2024/03/15 14:08:48 - patchstitcher - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0] CUDA available: True numpy_random_seed: 621 GPU 0,1,2,3: NVIDIA A100-SXM4-80GB CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary NVCC: Cuda compilation tools, release 11.8, V11.8.89 GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2) PyTorch: 2.1.2 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.8 - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.7 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.16.2 OpenCV: 4.8.1 MMEngine: 0.10.2 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'forkserver'} dist_cfg: {'backend': 'nccl'} seed: 621 Distributed launcher: pytorch Distributed training: True GPU number: 4 ------------------------------------------------------------ 2024/03/15 14:08:49 - patchstitcher - INFO - Config: collect_input_args = [ 'image_lr', 'crops_image_hr', 'depth_gt', 'crop_depths', 'bboxs', 'image_hr', ] convert_syncbn = True debug = False env_cfg = dict( cudnn_benchmark=True, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='forkserver')) find_unused_parameters = True general_dataloader = dict( batch_size=1, dataset=dict( dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'), num_workers=2) launcher = 'pytorch' log_name = 'fine_pretrain' max_depth = 80 min_depth = 0.001 model = dict( coarse_branch=dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vitl', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vitl.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16), fine_branch=dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vitl', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vitl.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16), max_depth=80, min_depth=0.001, patch_process_shape=( 392, 518, ), sigloss=dict(type='SILogLoss'), target='fine', type='BaselinePretrain') optim_wrapper = dict( clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'), optimizer=dict(lr=4e-06, type='AdamW', weight_decay=0.01), paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict())) param_scheduler = dict( base_momentum=0.85, cycle_momentum=True, div_factor=1, final_div_factor=10000, max_momentum=0.95, pct_start=0.5, three_phase=False) project = 'patchfusion' tags = [ 'fine', 'da', 'vitl', ] test_in_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', split='./data/u4k/splits/test.txt', transform_cfg=dict(network_process_size=[ 384, 512, ]), type='UnrealStereo4kDataset'), num_workers=2) test_out_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', split='./data/u4k/splits/test_out.txt', transform_cfg=dict(network_process_size=[ 384, 512, ]), type='UnrealStereo4kDataset'), num_workers=2) train_cfg = dict( eval_start=0, log_interval=100, max_epochs=24, save_checkpoint_interval=24, train_log_img_interval=500, val_interval=2, val_log_img_interval=50, val_type='epoch_base') train_dataloader = dict( batch_size=4, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='train', resize_mode='depth-anything', split='./data/u4k/splits/train.txt', transform_cfg=dict( degree=1.0, network_process_size=[ 392, 518, ], random_crop=True), type='UnrealStereo4kDataset'), num_workers=4) val_dataloader = dict( batch_size=1, dataset=dict( data_root='./data/u4k', max_depth=80, min_depth=0.001, mode='infer', resize_mode='depth-anything', split='./data/u4k/splits/val.txt', transform_cfg=dict(degree=1.0, network_process_size=[ 392, 518, ]), type='UnrealStereo4kDataset'), num_workers=2) work_dir = './work_dir/depthanything_vitl_u4k/fine_pretrain' zoe_depth_config = dict( attractor_alpha=1000, attractor_gamma=2, attractor_kind='mean', attractor_type='inv', aug=True, bin_centers_type='softplus', bin_embedding_dim=128, clip_grad=0.1, dataset='nyu', depth_anything=True, distributed=True, do_resize=False, force_keep_ar=True, freeze_midas_bn=True, gpu='NULL', img_size=[ 392, 518, ], inverse_midas=False, log_images_every=0.1, max_depth=80, max_temp=50.0, max_translation=100, memory_efficient=True, midas_model_type='vitl', min_depth=0.001, min_temp=0.0212, model='zoedepth', n_attractors=[ 16, 8, 4, 1, ], n_bins=64, name='ZoeDepth', notes='', output_distribution='logbinomial', prefetch=False, pretrained_resource='local::./work_dir/DepthAnything_vitl.pt', print_losses=False, project='ZoeDepth', random_crop=False, random_translate=False, root='.', save_dir='', shared_dict='NULL', tags='', train_midas=True, translate_prob=0.2, type='DA-ZoeDepth', uid='NULL', use_amp=False, use_pretrained_midas=True, use_shared_dict=False, validate_every=0.25, version_name='v1', workers=16) 2024/03/15 14:08:53 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vitl.pt 2024/03/15 14:08:53 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is 2024/03/15 14:08:53 - patchstitcher - INFO - DistributedDataParallel( (module): BaselinePretrain( (fine_branch): ZoeDepth( (core): DepthAnythingCore( (core): DPT_DINOv2( (pretrained): DinoVisionTransformer( (patch_embed): PatchEmbed( (proj): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14)) (norm): Identity() ) (blocks): ModuleList( (0-23): 24 x