
(norm): Identity()
)
(blocks): ModuleList(
(0-23): 24 x NestedTensorBlock(
(norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
(attn): MemEffAttention(
(qkv): Linear(in_features=1024, out_features=3072, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=1024, out_features=1024, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(ls1): LayerScale()
(drop_path1): Identity()
(norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(act): GELU(approximate='none')
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
(drop): Dropout(p=0.0, inplace=False)
)
(ls2): LayerScale()
(drop_path2): Identity()
)
)
(norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
(head): Identity()
)
(depth_head): DPTHead(
(projects): ModuleList(
(0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
(1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
(2-3): 2 x Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))
)
(resize_layers): ModuleList(
(0): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4))
(1): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2))
(2): Identity()
(3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
(scratch): Module(
(layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(layer3_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(layer4_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(refinenet1): FeatureFusionBlock(
(out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(resConfUnit1): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(resConfUnit2): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(refinenet2): FeatureFusionBlock(
(out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(resConfUnit1): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(resConfUnit2): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(refinenet3): FeatureFusionBlock(
(out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(resConfUnit1): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(resConfUnit2): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(refinenet4): FeatureFusionBlock(
(out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(resConfUnit1): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(resConfUnit2): ResidualConvUnit(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(activation): ReLU()
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(skip_add): FloatFunctional(
(activation_post_process): Identity()
)
)
(output_conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(output_conv2): Sequential(
(0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1))
(3): ReLU(inplace=True)
(4): Identity()
)
)
)
)
)
(conv2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(seed_bin_regressor): SeedBinRegressorUnnormed(
(_net): Sequential(
(0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
(seed_projector): Projector(
(_net): Sequential(
(0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(projectors): ModuleList(
(0-3): 4 x Projector(
(_net): Sequential(
(0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
)
(attractors): ModuleList(
(0): AttractorLayerUnnormed(
(_net): Sequential(
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
(1): AttractorLayerUnnormed(
(_net): Sequential(
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
(2): AttractorLayerUnnormed(
(_net): Sequential(
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
(3): AttractorLayerUnnormed(
(_net): Sequential(
(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
)
(conditional_log_binomial): ConditionalLogBinomial(
(log_binomial_transform): LogBinomial()
(mlp): Sequential(
(0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1))
(1): GELU(approximate='none')
(2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1))
(3): Softplus(beta=1, threshold=20)
)
)
)
(sigloss): SILogLoss()
)
)
2024/03/15 14:09:00 - patchstitcher - INFO - successfully init trainer
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls1.gamma
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls2.gamma
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.bias
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2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 14:11:58 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 3.4369797706604004 - fine_loss: 3.4369797706604004
2024/03/15 14:13:53 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7487813234329224 - fine_loss: 1.7487813234329224
2024/03/15 14:15:47 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.9658074378967285 - fine_loss: 2.9658074378967285
2024/03/15 14:17:39 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7623008489608765 - fine_loss: 1.7623008489608765
2024/03/15 14:21:19 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.5338351726531982 - fine_loss: 2.5338351726531982
2024/03/15 14:23:13 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.949808120727539 - fine_loss: 1.949808120727539
2024/03/15 14:25:07 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5897963047027588 - fine_loss: 1.5897963047027588
2024/03/15 14:26:57 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5241825580596924 - fine_loss: 1.5241825580596924
2024/03/15 14:29:02 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+
| 0.7777531 | 0.9691491 | 0.9920921 | 0.1407966 | 2.1086142 | 0.064992 | 0.1937843 | 15.8287334 | 0.3172734 | 1.5069758 |
+-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+
2024/03/15 14:30:59 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.046777367591858 - fine_loss: 1.046777367591858
2024/03/15 14:32:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0578508377075195 - fine_loss: 1.0578508377075195
2024/03/15 14:34:43 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1272225379943848 - fine_loss: 1.1272225379943848
2024/03/15 14:36:33 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4212095737457275 - fine_loss: 1.4212095737457275
2024/03/15 14:39:52 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.175830125808716 - fine_loss: 2.175830125808716
2024/03/15 14:41:46 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.6945712566375732 - fine_loss: 1.6945712566375732
2024/03/15 14:43:36 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6684590578079224 - fine_loss: 1.6684590578079224
2024/03/15 14:45:27 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.978624165058136 - fine_loss: 0.978624165058136
