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2024/03/15 00:20:41 - patchstitcher - INFO - |
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------------------------------------------------------------ |
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System environment: |
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sys.platform: linux |
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Python: 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0] |
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CUDA available: True |
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numpy_random_seed: 621 |
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GPU 0,1,2,3: NVIDIA A100-SXM4-80GB |
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CUDA_HOME: /sw/rl9g/cuda/11.8/rl9_binary |
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NVCC: Cuda compilation tools, release 11.8, V11.8.89 |
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GCC: gcc (GCC) 11.3.1 20220421 (Red Hat 11.3.1-2) |
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PyTorch: 2.1.2 |
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PyTorch compiling details: PyTorch built with: |
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- GCC 9.3 |
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- C++ Version: 201703 |
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- Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications |
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- Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) |
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- OpenMP 201511 (a.k.a. OpenMP 4.5) |
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- LAPACK is enabled (usually provided by MKL) |
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- NNPACK is enabled |
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- CPU capability usage: AVX2 |
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- CUDA Runtime 11.8 |
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- 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 |
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- CuDNN 8.7 |
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- Magma 2.6.1 |
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- 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, |
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|
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TorchVision: 0.16.2 |
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OpenCV: 4.8.1 |
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MMEngine: 0.10.2 |
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|
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Runtime environment: |
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cudnn_benchmark: True |
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mp_cfg: {'mp_start_method': 'forkserver'} |
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dist_cfg: {'backend': 'nccl'} |
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seed: 621 |
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Distributed launcher: pytorch |
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Distributed training: True |
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GPU number: 4 |
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------------------------------------------------------------ |
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2024/03/15 00:20:41 - patchstitcher - INFO - Config: |
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collect_input_args = [ |
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'image_lr', |
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'crops_image_hr', |
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'depth_gt', |
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'crop_depths', |
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'bboxs', |
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'image_hr', |
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] |
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convert_syncbn = True |
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debug = False |
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env_cfg = dict( |
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cudnn_benchmark=True, |
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dist_cfg=dict(backend='nccl'), |
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mp_cfg=dict(mp_start_method='forkserver')) |
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find_unused_parameters = True |
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general_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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dataset_name='', gt_dir=None, rgb_image_dir='', type='ImageDataset'), |
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num_workers=2) |
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launcher = 'pytorch' |
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log_name = 'coarse_pretrain' |
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max_depth = 80 |
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min_depth = 0.001 |
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model = dict( |
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coarse_branch=dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
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attractor_kind='mean', |
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attractor_type='inv', |
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aug=True, |
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bin_centers_type='softplus', |
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bin_embedding_dim=128, |
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clip_grad=0.1, |
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dataset='nyu', |
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depth_anything=True, |
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distributed=True, |
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do_resize=False, |
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force_keep_ar=True, |
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freeze_midas_bn=True, |
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gpu='NULL', |
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img_size=[ |
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392, |
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518, |
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], |
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inverse_midas=False, |
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log_images_every=0.1, |
