depthanything_vitl_u4k/coarse_pretrain/20240315_102957.log

2024/03/15 10:30:08 - 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 10:30:08 - 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 = 'coarse_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,
    sigloss=dict(type='SILogLoss'),
    target='coarse',
    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 = [
    'coarse',
    '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/coarse_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 10:30:12 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitl.pt
2024/03/15 10:30:13 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'>
2024/03/15 10:30:13 - patchstitcher - INFO - DistributedDataParallel(
  (module): BaselinePretrain(
    (coarse_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 10:30:19 - patchstitcher - INFO - successfully init trainer
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight
2024/03/15 10:30:19 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias
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2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.resize_layers.3.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer1_rn.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer2_rn.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer3_rn.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.layer4_rn.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.out_conv.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit1.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet1.resConfUnit2.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.out_conv.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit1.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv1.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.core.core.depth_head.scratch.output_conv2.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conv2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conv2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_bin_regressor._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.seed_projector._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.0._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.1._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.2._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.projectors.3._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.0._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.1._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.2._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.attractors.3._net.2.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.0.bias
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.weight
2024/03/15 10:30:20 - patchstitcher - INFO - training param: module.coarse_branch.conditional_log_binomial.mlp.2.bias
2024/03/15 10:33:18 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3691939115524292 - coarse_loss: 1.3691939115524292
2024/03/15 10:35:12 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.619562029838562 - coarse_loss: 1.619562029838562
2024/03/15 10:37:06 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4217467308044434 - coarse_loss: 1.4217467308044434
2024/03/15 10:38:59 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.368167519569397 - coarse_loss: 1.368167519569397
2024/03/15 10:42:42 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7346311211585999 - coarse_loss: 0.7346311211585999
2024/03/15 10:44:36 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.020386815071106 - coarse_loss: 1.020386815071106
2024/03/15 10:46:30 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2180640697479248 - coarse_loss: 1.2180640697479248
2024/03/15 10:48:23 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1110318899154663 - coarse_loss: 1.1110318899154663
2024/03/15 10:50:14 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    | abs_rel  |   rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9598735 | 0.9891855 | 0.9951727 | 0.072453 | 1.587027 | 0.0309977 | 0.1077142 | 9.6015839 | 0.2148946 | 1.2024959 |
+-----------+-----------+-----------+----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 10:52:09 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.123214602470398 - coarse_loss: 1.123214602470398
2024/03/15 10:54:04 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0366013050079346 - coarse_loss: 1.0366013050079346
2024/03/15 10:55:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3733680248260498 - coarse_loss: 1.3733680248260498
2024/03/15 10:57:44 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9410479068756104 - coarse_loss: 0.9410479068756104
2024/03/15 11:01:02 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.338404893875122 - coarse_loss: 1.338404893875122
2024/03/15 11:02:55 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8547713756561279 - coarse_loss: 0.8547713756561279
2024/03/15 11:04:47 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0474542379379272 - coarse_loss: 1.0474542379379272
2024/03/15 11:06:40 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7824715971946716 - coarse_loss: 0.7824715971946716
2024/03/15 11:08:17 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9550269 | 0.9895827 | 0.9956135 | 0.0805194 | 1.5514586 | 0.0332511 | 0.1088383 | 9.1410583 | 0.2326348 | 1.2689711 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 11:10:14 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5382569432258606 - coarse_loss: 0.5382569432258606
2024/03/15 11:12:06 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5633417367935181 - coarse_loss: 0.5633417367935181
2024/03/15 11:13:57 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5049566030502319 - coarse_loss: 0.5049566030502319
2024/03/15 11:15:48 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.69290691614151 - coarse_loss: 0.69290691614151
2024/03/15 11:19:09 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2545151710510254 - coarse_loss: 1.2545151710510254
2024/03/15 11:20:59 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.805565357208252 - coarse_loss: 0.805565357208252
2024/03/15 11:22:49 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6542041897773743 - coarse_loss: 0.6542041897773743
2024/03/15 11:24:41 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6170626282691956 - coarse_loss: 0.6170626282691956
2024/03/15 11:26:22 - patchstitcher - INFO - Evaluation Summary: 
