arjundd/meddlr-data · Datasets at Hugging Face

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[01/08 07:53:48] mr.evaluation.evaluator INFO: Inference done 299/320. 0.0225 s / img. ETA=0:00:03
[01/08 07:53:52] mr.evaluation.evaluator INFO: Total inference time: 0:00:49.495082 (0.157127 s / batch on 1 devices)
[01/08 07:53:52] mr.evaluation.evaluator INFO: Total inference pure compute time: 0:00:07 (0.022384 s / batch on 1 devices)
[01/08 07:54:01] mr.evaluation.scan_evaluator INFO: Structuring slices into volumes...
[01/08 07:54:01] mr.evaluation.scan_evaluator INFO: Structuring slices into volumes...
[01/08 07:54:05] mr.evaluation.recon_evaluation INFO: [ReconEvaluator] Slice metrics summary:
channel_0
--------------- --------------
val_nrmse 0.189 (0.041)
val_nrmse_mag 0.117 (0.032)
val_psnr 32.410 (2.903)
val_psnr_mag 36.686 (3.132)
val_ssim (Wang) 0.858 (0.081)

[01/08 07:54:05] mr.evaluation.recon_evaluation INFO: [ReconEvaluator] Scan metrics summary:
channel_0
-------------------- --------------
val_nrmse_mag_scan 0.107 (0.005)
val_nrmse_scan 0.177 (0.004)
val_psnr_mag_scan 44.659 (0.393)
val_psnr_scan 40.292 (0.600)
val_ssim (Wang)_scan 0.959 (0.005)

[01/08 07:54:05] mr.evaluation.evaluator INFO: Evaluation Time: 13.055382 s
[01/08 07:54:05] mr.engine.trainer INFO: Evaluation results for mridata_knee_2019_val in csv format:
[01/08 07:54:05] mr.evaluation.testing INFO: copypaste: val_nrmse,val_nrmse_mag,val_psnr,val_psnr_mag,val_ssim (Wang),val_nrmse_mag_scan,val_nrmse_scan,val_psnr_mag_scan,val_psnr_scan,val_ssim (Wang)_scan
[01/08 07:54:05] mr.evaluation.testing INFO: copypaste: 0.1889,0.1167,32.4100,36.6860,0.8582,0.1071,0.1770,44.6590,40.2923,0.9595
[01/08 07:54:05] mr.evaluation.testing INFO: Metrics (comma delimited):
val_nrmse,val_nrmse_mag,val_psnr,val_psnr_mag,val_ssim (Wang),val_nrmse_mag_scan,val_nrmse_scan,val_psnr_mag_scan,val_psnr_scan,val_ssim (Wang)_scan
0.1889,0.1167,32.4100,36.6860,0.8582,0.1071,0.1770,44.6590,40.2923,0.9595
[01/08 07:54:05] mr.utils.events INFO: eta: 0:00:00 iter: 1599 loss: 13840.166 total_loss: 13840.166 time: 0.2415 data_time: 0.0001 lr: 0.000100 max_mem: 4245M
[01/08 07:54:05] mr.engine.hooks INFO: Overall training speed: 1597 iterations in 0:06:25 (0.2417 s / it)
[01/08 07:54:05] mr.engine.hooks INFO: Total training time: 0:15:56 (0:09:30 on hooks)
[01/08 07:36:30] meddlr INFO: Running in debug mode
[01/08 07:36:30] meddlr INFO: Environment info:
------------------- ----------------------------------------------------------------------------------------------
sys.platform linux
Python 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
numpy 1.20.3
PyTorch 1.7.1 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torch
PyTorch debug build False
CUDA available False
Pillow 8.4.0
torchvision 0.8.2 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torchvision
SLURM_JOB_ID slurm not detected
------------------- ----------------------------------------------------------------------------------------------
PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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,

