depthanything_vitl_u4k/fine_pretrain/20240315_140837.log

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
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2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
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