2024/03/15 14:47:24 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.8861725 | 0.9834373 | 0.9954867 | 0.1163805 | 1.6259623 | 0.0493756 | 0.1501408 | 13.2967868 | 0.2082237 | 1.2041435 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 14:49:20 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0877785682678223 - fine_loss: 1.0877785682678223
2024/03/15 14:51:13 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.627789855003357 - fine_loss: 1.627789855003357
2024/03/15 14:53:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.852139949798584 - fine_loss: 1.852139949798584
2024/03/15 14:54:56 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4405653774738312 - fine_loss: 0.4405653774738312
2024/03/15 14:58:15 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5338048934936523 - fine_loss: 1.5338048934936523
2024/03/15 15:00:08 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6242114901542664 - fine_loss: 0.6242114901542664
2024/03/15 15:01:57 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.961182177066803 - fine_loss: 0.961182177066803
2024/03/15 15:03:48 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0484611988067627 - fine_loss: 1.0484611988067627
2024/03/15 15:05:46 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+
| 0.9134527 | 0.9875949 | 0.9960728 | 0.1049193 | 1.7395931 | 0.0450357 | 0.1404951 | 12.8880463 | 0.238056 | 1.314327 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+
2024/03/15 15:07:43 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7090848684310913 - fine_loss: 0.7090848684310913
2024/03/15 15:09:36 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1976611614227295 - fine_loss: 2.1976611614227295
2024/03/15 15:11:25 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8840272426605225 - fine_loss: 0.8840272426605225
2024/03/15 15:13:16 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7640607357025146 - fine_loss: 0.7640607357025146
2024/03/15 15:16:35 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2251319885253906 - fine_loss: 1.2251319885253906
2024/03/15 15:18:29 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.059991717338562 - fine_loss: 1.059991717338562
2024/03/15 15:20:19 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.0797297954559326 - fine_loss: 2.0797297954559326
2024/03/15 15:22:08 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7019829750061035 - fine_loss: 1.7019829750061035
2024/03/15 15:24:01 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+
| 0.9341925 | 0.9901051 | 0.996222 | 0.0896415 | 1.4864388 | 0.0385839 | 0.1228082 | 11.3385688 | 0.174791 | 1.2037814 |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+
2024/03/15 15:25:57 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8242141604423523 - fine_loss: 0.8242141604423523
2024/03/15 15:27:48 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.5683438777923584 - fine_loss: 2.5683438777923584
2024/03/15 15:29:37 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3079029321670532 - fine_loss: 1.3079029321670532
2024/03/15 15:31:28 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6061334609985352 - fine_loss: 1.6061334609985352
2024/03/15 15:34:46 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8605632185935974 - fine_loss: 0.8605632185935974
2024/03/15 15:36:39 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0743176937103271 - fine_loss: 1.0743176937103271
2024/03/15 15:38:29 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.351250410079956 - fine_loss: 1.351250410079956
2024/03/15 15:40:23 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0148663520812988 - fine_loss: 1.0148663520812988
2024/03/15 15:42:18 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+
| 0.9474117 | 0.9913495 | 0.9966189 | 0.0881257 | 1.489531 | 0.0376722 | 0.119152 | 10.8393766 | 0.1784139 | 1.1567976 |
+-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+
2024/03/15 15:44:20 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1992911100387573 - fine_loss: 1.1992911100387573
2024/03/15 15:46:09 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1084281206130981 - fine_loss: 1.1084281206130981
2024/03/15 15:48:04 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6499404907226562 - fine_loss: 1.6499404907226562
2024/03/15 15:49:54 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.508489191532135 - fine_loss: 0.508489191532135
2024/03/15 15:53:12 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8108782768249512 - fine_loss: 0.8108782768249512
2024/03/15 15:55:02 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.48491033911705017 - fine_loss: 0.48491033911705017
2024/03/15 15:56:57 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0899862051010132 - fine_loss: 1.0899862051010132
2024/03/15 15:58:51 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8050335645675659 - fine_loss: 0.8050335645675659
2024/03/15 16:00:51 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+
| 0.9395763 | 0.9908747 | 0.9968585 | 0.092961 | 1.3657981 | 0.0391972 | 0.1228719 | 10.8688248 | 0.149488 | 1.0754925 |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+
2024/03/15 16:02:50 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4576977491378784 - fine_loss: 0.4576977491378784
2024/03/15 16:04:41 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8896196484565735 - fine_loss: 0.8896196484565735
2024/03/15 16:06:39 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7209967374801636 - fine_loss: 0.7209967374801636
2024/03/15 16:08:35 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.045252799987793 - fine_loss: 1.045252799987793
2024/03/15 16:11:57 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4737454354763031 - fine_loss: 0.4737454354763031
2024/03/15 16:13:47 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8389743566513062 - fine_loss: 0.8389743566513062
2024/03/15 16:15:43 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7807110548019409 - fine_loss: 0.7807110548019409
2024/03/15 16:17:37 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.445203959941864 - fine_loss: 0.445203959941864
2024/03/15 16:19:33 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
| 0.9540785 | 0.9909157 | 0.996375 | 0.0777575 | 1.3239907 | 0.0337207 | 0.1110354 | 10.5282875 | 0.1364373 | 1.0023539 |
+-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+
2024/03/15 16:21:31 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7916954159736633 - fine_loss: 0.7916954159736633