NestedTensorBlock( (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (attn): MemEffAttention( (qkv): Linear(in_features=1024, out_features=3072, bias=True) (attn_drop): Dropout(p=0.0, inplace=False) (proj): Linear(in_features=1024, out_features=1024, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) ) (ls1): LayerScale() (drop_path1): Identity() (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1024, out_features=4096, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=4096, out_features=1024, bias=True) (drop): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): Identity() ) ) (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True) (head): Identity() ) (depth_head): DPTHead( (projects): ModuleList( (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) (2-3): 2 x Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) ) (resize_layers): ModuleList( (0): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(4, 4)) (1): ConvTranspose2d(512, 512, kernel_size=(2, 2), stride=(2, 2)) (2): Identity() (3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) (scratch): Module( (layer1_rn): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer2_rn): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer3_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (layer4_rn): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (refinenet1): FeatureFusionBlock( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet2): FeatureFusionBlock( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet3): FeatureFusionBlock( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (refinenet4): FeatureFusionBlock( (out_conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (resConfUnit1): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (resConfUnit2): ResidualConvUnit( (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (activation): ReLU() (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (skip_add): FloatFunctional( (activation_post_process): Identity() ) ) (output_conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (output_conv2): Sequential( (0): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) (3): ReLU(inplace=True) (4): Identity() ) ) ) ) ) (conv2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (seed_bin_regressor): SeedBinRegressorUnnormed( (_net): Sequential( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (seed_projector): Projector( (_net): Sequential( (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) ) ) (projectors): ModuleList( (0-3): 4 x Projector( (_net): Sequential( (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) ) ) ) (attractors): ModuleList( (0): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (1): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (2): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) (3): AttractorLayerUnnormed( (_net): Sequential( (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) ) (conditional_log_binomial): ConditionalLogBinomial( (log_binomial_transform): LogBinomial() (mlp): Sequential( (0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1)) (1): GELU(approximate='none') (2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) (3): Softplus(beta=1, threshold=20) ) ) ) (sigloss): SILogLoss() ) ) 2024/03/15 14:09:00 - patchstitcher - INFO - successfully init trainer 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.cls_token 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.pos_embed 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.mask_token 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.patch_embed.proj.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.qkv.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.attn.proj.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls1.gamma 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.norm2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.mlp.fc2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.0.ls2.gamma 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.qkv.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.attn.proj.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.ls1.gamma 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.norm2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.mlp.fc2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.1.ls2.gamma 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.norm1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.norm1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.attn.qkv.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.pretrained.blocks.2.attn.qkv.bias 2024/03/15 14:09:00 - patchstitcher - 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training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet2.resConfUnit2.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.out_conv.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit1.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet3.resConfUnit2.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.out_conv.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit1.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.refinenet4.resConfUnit2.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv1.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.core.core.depth_head.scratch.output_conv2.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conv2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conv2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_bin_regressor._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.seed_projector._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.0._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.1._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.2._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.projectors.3._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.0._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.1._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.2._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.attractors.3._net.2.