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max_depth=80, |
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max_temp=50.0, |
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max_translation=100, |
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memory_efficient=True, |
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midas_model_type='vits', |
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min_depth=0.001, |
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min_temp=0.0212, |
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model='zoedepth', |
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n_attractors=[ |
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16, |
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8, |
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4, |
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1, |
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], |
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n_bins=64, |
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name='ZoeDepth', |
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notes='', |
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output_distribution='logbinomial', |
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prefetch=False, |
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pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
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print_losses=False, |
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project='ZoeDepth', |
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random_crop=False, |
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random_translate=False, |
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root='.', |
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save_dir='', |
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shared_dict='NULL', |
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tags='', |
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train_midas=True, |
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translate_prob=0.2, |
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type='DA-ZoeDepth', |
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uid='NULL', |
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use_amp=False, |
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use_pretrained_midas=True, |
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use_shared_dict=False, |
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validate_every=0.25, |
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version_name='v1', |
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workers=16), |
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fine_branch=dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
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attractor_kind='mean', |
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attractor_type='inv', |
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aug=True, |
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bin_centers_type='softplus', |
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bin_embedding_dim=128, |
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clip_grad=0.1, |
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dataset='nyu', |
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depth_anything=True, |
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distributed=True, |
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do_resize=False, |
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force_keep_ar=True, |
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freeze_midas_bn=True, |
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gpu='NULL', |
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img_size=[ |
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392, |
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518, |
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], |
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inverse_midas=False, |
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log_images_every=0.1, |
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max_depth=80, |
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max_temp=50.0, |
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max_translation=100, |
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memory_efficient=True, |
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midas_model_type='vits', |
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min_depth=0.001, |
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min_temp=0.0212, |
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model='zoedepth', |
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n_attractors=[ |
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16, |
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8, |
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4, |
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1, |
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], |
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n_bins=64, |
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name='ZoeDepth', |
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notes='', |
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output_distribution='logbinomial', |
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prefetch=False, |
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pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
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print_losses=False, |
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project='ZoeDepth', |
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random_crop=False, |
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random_translate=False, |
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root='.', |
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save_dir='', |
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shared_dict='NULL', |
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tags='', |
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train_midas=True, |
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translate_prob=0.2, |
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type='DA-ZoeDepth', |
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uid='NULL', |
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use_amp=False, |
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use_pretrained_midas=True, |
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use_shared_dict=False, |
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validate_every=0.25, |