2024/03/15 16:23:23 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9854772686958313 - fine_loss: 0.9854772686958313
2024/03/15 16:25:16 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6816364526748657 - fine_loss: 0.6816364526748657
2024/03/15 16:27:09 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8884357810020447 - fine_loss: 0.8884357810020447
2024/03/15 16:30:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9741940498352051 - fine_loss: 0.9741940498352051
2024/03/15 16:32:15 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.682526707649231 - fine_loss: 0.682526707649231
2024/03/15 16:34:08 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.49463391304016113 - fine_loss: 0.49463391304016113
2024/03/15 16:36:01 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2172904014587402 - fine_loss: 1.2172904014587402
2024/03/15 16:37:56 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+
| 0.9618936 | 0.9925915 | 0.9967338 | 0.0710943 | 1.2761649 | 0.0314868 | 0.1050877 | 9.96655 | 0.1254682 | 0.9844736 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+
2024/03/15 16:39:53 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5513850450515747 - fine_loss: 0.5513850450515747
2024/03/15 16:41:44 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6651225686073303 - fine_loss: 0.6651225686073303
2024/03/15 16:43:36 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3391605615615845 - fine_loss: 0.3391605615615845
2024/03/15 16:45:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7067725658416748 - fine_loss: 0.7067725658416748
2024/03/15 16:48:48 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.48334598541259766 - fine_loss: 0.48334598541259766
2024/03/15 16:50:39 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.020408034324646 - fine_loss: 1.020408034324646
2024/03/15 16:52:31 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7528542280197144 - fine_loss: 0.7528542280197144
2024/03/15 16:54:26 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5858664512634277 - fine_loss: 1.5858664512634277
2024/03/15 16:56:21 - patchstitcher - INFO - Evaluation Summary:
+-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+
| 0.9683023 | 0.992745 | 0.9975154 | 0.065207 | 1.2253438 | 0.0286473 | 0.097571 | 9.2976334 | 0.1133868 | 0.9503054 |
+-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+
2024/03/15 16:58:18 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6674519777297974 - fine_loss: 0.6674519777297974
2024/03/15 17:00:14 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6918603181838989 - fine_loss: 0.6918603181838989
2024/03/15 17:02:09 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8377310633659363 - fine_loss: 0.8377310633659363
2024/03/15 17:04:01 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5647892355918884 - fine_loss: 0.5647892355918884
2024/03/15 17:07:21 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8300132751464844 - fine_loss: 0.8300132751464844
2024/03/15 17:09:18 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.42331066727638245 - fine_loss: 0.42331066727638245
2024/03/15 17:11:11 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5752788782119751 - fine_loss: 0.5752788782119751
2024/03/15 17:13:05 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.2536908984184265 - fine_loss: 0.2536908984184265
2024/03/15 17:15:05 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
| 0.9718122 | 0.9932088 | 0.9976712 | 0.0618038 | 1.2020189 | 0.0268609 | 0.0932138 | 8.8305159 | 0.108609 | 0.9153564 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+
2024/03/15 17:17:06 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6014220118522644 - fine_loss: 0.6014220118522644
2024/03/15 17:18:55 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8239718675613403 - fine_loss: 0.8239718675613403
2024/03/15 17:20:48 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.2863079607486725 - fine_loss: 0.2863079607486725
2024/03/15 17:22:41 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1374967098236084 - fine_loss: 1.1374967098236084
2024/03/15 17:26:06 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7279921770095825 - fine_loss: 0.7279921770095825
2024/03/15 17:28:00 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7760436534881592 - fine_loss: 1.7760436534881592
2024/03/15 17:29:50 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5720145106315613 - fine_loss: 0.5720145106315613
2024/03/15 17:31:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.39483922719955444 - fine_loss: 0.39483922719955444
2024/03/15 17:33:44 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+
| 0.9754718 | 0.9935436 | 0.9978637 | 0.0557471 | 1.1464982 | 0.0244453 | 0.0875094 | 8.4613936 | 0.09984 | 0.8968383 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+
2024/03/15 17:35:41 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.47871437668800354 - fine_loss: 0.47871437668800354
2024/03/15 17:37:33 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1419689655303955 - fine_loss: 1.1419689655303955
2024/03/15 17:39:23 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9582790732383728 - fine_loss: 0.9582790732383728
2024/03/15 17:41:19 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5557586550712585 - fine_loss: 0.5557586550712585
2024/03/15 17:44:39 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7695197463035583 - fine_loss: 0.7695197463035583
2024/03/15 17:46:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5754735469818115 - fine_loss: 0.5754735469818115
2024/03/15 17:48:24 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4625895321369171 - fine_loss: 0.4625895321369171
2024/03/15 17:50:13 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4534989297389984 - fine_loss: 0.4534989297389984
2024/03/15 17:52:12 - patchstitcher - INFO - Evaluation Summary:
+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see |
+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9761539 | 0.9936435 | 0.997857 | 0.0550249 | 1.141468 | 0.0240967 | 0.0863539 | 8.3515906 | 0.0991177 | 0.8908329 |
+-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 17:52:12 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 17:52:12 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 17:52:15 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitl_u4k/fine_pretrain