+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |    a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9726048 | 0.990251 | 0.9956528 | 0.0649057 | 1.4030008 | 0.0282428 | 0.0986983 | 8.6367456 | 0.1673493 | 1.0348499 |
+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 11:28:17 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7065086960792542 - coarse_loss: 0.7065086960792542
2024/03/15 11:30:12 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0559895038604736 - coarse_loss: 1.0559895038604736
2024/03/15 11:32:01 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0681591033935547 - coarse_loss: 1.0681591033935547
2024/03/15 11:33:51 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5747287273406982 - coarse_loss: 0.5747287273406982
2024/03/15 11:37:07 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6418678760528564 - coarse_loss: 0.6418678760528564
2024/03/15 11:38:58 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8234983086585999 - coarse_loss: 0.8234983086585999
2024/03/15 11:40:44 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.230547308921814 - coarse_loss: 1.230547308921814
2024/03/15 11:42:33 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3322598934173584 - coarse_loss: 1.3322598934173584
2024/03/15 11:44:10 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |   see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
| 0.9731681 | 0.9907731 | 0.9960122 | 0.0561289 | 1.3575984 | 0.0240152 | 0.0906736 | 8.3633755 | 0.1553763 | 1.001603 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+
2024/03/15 11:46:07 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0721046924591064 - coarse_loss: 1.0721046924591064
2024/03/15 11:47:57 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.740877389907837 - coarse_loss: 1.740877389907837
2024/03/15 11:49:43 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7495399713516235 - coarse_loss: 0.7495399713516235
2024/03/15 11:51:33 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9519928097724915 - coarse_loss: 0.9519928097724915
2024/03/15 11:54:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4422484040260315 - coarse_loss: 0.4422484040260315
2024/03/15 11:56:39 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8411234617233276 - coarse_loss: 0.8411234617233276
2024/03/15 11:58:30 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4021077156066895 - coarse_loss: 1.4021077156066895
2024/03/15 12:00:23 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.925296425819397 - coarse_loss: 0.925296425819397
2024/03/15 12:01:59 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |   rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
| 0.9740256 | 0.9909777 | 0.9960672 | 0.0548506 | 1.342812 | 0.0235422 | 0.0895881 | 7.8919773 | 0.1552812 | 1.0021132 |
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 12:03:58 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.770862340927124 - coarse_loss: 0.770862340927124
2024/03/15 12:05:47 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8211466073989868 - coarse_loss: 0.8211466073989868
2024/03/15 12:07:39 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.038414716720581 - coarse_loss: 1.038414716720581
2024/03/15 12:09:30 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4909307062625885 - coarse_loss: 0.4909307062625885
2024/03/15 12:12:45 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6742696762084961 - coarse_loss: 0.6742696762084961
2024/03/15 12:14:37 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7697466015815735 - coarse_loss: 0.7697466015815735
2024/03/15 12:16:25 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8855838179588318 - coarse_loss: 0.8855838179588318
2024/03/15 12:18:20 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.735936164855957 - coarse_loss: 0.735936164855957
2024/03/15 12:19:58 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |   see   |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+
| 0.9751907 | 0.9907739 | 0.9959293 | 0.0535933 | 1.3472066 | 0.0232206 | 0.0892041 | 7.9883518 | 0.1477947 | 0.97325 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+
2024/03/15 12:21:57 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1638290882110596 - coarse_loss: 1.1638290882110596
2024/03/15 12:23:45 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0216888189315796 - coarse_loss: 1.0216888189315796
2024/03/15 12:25:40 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8358562588691711 - coarse_loss: 0.8358562588691711
2024/03/15 12:27:34 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7175360918045044 - coarse_loss: 0.7175360918045044
2024/03/15 12:30:52 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5334227085113525 - coarse_loss: 0.5334227085113525
2024/03/15 12:32:41 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7218840718269348 - coarse_loss: 0.7218840718269348
2024/03/15 12:34:35 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5779137015342712 - coarse_loss: 0.5779137015342712
2024/03/15 12:36:26 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4988095760345459 - coarse_loss: 0.4988095760345459
2024/03/15 12:38:04 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  | rmse_log |  silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+
| 0.9751327 | 0.9912391 | 0.9962269 | 0.0510332 | 1.3050884 | 0.0220181 | 0.085528 | 7.621976 | 0.1437526 | 0.9718316 |
+-----------+-----------+-----------+-----------+-----------+-----------+----------+----------+-----------+-----------+
2024/03/15 12:40:00 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.563976526260376 - coarse_loss: 0.563976526260376
2024/03/15 12:41:52 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8443318605422974 - coarse_loss: 0.8443318605422974
2024/03/15 12:43:42 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0198791027069092 - coarse_loss: 1.0198791027069092
2024/03/15 12:45:33 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6089605093002319 - coarse_loss: 0.6089605093002319
2024/03/15 12:48:51 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9242361187934875 - coarse_loss: 0.9242361187934875
2024/03/15 12:50:38 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8595165014266968 - coarse_loss: 0.8595165014266968
2024/03/15 12:52:29 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9421486854553223 - coarse_loss: 0.9421486854553223