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version_name='v1', |
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workers=16), |
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max_depth=80, |
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min_depth=0.001, |
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sigloss=dict(type='SILogLoss'), |
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target='coarse', |
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type='BaselinePretrain') |
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optim_wrapper = dict( |
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clip_grad=dict(max_norm=0.1, norm_type=2, type='norm'), |
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optimizer=dict(lr=4e-06, type='AdamW', weight_decay=0.01), |
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paramwise_cfg=dict(bypass_duplicate=True, custom_keys=dict())) |
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param_scheduler = dict( |
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base_momentum=0.85, |
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cycle_momentum=True, |
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div_factor=1, |
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final_div_factor=10000, |
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max_momentum=0.95, |
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pct_start=0.5, |
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three_phase=False) |
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project = 'patchfusion' |
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tags = [ |
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'coarse', |
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'da', |
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'vits', |
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] |
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test_in_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
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split='./data/u4k/splits/test.txt', |
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transform_cfg=dict(network_process_size=[ |
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384, |
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512, |
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]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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test_out_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
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split='./data/u4k/splits/test_out.txt', |
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transform_cfg=dict(network_process_size=[ |
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384, |
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512, |
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]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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train_cfg = dict( |
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eval_start=0, |
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log_interval=100, |
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max_epochs=24, |
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save_checkpoint_interval=24, |
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train_log_img_interval=100, |
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val_interval=2, |
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val_log_img_interval=50, |
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val_type='epoch_base') |
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train_dataloader = dict( |
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batch_size=4, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='train', |
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resize_mode='depth-anything', |
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split='./data/u4k/splits/train.txt', |
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transform_cfg=dict( |
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degree=1.0, network_process_size=[ |
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392, |
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518, |
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], random_crop=True), |
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type='UnrealStereo4kDataset'), |
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num_workers=4) |
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val_dataloader = dict( |
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batch_size=1, |
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dataset=dict( |
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data_root='./data/u4k', |
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max_depth=80, |
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min_depth=0.001, |
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mode='infer', |
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resize_mode='depth-anything', |
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split='./data/u4k/splits/val.txt', |
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transform_cfg=dict(degree=1.0, network_process_size=[ |
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392, |
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518, |
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]), |
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type='UnrealStereo4kDataset'), |
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num_workers=2) |
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work_dir = './work_dir/depthanything_vits_u4k/coarse_pretrain' |
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zoe_depth_config = dict( |
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attractor_alpha=1000, |
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attractor_gamma=2, |
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attractor_kind='mean', |
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attractor_type='inv', |
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aug=True, |
|
bin_centers_type='softplus', |
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bin_embedding_dim=128, |
|
clip_grad=0.1, |
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dataset='nyu', |
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depth_anything=True, |
|
distributed=True, |
|
do_resize=False, |
|
force_keep_ar=True, |
|
freeze_midas_bn=True, |
|
gpu='NULL', |
|
img_size=[ |
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392, |
|
518, |
|
], |
|
inverse_midas=False, |
|
log_images_every=0.1, |