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.0.bias 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.weight 2024/03/15 14:09:00 - patchstitcher - INFO - training param: module.fine_branch.conditional_log_binomial.mlp.2.bias 2024/03/15 14:11:58 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 3.4369797706604004 - fine_loss: 3.4369797706604004 2024/03/15 14:13:53 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7487813234329224 - fine_loss: 1.7487813234329224 2024/03/15 14:15:47 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.9658074378967285 - fine_loss: 2.9658074378967285 2024/03/15 14:17:39 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7623008489608765 - fine_loss: 1.7623008489608765 2024/03/15 14:21:19 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.5338351726531982 - fine_loss: 2.5338351726531982 2024/03/15 14:23:13 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.949808120727539 - fine_loss: 1.949808120727539 2024/03/15 14:25:07 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.5897963047027588 - fine_loss: 1.5897963047027588 2024/03/15 14:26:57 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5241825580596924 - fine_loss: 1.5241825580596924 2024/03/15 14:29:02 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+ | 0.7777531 | 0.9691491 | 0.9920921 | 0.1407966 | 2.1086142 | 0.064992 | 0.1937843 | 15.8287334 | 0.3172734 | 1.5069758 | +-----------+-----------+-----------+-----------+-----------+----------+-----------+------------+-----------+-----------+ 2024/03/15 14:30:59 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.046777367591858 - fine_loss: 1.046777367591858 2024/03/15 14:32:52 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0578508377075195 - fine_loss: 1.0578508377075195 2024/03/15 14:34:43 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1272225379943848 - fine_loss: 1.1272225379943848 2024/03/15 14:36:33 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4212095737457275 - fine_loss: 1.4212095737457275 2024/03/15 14:39:52 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.175830125808716 - fine_loss: 2.175830125808716 2024/03/15 14:41:46 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.6945712566375732 - fine_loss: 1.6945712566375732 2024/03/15 14:43:36 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6684590578079224 - fine_loss: 1.6684590578079224 2024/03/15 14:45:27 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.978624165058136 - fine_loss: 0.978624165058136 2024/03/15 14:47:24 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.8861725 | 0.9834373 | 0.9954867 | 0.1163805 | 1.6259623 | 0.0493756 | 0.1501408 | 13.2967868 | 0.2082237 | 1.2041435 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 14:49:20 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0877785682678223 - fine_loss: 1.0877785682678223 2024/03/15 14:51:13 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.627789855003357 - fine_loss: 1.627789855003357 2024/03/15 14:53:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.852139949798584 - fine_loss: 1.852139949798584 2024/03/15 14:54:56 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4405653774738312 - fine_loss: 0.4405653774738312 2024/03/15 14:58:15 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.5338048934936523 - fine_loss: 1.5338048934936523 2024/03/15 15:00:08 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6242114901542664 - fine_loss: 0.6242114901542664 2024/03/15 15:01:57 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.961182177066803 - fine_loss: 0.961182177066803 2024/03/15 15:03:48 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0484611988067627 - fine_loss: 1.0484611988067627 2024/03/15 15:05:46 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+ | 0.9134527 | 0.9875949 | 0.9960728 | 0.1049193 | 1.7395931 | 0.0450357 | 0.1404951 | 12.8880463 | 0.238056 | 1.314327 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+------------+----------+----------+ 2024/03/15 15:07:43 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7090848684310913 - fine_loss: 0.7090848684310913 2024/03/15 15:09:36 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.1976611614227295 - fine_loss: 2.1976611614227295 2024/03/15 15:11:25 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8840272426605225 - fine_loss: 0.8840272426605225 2024/03/15 15:13:16 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7640607357025146 - fine_loss: 0.7640607357025146 2024/03/15 15:16:35 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2251319885253906 - fine_loss: 1.2251319885253906 2024/03/15 15:18:29 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.059991717338562 - fine_loss: 1.059991717338562 2024/03/15 15:20:19 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.0797297954559326 - fine_loss: 2.0797297954559326 2024/03/15 15:22:08 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.7019829750061035 - fine_loss: 1.7019829750061035 2024/03/15 15:24:01 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+ | 0.9341925 | 0.9901051 | 0.996222 | 0.0896415 | 1.4864388 | 0.0385839 | 0.1228082 | 11.3385688 | 0.174791 | 1.2037814 | +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+----------+-----------+ 2024/03/15 15:25:57 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8242141604423523 - fine_loss: 0.8242141604423523 2024/03/15 15:27:48 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.5683438777923584 - fine_loss: 2.5683438777923584 2024/03/15 15:29:37 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3079029321670532 - fine_loss: 1.3079029321670532 2024/03/15 15:31:28 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.6061334609985352 - fine_loss: 1.6061334609985352 2024/03/15 15:34:46 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8605632185935974 - fine_loss: 0.8605632185935974 2024/03/15 15:36:39 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0743176937103271 - fine_loss: 1.0743176937103271 2024/03/15 15:38:29 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.351250410079956 - fine_loss: 1.351250410079956 2024/03/15 15:40:23 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0148663520812988 - fine_loss: 1.0148663520812988 2024/03/15 15:42:18 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+ | 0.9474117 | 0.9913495 | 0.9966189 | 0.0881257 | 1.489531 | 0.0376722 | 0.119152 | 10.8393766 | 0.1784139 | 1.1567976 | +-----------+-----------+-----------+-----------+----------+-----------+----------+------------+-----------+-----------+ 2024/03/15 15:44:20 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1992911100387573 - fine_loss: 1.1992911100387573 2024/03/15 15:46:09 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1084281206130981 - fine_loss: 1.1084281206130981 2024/03/15 15:48:04 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6499404907226562 - fine_loss: 1.6499404907226562 2024/03/15 15:49:54 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.508489191532135 - fine_loss: 0.508489191532135 2024/03/15 15:53:12 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8108782768249512 - fine_loss: 0.8108782768249512 2024/03/15 15:55:02 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.48491033911705017 - fine_loss: 0.48491033911705017 2024/03/15 15:56:57 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0899862051010132 - fine_loss: 1.0899862051010132 2024/03/15 15:58:51 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8050335645675659 - fine_loss: 0.8050335645675659 2024/03/15 16:00:51 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+ | 0.9395763 | 0.9908747 | 0.9968585 | 0.092961 | 1.3657981 | 0.0391972 | 0.1228719 | 10.8688248 | 0.149488 | 1.0754925 | +-----------+-----------+-----------+----------+-----------+-----------+-----------+------------+----------+-----------+ 2024/03/15 16:02:50 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4576977491378784 - fine_loss: 0.4576977491378784 2024/03/15 16:04:41 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8896196484565735 - fine_loss: 0.8896196484565735 2024/03/15 16:06:39 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7209967374801636 - fine_loss: 0.7209967374801636 2024/03/15 16:08:35 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.045252799987793 - fine_loss: 1.045252799987793 2024/03/15 16:11:57 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.4737454354763031 - fine_loss: 0.4737454354763031 2024/03/15 16:13:47 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8389743566513062 - fine_loss: 0.8389743566513062 2024/03/15 16:15:43 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7807110548019409 - fine_loss: 0.7807110548019409 2024/03/15 16:17:37 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.445203959941864 - fine_loss: 0.445203959941864 2024/03/15 16:19:33 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ | 0.9540785 | 0.9909157 | 0.996375 | 0.0777575 | 1.3239907 | 0.0337207 | 0.1110354 | 10.5282875 | 0.1364373 | 1.0023539 | +-----------+-----------+----------+-----------+-----------+-----------+-----------+------------+-----------+-----------+ 2024/03/15 16:21:31 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7916954159736633 - fine_loss: 0.7916954159736633 2024/03/15 16:23:23 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9854772686958313 - fine_loss: 0.9854772686958313 2024/03/15 16:25:16 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.6816364526748657 - fine_loss: 0.6816364526748657 2024/03/15 16:27:09 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8884357810020447 - fine_loss: 0.8884357810020447 2024/03/15 16:30:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9741940498352051 - fine_loss: 0.9741940498352051 2024/03/15 16:32:15 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.682526707649231 - fine_loss: 0.682526707649231 2024/03/15 16:34:08 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.49463391304016113 - fine_loss: 0.49463391304016113 2024/03/15 16:36:01 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2172904014587402 - fine_loss: 1.2172904014587402 2024/03/15 16:37:56 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+ | 0.9618936 | 0.9925915 | 0.9967338 | 0.0710943 | 1.2761649 | 0.0314868 | 0.1050877 | 9.96655 | 0.1254682 | 0.9844736 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+-----------+ 2024/03/15 16:39:53 