2024/03/15 15:30:44 - 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 15:30:44 - 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='vitb', 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_vitb.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='vitb', 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_vitb.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', 'vitb', ] 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_vitb_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='vitb', 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_vitb.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 15:30:45 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitb.pt 2024/03/15 15:30:45 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is 2024/03/15 15:30:45 - patchstitcher - INFO - DistributedDataParallel( (module): BaselinePretrain( (fine_branch): ZoeDepth( (core): DepthAnythingCore( (core): DPT_DINOv2( (pretrained): DinoVisionTransformer( (patch_embed): PatchEmbed( (proj): Conv2d(3, 768, kernel_size=(14, 14), stride=(14, 14)) (norm): Identity() ) (blocks): ModuleList( (0-11): 12 x NestedTensorBlock( (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True) (attn): MemEffAttention( (qkv): Linear(in_features=768, out_features=2304, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (ls1): LayerScale() (drop_path1): Identity() (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=768, out_features=3072, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=3072, out_features=768, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): Identity() ) ) (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True) (head): Identity() ) (depth_head): DPTHead( (projects): ModuleList( (0): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1)) (2): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1)) (3): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1)) ) (resize_layers): ModuleList( (0): ConvTranspose2d(96, 96, kernel_size=(4, 4), stride=(4, 4)) (1): ConvTranspose2d(192, 192, kernel_size=(2, 2), stride=(2, 2)) (2): Identity() (3): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (scratch): Module( (layer1_rn): Conv2d(96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer2_rn): Conv2d(192, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer3_rn): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer4_rn): Conv2d(768, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (refinenet1): FeatureFusionBlock( (out_conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, 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(128, 128, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, 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(128, 128, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, 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(128, 128, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(128, 128, 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(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (output_conv2): Sequential( (0): Conv2d(64, 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(128, 128, kernel_size=(1, 1), stride=(1, 1)) (seed_bin_regressor): SeedBinRegressorUnnormed( (_net): Sequential( (0): Conv2d(128, 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(128, 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(128, 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 15:30:51 - patchstitcher - INFO - successfully init trainer 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls1.gamma 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls2.gamma 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.ls1.gamma 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: 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training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight 2024/03/15 15:30:51 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias 2024/03/15 15:33:25 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.288588523864746 - fine_loss: 2.288588523864746 2024/03/15 15:35:13 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.749260425567627 - fine_loss: 1.749260425567627 2024/03/15 15:36:58 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.603142499923706 - fine_loss: 2.603142499923706 2024/03/15 15:38:59 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 3.0235860347747803 - fine_loss: 3.0235860347747803 2024/03/15 15:42:38 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.2628891468048096 - fine_loss: 2.2628891468048096 2024/03/15 15:44:44 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.2125635147094727 - fine_loss: 2.2125635147094727 2024/03/15 15:46:44 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.884977102279663 - fine_loss: 1.884977102279663 2024/03/15 15:48:46 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 3.667808771133423 - fine_loss: 3.667808771133423 2024/03/15 15:50:43 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | 0.7653929 | 0.9569647 | 0.9891034 | 0.1631364 | 2.063872 | 0.0675193 | 0.2015772 | 17.5721867 | 0.3284417 | 1.5396647 | +-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 15:52:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.9002115726470947 - fine_loss: 1.9002115726470947 2024/03/15 15:54:51 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.533200979232788 - fine_loss: 1.533200979232788 2024/03/15 15:56:53 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3708069324493408 - fine_loss: 1.3708069324493408 2024/03/15 15:58:56 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.3536834716796875 - fine_loss: 1.3536834716796875 2024/03/15 16:02:35 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4067535400390625 - fine_loss: 1.4067535400390625 2024/03/15 16:04:38 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.571197509765625 - fine_loss: 1.571197509765625 2024/03/15 16:06:40 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.9749035835266113 - fine_loss: 2.9749035835266113 2024/03/15 16:08:48 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.893333911895752 - fine_loss: 0.893333911895752 2024/03/15 16:10:40 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | 0.8439181 | 0.9733375 | 0.992747 | 0.1316369 | 1.8230734 | 0.0558847 | 0.171333 | 15.4284363 | 0.2575101 | 1.3799866 | +-----------+-----------+----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ 2024/03/15 16:12:51 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5204694271087646 - fine_loss: 1.5204694271087646 2024/03/15 16:14:53 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0538222789764404 - fine_loss: 1.0538222789764404 2024/03/15 16:17:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.246050477027893 - fine_loss: 1.246050477027893 2024/03/15 16:19:04 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4139764308929443 - fine_loss: 1.4139764308929443 2024/03/15 16:22:40 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5990095138549805 - fine_loss: 1.5990095138549805 2024/03/15 16:24:45 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4719877243041992 - fine_loss: 1.4719877243041992 2024/03/15 16:26:49 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.998321533203125 - fine_loss: 0.998321533203125 2024/03/15 16:28:52 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2637615203857422 - fine_loss: 1.2637615203857422 2024/03/15 16:30:46 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.8831826 | 0.9846013 | 0.9953048 | 0.1145366 | 1.6448599 | 0.0488564 | 0.1510406 | 14.0402038 | 0.2199031 | 1.3085128 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 16:32:53 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.65132737159729 - fine_loss: 1.65132737159729 2024/03/15 16:34:56 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4322144985198975 - fine_loss: 1.4322144985198975 2024/03/15 16:37:04 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.034339427947998 - fine_loss: 1.034339427947998 2024/03/15 16:39:08 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0732086896896362 - fine_loss: 1.0732086896896362 2024/03/15 16:42:43 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3489086627960205 - fine_loss: 1.3489086627960205 2024/03/15 16:44:47 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.4356486797332764 - fine_loss: 1.4356486797332764 2024/03/15 16:46:50 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6865524649620056 - fine_loss: 0.6865524649620056 2024/03/15 16:48:50 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4590085744857788 - fine_loss: 1.4590085744857788 2024/03/15 16:50:41 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ | 0.8921932 | 0.9874671 | 0.9972081 | 0.1083586 | 1.6257898 | 0.0457595 | 0.142043 | 12.7745355 | 0.2076856 | 1.2743567 | +-----------+-----------+-----------+-----------+-----------+-----------+----------+------------+-----------+-----------+ 2024/03/15 16:52:44 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.008254885673523 - fine_loss: 1.008254885673523 2024/03/15 16:54:54 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8210620880126953 - fine_loss: 0.8210620880126953 2024/03/15 16:56:55 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.8681334257125854 - fine_loss: 1.8681334257125854 2024/03/15 16:58:59 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9568914771080017 - fine_loss: 0.9568914771080017 2024/03/15 17:02:34 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5452194213867188 - fine_loss: 1.5452194213867188 2024/03/15 17:04:40 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9237810373306274 - fine_loss: 0.9237810373306274 2024/03/15 17:06:43 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.4192367792129517 - fine_loss: 1.4192367792129517 2024/03/15 17:08:47 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1616711616516113 - fine_loss: 1.1616711616516113 2024/03/15 17:10:40 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ | 0.9095374 | 0.9878494 | 0.996491 | 0.1000458 | 1.529536 | 0.0445519 | 0.1377915 | 12.2980782 | 0.1741764 | 1.1720957 | +-----------+-----------+----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 17:12:48 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2545241117477417 - fine_loss: 1.2545241117477417 2024/03/15 17:14:52 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9477699398994446 - fine_loss: 0.9477699398994446 2024/03/15 17:16:59 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3806159496307373 - fine_loss: 1.3806159496307373 2024/03/15 17:19:02 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.12031888961792 - fine_loss: 1.12031888961792 2024/03/15 17:22:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9633316993713379 - fine_loss: 0.9633316993713379 2024/03/15 17:24:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9473192691802979 - fine_loss: 0.9473192691802979 2024/03/15 17:26:38 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8891739845275879 - fine_loss: 0.8891739845275879 2024/03/15 17:28:46 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9305822849273682 - fine_loss: 0.9305822849273682 2024/03/15 17:30:43 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9285209 | 0.9902661 | 0.9963124 | 0.0922186 | 1.4988106 | 0.0394503 | 0.1265562 | 11.929424 | 0.1792194 | 1.2142439 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 17:32:52 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.26497220993042 - fine_loss: 1.26497220993042 2024/03/15 17:35:00 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.580217957496643 - fine_loss: 1.580217957496643 2024/03/15 17:36:59 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6395942568778992 - fine_loss: 0.6395942568778992 2024/03/15 17:39:02 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.32594698667526245 - fine_loss: 0.32594698667526245 2024/03/15 17:42:34 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.924031674861908 - fine_loss: 0.924031674861908 2024/03/15 17:44:36 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.985018253326416 - fine_loss: 0.985018253326416 2024/03/15 17:46:38 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0442320108413696 - fine_loss: 1.0442320108413696 2024/03/15 17:48:43 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5068702101707458 - fine_loss: 0.5068702101707458 2024/03/15 17:50:33 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9381619 | 0.9895476 | 0.9972216 | 0.0913334 | 1.5578288 | 0.0391697 | 0.1243245 | 11.1463653 | 0.1706981 | 1.1217431 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 17:52:46 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1108862161636353 - fine_loss: 1.1108862161636353 2024/03/15 17:54:52 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9237959980964661 - fine_loss: 0.9237959980964661 2024/03/15 17:56:56 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5644421577453613 - fine_loss: 1.5644421577453613 2024/03/15 17:58:54 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7902756929397583 - fine_loss: 0.7902756929397583 2024/03/15 18:02:26 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.966326117515564 - fine_loss: 0.966326117515564 2024/03/15 18:04:32 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9776898622512817 - fine_loss: 0.9776898622512817 2024/03/15 18:06:33 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6681317090988159 - fine_loss: 0.6681317090988159 2024/03/15 18:08:34 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.80037522315979 - fine_loss: 0.80037522315979 2024/03/15 18:10:20 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9538666 | 0.9917138 | 0.9972104 | 0.0811061 | 1.3823568 | 0.0351258 | 0.1140013 | 10.5376763 | 0.1382621 | 1.0577048 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 18:12:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.147787094116211 - fine_loss: 1.147787094116211 2024/03/15 18:14:30 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.7300316691398621 - fine_loss: 0.7300316691398621 2024/03/15 18:16:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7750428318977356 - fine_loss: 0.7750428318977356 2024/03/15 18:18:37 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.50600266456604 - fine_loss: 1.50600266456604 2024/03/15 18:22:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8911293745040894 - fine_loss: 0.8911293745040894 2024/03/15 18:24:18 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5605521202087402 - fine_loss: 0.5605521202087402 2024/03/15 18:26:21 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6763710975646973 - fine_loss: 1.6763710975646973 2024/03/15 18:28:20 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6500707864761353 - fine_loss: 0.6500707864761353 2024/03/15 18:30:14 - patchstitcher - INFO - Evaluation Summary: +-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9562948 | 0.990871 | 0.9974688 | 0.0761721 | 1.3729287 | 0.0331131 | 0.1092103 | 10.1530306 | 0.1366973 | 1.0216396 | +-----------+----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 18:32:23 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5755664110183716 - fine_loss: 0.5755664110183716 2024/03/15 18:34:28 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2044012546539307 - fine_loss: 1.2044012546539307 2024/03/15 18:36:33 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.266536831855774 - fine_loss: 1.266536831855774 2024/03/15 18:38:35 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7211558818817139 - fine_loss: 0.7211558818817139 2024/03/15 18:42:13 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6136915683746338 - fine_loss: 0.6136915683746338 2024/03/15 18:44:12 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.4747104048728943 - fine_loss: 0.4747104048728943 2024/03/15 18:46:16 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5850560069084167 - fine_loss: 0.5850560069084167 2024/03/15 18:48:21 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.37204447388648987 - fine_loss: 0.37204447388648987 2024/03/15 18:50:16 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ | 0.9645657 | 0.9920502 | 0.997654 | 0.0686085 | 1.2732928 | 0.0299144 | 0.1009926 | 9.6382305 | 0.1200509 | 0.993343 | +-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ 2024/03/15 18:52:27 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6047840714454651 - fine_loss: 0.6047840714454651 2024/03/15 18:54:31 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5551916360855103 - fine_loss: 0.5551916360855103 2024/03/15 18:56:37 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.32560303807258606 - fine_loss: 0.32560303807258606 2024/03/15 18:58:40 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7431879043579102 - fine_loss: 1.7431879043579102 2024/03/15 19:02:20 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7936936020851135 - fine_loss: 0.7936936020851135 2024/03/15 19:04:21 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6791415214538574 - fine_loss: 0.6791415214538574 2024/03/15 19:06:23 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6265323758125305 - fine_loss: 0.6265323758125305 2024/03/15 19:08:25 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6945874691009521 - fine_loss: 0.6945874691009521 2024/03/15 19:10:17 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ | 0.9671118 | 0.9931541 | 0.9976758 | 0.0652155 | 1.2549019 | 0.0282474 | 0.0973396 | 9.2669667 | 0.1172386 | 0.9884787 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 19:12:25 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2996392250061035 - fine_loss: 1.2996392250061035 2024/03/15 19:14:26 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.674423098564148 - fine_loss: 0.674423098564148 2024/03/15 19:16:29 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.0330402851104736 - fine_loss: 2.0330402851104736 2024/03/15 19:18:34 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1583242416381836 - fine_loss: 1.1583242416381836 2024/03/15 19:22:12 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8227792978286743 - fine_loss: 0.8227792978286743 2024/03/15 19:24:12 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6849284172058105 - fine_loss: 0.6849284172058105 2024/03/15 19:26:14 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5954287648200989 - fine_loss: 0.5954287648200989 2024/03/15 19:28:20 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.38687634468078613 - fine_loss: 0.38687634468078613 2024/03/15 19:30:07 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ | 0.9687062 | 0.9931654 | 0.9976169 | 0.0635503 | 1.2467909 | 0.0277027 | 0.0958232 | 9.191893 | 0.1155029 | 0.9803023 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+-----------+ 2024/03/15 19:30:07 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict 2024/03/15 19:30:07 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> 2024/03/15 19:30:08 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitb_u4k/fine_pretrain