|
max_depth=80, |
|
max_temp=50.0, |
|
max_translation=100, |
|
memory_efficient=True, |
|
midas_model_type='vits', |
|
min_depth=0.001, |
|
min_temp=0.0212, |
|
model='zoedepth', |
|
n_attractors=[ |
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16, |
|
8, |
|
4, |
|
1, |
|
], |
|
n_bins=64, |
|
name='ZoeDepth', |
|
notes='', |
|
output_distribution='logbinomial', |
|
prefetch=False, |
|
pretrained_resource='local::./work_dir/DepthAnything_vits.pt', |
|
print_losses=False, |
|
project='ZoeDepth', |
|
random_crop=False, |
|
random_translate=False, |
|
root='.', |
|
save_dir='', |
|
shared_dict='NULL', |
|
tags='', |
|
train_midas=True, |
|
translate_prob=0.2, |
|
type='DA-ZoeDepth', |
|
uid='NULL', |
|
use_amp=False, |
|
use_pretrained_midas=True, |
|
use_shared_dict=False, |
|
validate_every=0.25, |
|
version_name='v1', |
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workers=16) |
|
|
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2024/03/15 00:20:41 - patchstitcher - INFO - Loading deepnet from local::./work_dir/DepthAnything_vits.pt |
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2024/03/15 00:20:41 - patchstitcher - INFO - Current zoedepth.core.prep.resizer is <class 'torch.nn.modules.linear.Identity'> |
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2024/03/15 00:20:42 - patchstitcher - INFO - DistributedDataParallel( |
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(module): BaselinePretrain( |
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(coarse_branch): ZoeDepth( |
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(core): DepthAnythingCore( |
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(core): DPT_DINOv2( |
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(pretrained): DinoVisionTransformer( |
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(patch_embed): PatchEmbed( |
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(proj): Conv2d(3, 384, kernel_size=(14, 14), stride=(14, 14)) |
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(norm): Identity() |
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) |
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(blocks): ModuleList( |
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(0-11): 12 x NestedTensorBlock( |
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(norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(attn): MemEffAttention( |
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(qkv): Linear(in_features=384, out_features=1152, bias=True) |
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(attn_drop): Dropout(p=0.0, inplace=False) |
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(proj): Linear(in_features=384, out_features=384, bias=True) |
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(proj_drop): Dropout(p=0.0, inplace=False) |
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) |
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(ls1): LayerScale() |
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(drop_path1): Identity() |
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(norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(mlp): Mlp( |
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(fc1): Linear(in_features=384, out_features=1536, bias=True) |
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(act): GELU(approximate='none') |
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(fc2): Linear(in_features=1536, out_features=384, bias=True) |
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(drop): Dropout(p=0.0, inplace=False) |
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) |
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(ls2): LayerScale() |
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(drop_path2): Identity() |
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) |
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) |
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(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True) |
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(head): Identity() |
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) |
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(depth_head): DPTHead( |
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(projects): ModuleList( |
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(0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) |
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(1): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1)) |
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(2): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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(resize_layers): ModuleList( |
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(0): ConvTranspose2d(48, 48, kernel_size=(4, 4), stride=(4, 4)) |
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(1): ConvTranspose2d(96, 96, kernel_size=(2, 2), stride=(2, 2)) |
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(2): Identity() |
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(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
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) |
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(scratch): Module( |
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(layer1_rn): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer2_rn): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer3_rn): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(layer4_rn): Conv2d(384, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) |
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(refinenet1): FeatureFusionBlock( |
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(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
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(resConfUnit1): ResidualConvUnit( |
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(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
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(skip_add): FloatFunctional( |
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(activation_post_process): Identity() |
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) |
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) |
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(resConfUnit2): ResidualConvUnit( |
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(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(refinenet2): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(refinenet3): FeatureFusionBlock( |
|
(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
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) |
|
(resConfUnit2): ResidualConvUnit( |
