2024/03/15 03:55:26 - 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 03:55:26 - 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='vits', 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_vits.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='vits', 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_vits.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', 'vits', ] 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=100, 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_vits_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='vits', 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_vits.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 03:55:27 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt 2024/03/15 03:55:27 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is 2024/03/15 03:55:27 - patchstitcher - INFO - DistributedDataParallel( (module): BaselinePretrain( (fine_branch): ZoeDepth( (core): DepthAnythingCore( (core): DPT_DINOv2( (pretrained): DinoVisionTransformer( (patch_embed): PatchEmbed( (proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14)) (norm): Identity() ) (blocks): ModuleList( (0-11): 12 x NestedTensorBlock( (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (attn): MemEffAttention( (qkv): Linear(in_features=384, out_features=1152, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (ls1): LayerScale() (drop_path1): Identity() (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): Identity() ) ) (norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True) (head): Identity() ) (depth_head): DPTHead( (projects): ModuleList( (0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1)) (2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1)) (3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) ) (resize_layers): ModuleList( (0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4)) (1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2)) (2): Identity() (3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (scratch): Module( (layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (refinenet1): FeatureFusionBlock( (out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, 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(64, 64, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(64, 64, 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(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (output_conv2): Sequential( (0): Conv2d(32, 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(64, 64, kernel_size=(1, 1), stride=(1, 1)) (seed_bin_regressor): SeedBinRegressorUnnormed( (_net): Sequential( (0): Conv2d(64, 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(64, 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(64, 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 03:55:33 - patchstitcher - INFO - successfully init trainer 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls1.gamma 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls2.gamma 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.ls1.gamma 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.ls2.gamma 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.norm1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.norm1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.attn.qkv.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.attn.qkv.bias 2024/03/15 03:55:33 - patchstitcher - 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training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight 2024/03/15 03:55:33 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias 2024/03/15 03:57:49 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.039879322052002 - fine_loss: 2.039879322052002 2024/03/15 03:59:40 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 3.776620626449585 - fine_loss: 3.776620626449585 2024/03/15 04:01:30 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.1612205505371094 - fine_loss: 2.1612205505371094 2024/03/15 04:03:20 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3563077449798584 - fine_loss: 1.3563077449798584 2024/03/15 04:06:31 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.1678900718688965 - fine_loss: 2.1678900718688965 2024/03/15 04:08:25 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.8825774192810059 - fine_loss: 1.8825774192810059 2024/03/15 04:10:14 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.350590467453003 - fine_loss: 2.350590467453003 2024/03/15 04:12:06 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 2.691840648651123 - fine_loss: 2.691840648651123 2024/03/15 04:13:51 - patchstitcher - INFO - Evaluation Summary: +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.707044 | 0.9293698 | 0.9801447 | 0.1927294 | 2.3443637 | 0.0782506 | 0.2331481 | 20.0879481 | 0.4492522 | 1.7012854 | +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 04:15:48 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2447803020477295 - fine_loss: 1.2447803020477295 2024/03/15 04:17:37 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.6822900772094727 - fine_loss: 1.6822900772094727 2024/03/15 04:19:22 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.7436625957489014 - fine_loss: 2.7436625957489014 2024/03/15 04:21:15 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.9489283561706543 - fine_loss: 1.9489283561706543 2024/03/15 04:24:21 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5366265773773193 - fine_loss: 1.5366265773773193 2024/03/15 04:26:10 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0812580585479736 - fine_loss: 2.0812580585479736 2024/03/15 04:28:00 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.318430185317993 - fine_loss: 2.318430185317993 2024/03/15 04:29:48 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.638041615486145 - fine_loss: 1.638041615486145 2024/03/15 04:31:27 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+ | 0.7926732 | 0.9574633 | 0.9874803 | 0.1658911 | 2.033809 | 0.0653377 | 0.1971613 | 17.6386279 | 0.3764188 | 1.566062 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+----------+ 2024/03/15 04:33:22 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.071550726890564 - fine_loss: 1.071550726890564 2024/03/15 04:35:11 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.159848928451538 - fine_loss: 1.159848928451538 2024/03/15 04:36:58 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2986273765563965 - fine_loss: 1.2986273765563965 2024/03/15 04:38:48 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5721113681793213 - fine_loss: 1.5721113681793213 2024/03/15 04:42:00 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7645320892333984 - fine_loss: 1.7645320892333984 2024/03/15 04:43:48 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2818663120269775 - fine_loss: 1.2818663120269775 2024/03/15 04:45:40 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2445242404937744 - fine_loss: 1.2445242404937744 2024/03/15 04:47:30 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5368983745574951 - fine_loss: 1.5368983745574951 2024/03/15 04:49:02 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.8194143 | 0.9697637 | 0.9905658 | 0.1490125 | 1.8480574 | 0.0592408 | 0.1810736 | 15.8342003 | 0.3005681 | 1.3977808 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 04:50:58 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4791369438171387 - fine_loss: 1.4791369438171387 2024/03/15 04:52:44 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1252331733703613 - fine_loss: 2.1252331733703613 2024/03/15 04:54:32 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.84209406375885 - fine_loss: 1.84209406375885 2024/03/15 04:56:25 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1359673738479614 - fine_loss: 1.1359673738479614 2024/03/15 04:59:38 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5866280794143677 - fine_loss: 1.5866280794143677 2024/03/15 05:01:29 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3199617862701416 - fine_loss: 1.3199617862701416 2024/03/15 05:03:15 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6660882234573364 - fine_loss: 1.6660882234573364 2024/03/15 05:05:05 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0399880409240723 - fine_loss: 1.0399880409240723 2024/03/15 05:06:40 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.8804187 | 0.9831836 | 0.9948749 | 0.1118127 | 1.7537212 | 0.0498078 | 0.1550232 | 14.4210851 | 0.2352216 | 1.2980962 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 05:08:36 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7554281949996948 - fine_loss: 1.7554281949996948 2024/03/15 05:10:27 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.8572347164154053 - fine_loss: 2.8572347164154053 2024/03/15 05:12:16 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3657317161560059 - fine_loss: 1.3657317161560059 2024/03/15 05:14:08 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3460898399353027 - fine_loss: 1.3460898399353027 2024/03/15 05:17:20 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0736647844314575 - fine_loss: 1.0736647844314575 2024/03/15 05:19:11 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.179059624671936 - fine_loss: 1.179059624671936 2024/03/15 05:21:00 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0112545490264893 - fine_loss: 1.0112545490264893 2024/03/15 05:22:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2453086376190186 - fine_loss: 1.2453086376190186 2024/03/15 05:24:25 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.8772274 | 0.9823961 | 0.9948375 | 0.1173125 | 1.7241426 | 0.0501591 | 0.1553792 | 14.1530364 | 0.2422748 | 1.3415729 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 05:26:18 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.306344747543335 - fine_loss: 1.306344747543335 2024/03/15 05:28:16 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.348771572113037 - fine_loss: 1.348771572113037 2024/03/15 05:30:06 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.549656629562378 - fine_loss: 1.549656629562378 2024/03/15 05:31:57 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4452790021896362 - fine_loss: 1.4452790021896362 2024/03/15 05:35:10 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1077752113342285 - fine_loss: 1.1077752113342285 2024/03/15 05:37:01 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8956596255302429 - fine_loss: 0.8956596255302429 2024/03/15 05:38:52 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9720367789268494 - fine_loss: 0.9720367789268494 2024/03/15 05:40:41 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4826208353042603 - fine_loss: 1.4826208353042603 2024/03/15 05:42:16 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+ | 0.8740682 | 0.9844725 | 0.9957269 | 0.1142447 | 1.696142 | 0.0509766 | 0.1547242 | 13.9800131 | 0.237403 | 1.2716073 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+----------+-----------+ 2024/03/15 05:44:13 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.7906665802001953 - fine_loss: 1.7906665802001953 2024/03/15 05:46:06 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7277212142944336 - fine_loss: 1.7277212142944336 2024/03/15 05:48:00 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1345900297164917 - fine_loss: 1.1345900297164917 2024/03/15 05:49:53 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.680286169052124 - fine_loss: 0.680286169052124 2024/03/15 05:53:05 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0135771036148071 - fine_loss: 1.0135771036148071 2024/03/15 05:54:56 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1816802024841309 - fine_loss: 1.1816802024841309 2024/03/15 05:56:44 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3476241827011108 - fine_loss: 1.3476241827011108 2024/03/15 05:58:33 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6280028223991394 - fine_loss: 0.6280028223991394 2024/03/15 06:00:11 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | 0.9147314 | 0.9859354 | 0.9949076 | 0.1007045 | 1.6106567 | 0.0434999 | 0.138901 | 13.0318626 | 0.2056279 | 1.2140529 | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ 2024/03/15 06:02:04 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8376606702804565 - fine_loss: 0.8376606702804565 2024/03/15 06:03:57 