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.5513850450515747 - fine_loss: 0.5513850450515747 2024/03/15 16:41:44 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6651225686073303 - fine_loss: 0.6651225686073303 2024/03/15 16:43:36 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.3391605615615845 - fine_loss: 0.3391605615615845 2024/03/15 16:45:28 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7067725658416748 - fine_loss: 0.7067725658416748 2024/03/15 16:48:48 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.48334598541259766 - fine_loss: 0.48334598541259766 2024/03/15 16:50:39 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.020408034324646 - fine_loss: 1.020408034324646 2024/03/15 16:52:31 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7528542280197144 - fine_loss: 0.7528542280197144 2024/03/15 16:54:26 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.5858664512634277 - fine_loss: 1.5858664512634277 2024/03/15 16:56:21 - patchstitcher - INFO - Evaluation Summary: +-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+ | 0.9683023 | 0.992745 | 0.9975154 | 0.065207 | 1.2253438 | 0.0286473 | 0.097571 | 9.2976334 | 0.1133868 | 0.9503054 | +-----------+----------+-----------+----------+-----------+-----------+----------+-----------+-----------+-----------+ 2024/03/15 16:58:18 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6674519777297974 - fine_loss: 0.6674519777297974 2024/03/15 17:00:14 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6918603181838989 - fine_loss: 0.6918603181838989 2024/03/15 17:02:09 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8377310633659363 - fine_loss: 0.8377310633659363 2024/03/15 17:04:01 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5647892355918884 - fine_loss: 0.5647892355918884 2024/03/15 17:07:21 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8300132751464844 - fine_loss: 0.8300132751464844 2024/03/15 17:09:18 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.42331066727638245 - fine_loss: 0.42331066727638245 2024/03/15 17:11:11 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5752788782119751 - fine_loss: 0.5752788782119751 2024/03/15 17:13:05 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.2536908984184265 - fine_loss: 0.2536908984184265 2024/03/15 17:15:05 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ | 0.9718122 | 0.9932088 | 0.9976712 | 0.0618038 | 1.2020189 | 0.0268609 | 0.0932138 | 8.8305159 | 0.108609 | 0.9153564 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ 2024/03/15 17:17:06 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6014220118522644 - fine_loss: 0.6014220118522644 2024/03/15 17:18:55 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8239718675613403 - fine_loss: 0.8239718675613403 2024/03/15 17:20:48 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.2863079607486725 - fine_loss: 0.2863079607486725 2024/03/15 17:22:41 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.1374967098236084 - fine_loss: 1.1374967098236084 2024/03/15 17:26:06 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7279921770095825 - fine_loss: 0.7279921770095825 2024/03/15 17:28:00 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.7760436534881592 - fine_loss: 1.7760436534881592 2024/03/15 17:29:50 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5720145106315613 - fine_loss: 0.5720145106315613 2024/03/15 17:31:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.39483922719955444 - fine_loss: 0.39483922719955444 2024/03/15 17:33:44 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+ | 0.9754718 | 0.9935436 | 0.9978637 | 0.0557471 | 1.1464982 | 0.0244453 | 0.0875094 | 8.4613936 | 0.09984 | 0.8968383 | +-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+---------+-----------+ 2024/03/15 17:35:41 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.47871437668800354 - fine_loss: 0.47871437668800354 2024/03/15 17:37:33 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1419689655303955 - fine_loss: 1.1419689655303955 2024/03/15 17:39:23 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9582790732383728 - fine_loss: 0.9582790732383728 2024/03/15 17:41:19 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5557586550712585 - fine_loss: 0.5557586550712585 2024/03/15 17:44:39 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7695197463035583 - fine_loss: 0.7695197463035583 2024/03/15 17:46:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.5754735469818115 - fine_loss: 0.5754735469818115 2024/03/15 17:48:24 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4625895321369171 - fine_loss: 0.4625895321369171 2024/03/15 17:50:13 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.4534989297389984 - fine_loss: 0.4534989297389984 2024/03/15 17:52:12 - patchstitcher - INFO - Evaluation Summary: +-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | +-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ | 0.9761539 | 0.9936435 | 0.997857 | 0.0550249 | 1.141468 | 0.0240967 | 0.0863539 | 8.3515906 | 0.0991177 | 0.8908329 | +-----------+-----------+----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ 2024/03/15 17:52:12 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict 2024/03/15 17:52:12 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> 2024/03/15 17:52:15 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vitl_u4k/fine_pretrain