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(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
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) |
|
) |
|
(refinenet4): FeatureFusionBlock( |
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(out_conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
|
(resConfUnit1): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(resConfUnit2): ResidualConvUnit( |
|
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(activation): ReLU() |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(skip_add): FloatFunctional( |
|
(activation_post_process): Identity() |
|
) |
|
) |
|
(output_conv1): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
|
(output_conv2): Sequential( |
|
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(32, 1, kernel_size=(1, 1), stride=(1, 1)) |
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(3): ReLU(inplace=True) |
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(4): Identity() |
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) |
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) |
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) |
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) |
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) |
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(conv2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) |
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(seed_bin_regressor): SeedBinRegressorUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(seed_projector): Projector( |
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(_net): Sequential( |
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(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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) |
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(projectors): ModuleList( |
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(0-3): 4 x Projector( |
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(_net): Sequential( |
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(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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) |
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) |
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) |
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(attractors): ModuleList( |
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(0): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(1): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 8, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(2): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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(3): AttractorLayerUnnormed( |
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(_net): Sequential( |
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(0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) |
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(1): ReLU(inplace=True) |
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(2): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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) |
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(conditional_log_binomial): ConditionalLogBinomial( |
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(log_binomial_transform): LogBinomial() |
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(mlp): Sequential( |
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(0): Conv2d(161, 80, kernel_size=(1, 1), stride=(1, 1)) |
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(1): GELU(approximate='none') |
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(2): Conv2d(80, 4, kernel_size=(1, 1), stride=(1, 1)) |
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(3): Softplus(beta=1, threshold=20) |
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) |
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) |
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) |
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(sigloss): SILogLoss() |
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) |
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) |
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2024/03/15 00:20:47 - patchstitcher - INFO - successfully init trainer |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.cls_token |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.pos_embed |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.mask_token |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.patch_embed.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.0.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.1.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.2.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.3.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.4.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.5.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.6.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.7.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.attn.qkv.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.attn.qkv.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.attn.proj.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.attn.proj.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.ls1.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.norm2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.norm2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.mlp.fc1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.mlp.fc1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.mlp.fc2.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.mlp.fc2.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.8.ls2.gamma |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.9.norm1.weight |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.9.norm1.bias |
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2024/03/15 00:20:47 - patchstitcher - INFO - training param: module.coarse_branch.core.core.pretrained.blocks.9.attn.qkv.weight |
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2024/03/15 00:23:05 - patchstitcher - INFO - Epoch: [01/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 2.218886375427246 - coarse_loss: 2.218886375427246 |
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2024/03/15 00:24:52 - patchstitcher - INFO - Epoch: [01/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 2.0132031440734863 - coarse_loss: 2.0132031440734863 |
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2024/03/15 00:26:41 - patchstitcher - INFO - Epoch: [01/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 2.1340489387512207 - coarse_loss: 2.1340489387512207 |