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.03225576877594 - fine_loss: 1.03225576877594 2024/03/15 06:05:44 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9883253574371338 - fine_loss: 0.9883253574371338 2024/03/15 06:07:36 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.262385368347168 - fine_loss: 1.262385368347168 2024/03/15 06:10:46 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1695902347564697 - fine_loss: 1.1695902347564697 2024/03/15 06:12:36 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1688151359558105 - fine_loss: 2.1688151359558105 2024/03/15 06:14:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3791565895080566 - fine_loss: 1.3791565895080566 2024/03/15 06:16:12 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2718651294708252 - fine_loss: 1.2718651294708252 2024/03/15 06:17:50 - patchstitcher - INFO - Evaluation Summary: +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.917846 | 0.9849823 | 0.9948954 | 0.0979613 | 1.5791011 | 0.0433261 | 0.1380226 | 12.8257169 | 0.1883265 | 1.1684257 | +----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 06:19:42 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0557522773742676 - fine_loss: 1.0557522773742676 2024/03/15 06:21:33 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6954542398452759 - fine_loss: 0.6954542398452759 2024/03/15 06:23:20 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.203284740447998 - fine_loss: 1.203284740447998 2024/03/15 06:25:09 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2890739440917969 - fine_loss: 1.2890739440917969 2024/03/15 06:28:25 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8028295040130615 - fine_loss: 0.8028295040130615 2024/03/15 06:30:13 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.499220609664917 - fine_loss: 0.499220609664917 2024/03/15 06:32:01 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8515260219573975 - fine_loss: 0.8515260219573975 2024/03/15 06:33:51 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.953697919845581 - fine_loss: 0.953697919845581 2024/03/15 06:35:27 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9318894 | 0.9858092 | 0.9957183 | 0.0923063 | 1.5112557 | 0.0394818 | 0.1269675 | 11.6301649 | 0.1761516 | 1.1147971 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 06:37:24 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8879871368408203 - fine_loss: 0.8879871368408203 2024/03/15 06:39:14 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4138840436935425 - fine_loss: 1.4138840436935425 2024/03/15 06:41:05 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3911192417144775 - fine_loss: 1.3911192417144775 2024/03/15 06:42:59 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9037826061248779 - fine_loss: 0.9037826061248779 2024/03/15 06:46:06 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7059022784233093 - fine_loss: 0.7059022784233093 2024/03/15 06:47:58 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8616353273391724 - fine_loss: 0.8616353273391724 2024/03/15 06:49:51 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8395438194274902 - fine_loss: 0.8395438194274902 2024/03/15 06:51:43 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6362200379371643 - fine_loss: 0.6362200379371643 2024/03/15 06:53:21 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | 0.9486918 | 0.9883879 | 0.9965515 | 0.0802352 | 1.4414517 | 0.0349744 | 0.116316 | 10.9957016 | 0.1575956 | 1.0969994 | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ 2024/03/15 06:55:18 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6189630627632141 - fine_loss: 0.6189630627632141 2024/03/15 06:57:11 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1719452142715454 - fine_loss: 1.1719452142715454 2024/03/15 06:58:55 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.142961025238037 - fine_loss: 1.142961025238037 2024/03/15 07:00:45 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.719948649406433 - fine_loss: 1.719948649406433 2024/03/15 07:03:58 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6470488905906677 - fine_loss: 0.6470488905906677 2024/03/15 07:05:49 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5520279407501221 - fine_loss: 0.5520279407501221 2024/03/15 07:07:38 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8810967803001404 - fine_loss: 0.8810967803001404 2024/03/15 07:09:32 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6827142238616943 - fine_loss: 0.6827142238616943 2024/03/15 07:11:07 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | 0.9523656 | 0.9892937 | 0.9967417 | 0.0767006 | 1.4133022 | 0.0333895 | 0.1125023 | 10.666611 | 0.1523504 | 1.061902 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ 2024/03/15 07:13:02 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.8002086877822876 - fine_loss: 1.8002086877822876 2024/03/15 07:14:51 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5043245553970337 - fine_loss: 0.5043245553970337 2024/03/15 07:16:39 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6025413274765015 - fine_loss: 1.6025413274765015 2024/03/15 07:18:29 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3183393478393555 - fine_loss: 1.3183393478393555 2024/03/15 07:21:41 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.6571695804595947 - fine_loss: 1.6571695804595947 2024/03/15 07:23:30 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0306520462036133 - fine_loss: 1.0306520462036133 2024/03/15 07:25:19 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8030037879943848 - fine_loss: 0.8030037879943848 2024/03/15 07:27:09 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6139640808105469 - fine_loss: 0.6139640808105469 2024/03/15 07:28:49 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9531358 | 0.9897053 | 0.9967571 | 0.0759499 | 1.4041272 | 0.0327699 | 0.1107659 | 10.5243982 | 0.1508702 | 1.0635976 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 07:28:49 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict 2024/03/15 07:28:49 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> 2024/03/15 07:28:49 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vits_u4k/fine_pretrain