2024/03/15 12:54:21 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6723458766937256 - coarse_loss: 1.6723458766937256
2024/03/15 12:55:59 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
|     a1    |     a2    |     a3    | abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.9746925 | 0.9912283 | 0.9961616 | 0.050426 | 1.2974334 | 0.0215315 | 0.0845853 | 7.6158042 | 0.1422237 | 0.9475862 |
+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 12:57:56 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5518297553062439 - coarse_loss: 0.5518297553062439
2024/03/15 12:59:46 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7465678453445435 - coarse_loss: 0.7465678453445435
2024/03/15 13:01:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0676344633102417 - coarse_loss: 1.0676344633102417
2024/03/15 13:03:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1690986156463623 - coarse_loss: 1.1690986156463623
2024/03/15 13:06:46 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6604115962982178 - coarse_loss: 0.6604115962982178
2024/03/15 13:08:37 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5035145282745361 - coarse_loss: 0.5035145282745361
2024/03/15 13:10:28 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7860687375068665 - coarse_loss: 0.7860687375068665
2024/03/15 13:12:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9929344654083252 - coarse_loss: 0.9929344654083252
2024/03/15 13:13:56 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
|     a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |  silog   |   sq_rel  |    see    |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
| 0.9768352 | 0.9912757 | 0.9961631 | 0.0450889 | 1.3024287 | 0.0197906 | 0.0819427 | 7.473267 | 0.1371208 | 0.9184296 |
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+
2024/03/15 13:15:51 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4816737771034241 - coarse_loss: 0.4816737771034241
2024/03/15 13:17:44 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5091171264648438 - coarse_loss: 0.5091171264648438
2024/03/15 13:19:37 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7248642444610596 - coarse_loss: 0.7248642444610596
2024/03/15 13:21:29 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5194234251976013 - coarse_loss: 0.5194234251976013
2024/03/15 13:24:49 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0822392702102661 - coarse_loss: 1.0822392702102661
2024/03/15 13:26:42 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5438417196273804 - coarse_loss: 0.5438417196273804
2024/03/15 13:28:34 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.44422096014022827 - coarse_loss: 0.44422096014022827
2024/03/15 13:30:24 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8150679469108582 - coarse_loss: 0.8150679469108582
2024/03/15 13:32:03 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |    see    |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
| 0.977051 | 0.9914994 | 0.9962904 | 0.0436792 | 1.2543163 | 0.0187145 | 0.0793818 | 7.3642922 | 0.1357556 | 0.9069338 |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+
2024/03/15 13:34:01 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8032045364379883 - coarse_loss: 0.8032045364379883
2024/03/15 13:35:50 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5563585758209229 - coarse_loss: 0.5563585758209229
2024/03/15 13:37:44 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4848824143409729 - coarse_loss: 0.4848824143409729
2024/03/15 13:39:35 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5596399307250977 - coarse_loss: 1.5596399307250977
2024/03/15 13:42:55 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9384262561798096 - coarse_loss: 0.9384262561798096
2024/03/15 13:44:46 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8976131081581116 - coarse_loss: 0.8976131081581116
2024/03/15 13:46:36 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.43993157148361206 - coarse_loss: 0.43993157148361206
2024/03/15 13:48:30 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6713937520980835 - coarse_loss: 0.6713937520980835
2024/03/15 13:50:09 - patchstitcher - INFO - Evaluation Summary: 
+-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+
|     a1    |     a2    |     a3    | abs_rel  |    rmse   |   log_10  | rmse_log |   silog   |   sq_rel  |   see    |
+-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+
| 0.9772672 | 0.9916211 | 0.9963902 | 0.042066 | 1.2453072 | 0.0180818 | 0.078149 | 7.2418006 | 0.1324319 | 0.911489 |
+-----------+-----------+-----------+----------+-----------+-----------+----------+-----------+-----------+----------+
2024/03/15 13:52:06 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.503566563129425 - coarse_loss: 0.503566563129425
2024/03/15 13:53:55 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0374677181243896 - coarse_loss: 1.0374677181243896
2024/03/15 13:55:45 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5681146383285522 - coarse_loss: 1.5681146383285522
2024/03/15 13:57:38 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3371570110321045 - coarse_loss: 1.3371570110321045
2024/03/15 14:00:59 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8207178115844727 - coarse_loss: 0.8207178115844727
2024/03/15 14:02:50 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5041927695274353 - coarse_loss: 0.5041927695274353
2024/03/15 14:04:43 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3949466049671173 - coarse_loss: 0.3949466049671173
2024/03/15 14:06:32 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.37768083810806274 - coarse_loss: 0.37768083810806274
2024/03/15 14:08:10 - patchstitcher - INFO - Evaluation Summary: 
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+
|    a1    |     a2    |     a3    |  abs_rel  |    rmse   |   log_10  |  rmse_log |   silog   |   sq_rel  |  see   |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+
| 0.977363 | 0.9916447 | 0.9963651 | 0.0417068 | 1.2436186 | 0.0179399 | 0.0778791 | 7.2500755 | 0.1322989 | 0.9124 |
+----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+--------+
2024/03/15 14:08:10 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict
2024/03/15 14:08:10 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :>
2024/03/15 14:08:13 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitl_u4k/coarse_pretrain