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2024/03/15 00:28:31 - patchstitcher - INFO - Epoch: [01/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.68356192111969 - coarse_loss: 1.68356192111969 |
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2024/03/15 00:31:46 - patchstitcher - INFO - Epoch: [02/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.1240144968032837 - coarse_loss: 1.1240144968032837 |
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2024/03/15 00:33:37 - patchstitcher - INFO - Epoch: [02/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.2552540302276611 - coarse_loss: 1.2552540302276611 |
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2024/03/15 00:35:27 - patchstitcher - INFO - Epoch: [02/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3931670188903809 - coarse_loss: 1.3931670188903809 |
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2024/03/15 00:37:17 - patchstitcher - INFO - Epoch: [02/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4315416812896729 - coarse_loss: 1.4315416812896729 |
|
2024/03/15 00:38:56 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
| 0.9222131 | 0.9841732 | 0.9937032 | 0.0942684 | 1.901311 | 0.0392215 | 0.1319014 | 11.5870857 | 0.3169146 | 1.4523976 | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
2024/03/15 00:40:53 - patchstitcher - INFO - Epoch: [03/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3891286849975586 - coarse_loss: 1.3891286849975586 |
|
2024/03/15 00:42:45 - patchstitcher - INFO - Epoch: [03/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3853542804718018 - coarse_loss: 1.3853542804718018 |
|
2024/03/15 00:44:31 - patchstitcher - INFO - Epoch: [03/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.6085820198059082 - coarse_loss: 1.6085820198059082 |
|
2024/03/15 00:46:24 - patchstitcher - INFO - Epoch: [03/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.2743269205093384 - coarse_loss: 1.2743269205093384 |
|
2024/03/15 00:49:33 - patchstitcher - INFO - Epoch: [04/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4644969701766968 - coarse_loss: 1.4644969701766968 |
|
2024/03/15 00:51:20 - patchstitcher - INFO - Epoch: [04/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.040415644645691 - coarse_loss: 1.040415644645691 |
|
2024/03/15 00:53:07 - patchstitcher - INFO - Epoch: [04/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.2523736953735352 - coarse_loss: 1.2523736953735352 |
|
2024/03/15 00:54:57 - patchstitcher - INFO - Epoch: [04/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7893640995025635 - coarse_loss: 0.7893640995025635 |
|
2024/03/15 00:56:31 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
| 0.9466366 | 0.9857079 | 0.9944696 | 0.0784504 | 1.723246 | 0.0331783 | 0.1166779 | 10.4672395 | 0.2658952 | 1.2480133 | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+------------+-----------+-----------+ |
|
2024/03/15 00:58:25 - patchstitcher - INFO - Epoch: [05/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.8934182524681091 - coarse_loss: 0.8934182524681091 |
|
2024/03/15 01:00:10 - patchstitcher - INFO - Epoch: [05/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0365135669708252 - coarse_loss: 1.0365135669708252 |
|
2024/03/15 01:02:00 - patchstitcher - INFO - Epoch: [05/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0158889293670654 - coarse_loss: 1.0158889293670654 |
|
2024/03/15 01:03:50 - patchstitcher - INFO - Epoch: [05/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7366129159927368 - coarse_loss: 0.7366129159927368 |
|
2024/03/15 01:07:04 - patchstitcher - INFO - Epoch: [06/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4556183815002441 - coarse_loss: 1.4556183815002441 |
|
2024/03/15 01:08:51 - patchstitcher - INFO - Epoch: [06/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.093213677406311 - coarse_loss: 1.093213677406311 |
|
2024/03/15 01:10:45 - patchstitcher - INFO - Epoch: [06/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8329901099205017 - coarse_loss: 0.8329901099205017 |
|
2024/03/15 01:12:32 - patchstitcher - INFO - Epoch: [06/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8255199193954468 - coarse_loss: 0.8255199193954468 |
|
2024/03/15 01:14:05 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9492006 | 0.9876058 | 0.9947174 | 0.0765434 | 1.6623389 | 0.0336977 | 0.1157899 | 10.168448 | 0.2274059 | 1.1601292 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 01:16:01 - patchstitcher - INFO - Epoch: [07/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9320656061172485 - coarse_loss: 0.9320656061172485 |
|
2024/03/15 01:17:44 - patchstitcher - INFO - Epoch: [07/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.3558683395385742 - coarse_loss: 1.3558683395385742 |
|
2024/03/15 01:19:36 - patchstitcher - INFO - Epoch: [07/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1851251125335693 - coarse_loss: 1.1851251125335693 |
|
2024/03/15 01:21:28 - patchstitcher - INFO - Epoch: [07/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.7694360613822937 - coarse_loss: 0.7694360613822937 |
|
2024/03/15 01:24:39 - patchstitcher - INFO - Epoch: [08/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9268642067909241 - coarse_loss: 0.9268642067909241 |
|
2024/03/15 01:26:28 - patchstitcher - INFO - Epoch: [08/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0070387125015259 - coarse_loss: 1.0070387125015259 |
|
2024/03/15 01:28:17 - patchstitcher - INFO - Epoch: [08/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.3363308906555176 - coarse_loss: 1.3363308906555176 |
|
2024/03/15 01:30:03 - patchstitcher - INFO - Epoch: [08/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.549015998840332 - coarse_loss: 1.549015998840332 |
|
2024/03/15 01:31:38 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ |
|
| 0.9580896 | 0.9882235 | 0.9949475 | 0.0697348 | 1.6023046 | 0.0295156 | 0.1067427 | 9.6755001 | 0.224005 | 1.1545794 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+-----------+ |
|
2024/03/15 01:33:33 - patchstitcher - INFO - Epoch: [09/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.2725939750671387 - coarse_loss: 1.2725939750671387 |
|
2024/03/15 01:35:25 - patchstitcher - INFO - Epoch: [09/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.8319188356399536 - coarse_loss: 1.8319188356399536 |
|
2024/03/15 01:37:16 - patchstitcher - INFO - Epoch: [09/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9146164655685425 - coarse_loss: 0.9146164655685425 |
|
2024/03/15 01:39:08 - patchstitcher - INFO - Epoch: [09/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9933633208274841 - coarse_loss: 0.9933633208274841 |
|
2024/03/15 01:42:21 - patchstitcher - INFO - Epoch: [10/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.531670331954956 - coarse_loss: 0.531670331954956 |
|
2024/03/15 01:44:13 - patchstitcher - INFO - Epoch: [10/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.98005211353302 - coarse_loss: 0.98005211353302 |
|
2024/03/15 01:46:08 - patchstitcher - INFO - Epoch: [10/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.562972068786621 - coarse_loss: 1.562972068786621 |
|
2024/03/15 01:48:00 - patchstitcher - INFO - Epoch: [10/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.0578055381774902 - coarse_loss: 1.0578055381774902 |
|
2024/03/15 01:49:39 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ |
|
| 0.9573399 | 0.9884624 | 0.9949526 | 0.0727779 | 1.5619678 | 0.030998 | 0.1089102 | 9.5524 | 0.2075647 | 1.1259904 | |
|
+-----------+-----------+-----------+-----------+-----------+----------+-----------+--------+-----------+-----------+ |
|
2024/03/15 01:51:35 - patchstitcher - INFO - Epoch: [11/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9536416530609131 - coarse_loss: 0.9536416530609131 |
|
2024/03/15 01:53:33 - patchstitcher - INFO - Epoch: [11/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.061272382736206 - coarse_loss: 1.061272382736206 |
|
2024/03/15 01:55:27 - patchstitcher - INFO - Epoch: [11/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.403846263885498 - coarse_loss: 1.403846263885498 |
|
2024/03/15 01:57:19 - patchstitcher - INFO - Epoch: [11/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.6634379625320435 - coarse_loss: 0.6634379625320435 |
|
2024/03/15 02:00:39 - patchstitcher - INFO - Epoch: [12/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7105982303619385 - coarse_loss: 0.7105982303619385 |
|
2024/03/15 02:02:34 - patchstitcher - INFO - Epoch: [12/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.8706010580062866 - coarse_loss: 0.8706010580062866 |
|
2024/03/15 02:04:29 - patchstitcher - INFO - Epoch: [12/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.013525366783142 - coarse_loss: 1.013525366783142 |
|
2024/03/15 02:06:17 - patchstitcher - INFO - Epoch: [12/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9357657432556152 - coarse_loss: 0.9357657432556152 |
|
2024/03/15 02:07:50 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9572283 | 0.9886936 | 0.9950871 | 0.0731142 | 1.5668887 | 0.0308406 | 0.1077591 | 9.5263897 | 0.2176418 | 1.1755943 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 02:09:47 - patchstitcher - INFO - Epoch: [13/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.4000838994979858 - coarse_loss: 1.4000838994979858 |
|
2024/03/15 02:11:41 - patchstitcher - INFO - Epoch: [13/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.147301435470581 - coarse_loss: 1.147301435470581 |
|
2024/03/15 02:13:39 - patchstitcher - INFO - Epoch: [13/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9417784214019775 - coarse_loss: 0.9417784214019775 |
|
2024/03/15 02:15:36 - patchstitcher - INFO - Epoch: [13/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.898872971534729 - coarse_loss: 0.898872971534729 |
|
2024/03/15 02:18:53 - patchstitcher - INFO - Epoch: [14/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6218137741088867 - coarse_loss: 0.6218137741088867 |
|
2024/03/15 02:20:49 - patchstitcher - INFO - Epoch: [14/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9591147303581238 - coarse_loss: 0.9591147303581238 |
|
2024/03/15 02:22:42 - patchstitcher - INFO - Epoch: [14/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.7330798506736755 - coarse_loss: 0.7330798506736755 |
|
2024/03/15 02:24:37 - patchstitcher - INFO - Epoch: [14/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.671249508857727 - coarse_loss: 0.671249508857727 |
|
2024/03/15 02:26:12 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9656246 | 0.9891472 | 0.9951051 | 0.0614303 | 1.5103214 | 0.0264747 | 0.1001097 | 9.4011921 | 0.1946697 | 1.0991172 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 02:28:10 - patchstitcher - INFO - Epoch: [15/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7798411846160889 - coarse_loss: 0.7798411846160889 |
|
2024/03/15 02:30:02 - patchstitcher - INFO - Epoch: [15/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9757544994354248 - coarse_loss: 0.9757544994354248 |
|
2024/03/15 02:31:49 - patchstitcher - INFO - Epoch: [15/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.1485944986343384 - coarse_loss: 1.1485944986343384 |
|
2024/03/15 02:33:42 - patchstitcher - INFO - Epoch: [15/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8730670809745789 - coarse_loss: 0.8730670809745789 |
|
2024/03/15 02:36:57 - patchstitcher - INFO - Epoch: [16/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0859500169754028 - coarse_loss: 1.0859500169754028 |
|
2024/03/15 02:38:46 - patchstitcher - INFO - Epoch: [16/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9123729467391968 - coarse_loss: 0.9123729467391968 |
|
2024/03/15 02:40:36 - patchstitcher - INFO - Epoch: [16/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.0700657367706299 - coarse_loss: 1.0700657367706299 |
|
2024/03/15 02:42:24 - patchstitcher - INFO - Epoch: [16/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8393980264663696 - coarse_loss: 1.8393980264663696 |
|
2024/03/15 02:43:58 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9678091 | 0.9892931 | 0.9952321 | 0.0607629 | 1.488932 | 0.0257705 | 0.0981663 | 9.0934609 | 0.1966839 | 1.0878515 | |
|
+-----------+-----------+-----------+-----------+----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 02:45:51 - patchstitcher - INFO - Epoch: [17/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7053128480911255 - coarse_loss: 0.7053128480911255 |
|
2024/03/15 02:47:43 - patchstitcher - INFO - Epoch: [17/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.9886703491210938 - coarse_loss: 0.9886703491210938 |
|
2024/03/15 02:49:32 - patchstitcher - INFO - Epoch: [17/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.180053949356079 - coarse_loss: 1.180053949356079 |
|
2024/03/15 02:51:22 - patchstitcher - INFO - Epoch: [17/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.316230297088623 - coarse_loss: 1.316230297088623 |
|
2024/03/15 02:54:39 - patchstitcher - INFO - Epoch: [18/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.7665231227874756 - coarse_loss: 0.7665231227874756 |
|
2024/03/15 02:56:30 - patchstitcher - INFO - Epoch: [18/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6590834856033325 - coarse_loss: 0.6590834856033325 |
|
2024/03/15 02:58:17 - patchstitcher - INFO - Epoch: [18/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.9268083572387695 - coarse_loss: 0.9268083572387695 |
|
2024/03/15 03:00:07 - patchstitcher - INFO - Epoch: [18/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.255874752998352 - coarse_loss: 1.255874752998352 |
|
2024/03/15 03:01:42 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9691702 | 0.9894969 | 0.9952754 | 0.0559551 | 1.4743834 | 0.0240017 | 0.0943829 | 8.8561864 | 0.1819411 | 1.0395958 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 03:03:35 - patchstitcher - INFO - Epoch: [19/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6205350756645203 - coarse_loss: 0.6205350756645203 |
|
2024/03/15 03:05:29 - patchstitcher - INFO - Epoch: [19/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6529569625854492 - coarse_loss: 0.6529569625854492 |
|
2024/03/15 03:07:18 - patchstitcher - INFO - Epoch: [19/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.8907508850097656 - coarse_loss: 0.8907508850097656 |
|
2024/03/15 03:09:13 - patchstitcher - INFO - Epoch: [19/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5823774337768555 - coarse_loss: 0.5823774337768555 |
|
2024/03/15 03:12:22 - patchstitcher - INFO - Epoch: [20/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.3379265069961548 - coarse_loss: 1.3379265069961548 |
|
2024/03/15 03:14:13 - patchstitcher - INFO - Epoch: [20/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.615516185760498 - coarse_loss: 0.615516185760498 |
|
2024/03/15 03:16:04 - patchstitcher - INFO - Epoch: [20/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5864847302436829 - coarse_loss: 0.5864847302436829 |
|
2024/03/15 03:17:54 - patchstitcher - INFO - Epoch: [20/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.9669459462165833 - coarse_loss: 0.9669459462165833 |
|
2024/03/15 03:19:29 - patchstitcher - INFO - Evaluation Summary: |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
| 0.9697102 | 0.9895742 | 0.9953449 | 0.0539071 | 1.4497501 | 0.0229091 | 0.0925752 | 8.7784555 | 0.1802817 | 1.0580258 | |
|
+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
|
2024/03/15 03:21:25 - patchstitcher - INFO - Epoch: [21/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9557666778564453 - coarse_loss: 0.9557666778564453 |
|
2024/03/15 03:23:15 - patchstitcher - INFO - Epoch: [21/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6958411931991577 - coarse_loss: 0.6958411931991577 |
|
2024/03/15 03:25:01 - patchstitcher - INFO - Epoch: [21/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5607629418373108 - coarse_loss: 0.5607629418373108 |
|
2024/03/15 03:26:54 - patchstitcher - INFO - Epoch: [21/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.8118071556091309 - coarse_loss: 1.8118071556091309 |
|
2024/03/15 03:30:05 - patchstitcher - INFO - Epoch: [22/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 1.0183720588684082 - coarse_loss: 1.0183720588684082 |
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2024/03/15 03:31:53 - patchstitcher - INFO - Epoch: [22/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.0083253383636475 - coarse_loss: 1.0083253383636475 |
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2024/03/15 03:33:45 - patchstitcher - INFO - Epoch: [22/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.5852430462837219 - coarse_loss: 0.5852430462837219 |
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2024/03/15 03:35:35 - patchstitcher - INFO - Epoch: [22/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.8135958909988403 - coarse_loss: 0.8135958909988403 |
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2024/03/15 03:37:10 - patchstitcher - INFO - Evaluation Summary: |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
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| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
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| 0.9699838 | 0.9896694 | 0.9953757 | 0.0526183 | 1.4463599 | 0.0224501 | 0.0915034 | 8.7251583 | 0.1771403 | 1.0479052 | |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+ |
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2024/03/15 03:39:04 - patchstitcher - INFO - Epoch: [23/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.6198912262916565 - coarse_loss: 0.6198912262916565 |
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2024/03/15 03:40:57 - patchstitcher - INFO - Epoch: [23/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 1.1995759010314941 - coarse_loss: 1.1995759010314941 |
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2024/03/15 03:42:47 - patchstitcher - INFO - Epoch: [23/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 1.7696393728256226 - coarse_loss: 1.7696393728256226 |
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2024/03/15 03:44:34 - patchstitcher - INFO - Epoch: [23/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 1.4660639762878418 - coarse_loss: 1.4660639762878418 |
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2024/03/15 03:47:48 - patchstitcher - INFO - Epoch: [24/24] - Step: [00100/00475] - Time: [1/1] - Total Loss: 0.9167467355728149 - coarse_loss: 0.9167467355728149 |
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2024/03/15 03:49:40 - patchstitcher - INFO - Epoch: [24/24] - Step: [00200/00475] - Time: [1/1] - Total Loss: 0.6638955473899841 - coarse_loss: 0.6638955473899841 |
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2024/03/15 03:51:31 - patchstitcher - INFO - Epoch: [24/24] - Step: [00300/00475] - Time: [1/1] - Total Loss: 0.4969237446784973 - coarse_loss: 0.4969237446784973 |
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2024/03/15 03:53:18 - patchstitcher - INFO - Epoch: [24/24] - Step: [00400/00475] - Time: [1/1] - Total Loss: 0.5059656500816345 - coarse_loss: 0.5059656500816345 |
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2024/03/15 03:54:52 - patchstitcher - INFO - Evaluation Summary: |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
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| a1 | a2 | a3 | abs_rel | rmse | log_10 | rmse_log | silog | sq_rel | see | |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
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| 0.9701259 | 0.9896677 | 0.9953767 | 0.0521426 | 1.4457442 | 0.0222779 | 0.0914182 | 8.7319618 | 0.1776046 | 1.046505 | |
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+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+-----------+----------+ |
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2024/03/15 03:54:52 - patchstitcher - INFO - Saving ckp, but use the inner get_save_dict fuction to get model_dict |
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2024/03/15 03:54:52 - patchstitcher - INFO - For saving space. Would you like to save base model several times? :> |
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2024/03/15 03:54:52 - patchstitcher - INFO - save checkpoint_24.pth at ./work_dir/depthanything_vits_u4k/coarse_pretrain |
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