[01/08 07:36:30] meddlr INFO: Command line arguments: Namespace(auto_version=False, config_file='../configs/tests/basic.yaml', debug=True, devices=None, eval_only=False, num_gpus=1, opts=['DATALOADER.NUM_WORKERS', '8', 'SOLVER.MAX_ITER', '1600', 'SOLVER.CHECKPOINT_PERIOD', '200', 'TEST.EVAL_PERIOD', '200'], reproducible=False, restart_iter=False, resume=False)
[01/08 07:36:30] meddlr INFO: Contents of args.config_file=../configs/tests/basic.yaml:
# Basic testing config
# Use this for any testing you may want to do in the future.
# The model will be trained for 60 iterations (not epochs)
# on the mridata.org 2019 knee dataset.
MODEL:
UNROLLED:
NUM_UNROLLED_STEPS: 8
NUM_RESBLOCKS: 2
NUM_FEATURES: 128
DROPOUT: 0.
DATASETS:
TRAIN: ("mridata_knee_2019_train",)
VAL: ("mridata_knee_2019_val",)
TEST: ("mridata_knee_2019_test",)
DATALOADER:
NUM_WORKERS: 0 # for debugging purposes
SOLVER:
TRAIN_BATCH_SIZE: 1
TEST_BATCH_SIZE: 2
CHECKPOINT_PERIOD: 20
MAX_ITER: 80
TEST:
EVAL_PERIOD: 40
VIS_PERIOD: 20
TIME_SCALE: "iter"
OUTPUT_DIR: "results://tests/basic"
VERSION: 1
[01/08 07:37:52] meddlr INFO: Running in debug mode
[01/08 07:37:54] meddlr INFO: Environment info:
---------------------- ----------------------------------------------------------------------------------------------
sys.platform linux
Python 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
numpy 1.20.3
PyTorch 1.7.1 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torch
PyTorch debug build False
CUDA available True
GPU 0 GeForce RTX 2080 Ti
CUDA_HOME /usr/local/cuda
NVCC Cuda compilation tools, release 9.0, V9.0.176
Pillow 8.4.0
torchvision 0.8.2 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torchvision
torchvision arch flags sm_35, sm_50, sm_60, sm_70, sm_75

[01/08 07:53:48] mr.evaluation.evaluator INFO: Inference done 299/320. 0.0225 s / img. ETA=0:00:03

[01/08 07:53:52] mr.evaluation.evaluator INFO: Total inference time: 0:00:49.495082 (0.157127 s / batch on 1 devices)

[01/08 07:53:52] mr.evaluation.evaluator INFO: Total inference pure compute time: 0:00:07 (0.022384 s / batch on 1 devices)

[01/08 07:54:01] mr.evaluation.scan_evaluator INFO: Structuring slices into volumes...

[01/08 07:54:05] mr.evaluation.recon_evaluation INFO: [ReconEvaluator] Slice metrics summary:

channel_0

--------------- --------------

val_nrmse 0.189 (0.041)

val_nrmse_mag 0.117 (0.032)

val_psnr 32.410 (2.903)

val_psnr_mag 36.686 (3.132)

val_ssim (Wang) 0.858 (0.081)

[01/08 07:54:05] mr.evaluation.recon_evaluation INFO: [ReconEvaluator] Scan metrics summary:

channel_0

-------------------- --------------

val_nrmse_mag_scan 0.107 (0.005)

val_nrmse_scan 0.177 (0.004)

val_psnr_mag_scan 44.659 (0.393)

val_psnr_scan 40.292 (0.600)

val_ssim (Wang)_scan 0.959 (0.005)

[01/08 07:54:05] mr.evaluation.evaluator INFO: Evaluation Time: 13.055382 s

[01/08 07:54:05] mr.engine.trainer INFO: Evaluation results for mridata_knee_2019_val in csv format:

[01/08 07:54:05] mr.evaluation.testing INFO: copypaste: val_nrmse,val_nrmse_mag,val_psnr,val_psnr_mag,val_ssim (Wang),val_nrmse_mag_scan,val_nrmse_scan,val_psnr_mag_scan,val_psnr_scan,val_ssim (Wang)_scan

[01/08 07:54:05] mr.evaluation.testing INFO: copypaste: 0.1889,0.1167,32.4100,36.6860,0.8582,0.1071,0.1770,44.6590,40.2923,0.9595

[01/08 07:54:05] mr.evaluation.testing INFO: Metrics (comma delimited):

val_nrmse,val_nrmse_mag,val_psnr,val_psnr_mag,val_ssim (Wang),val_nrmse_mag_scan,val_nrmse_scan,val_psnr_mag_scan,val_psnr_scan,val_ssim (Wang)_scan

0.1889,0.1167,32.4100,36.6860,0.8582,0.1071,0.1770,44.6590,40.2923,0.9595

[01/08 07:54:05] mr.utils.events INFO: eta: 0:00:00 iter: 1599 loss: 13840.166 total_loss: 13840.166 time: 0.2415 data_time: 0.0001 lr: 0.000100 max_mem: 4245M

[01/08 07:54:05] mr.engine.hooks INFO: Overall training speed: 1597 iterations in 0:06:25 (0.2417 s / it)

[01/08 07:54:05] mr.engine.hooks INFO: Total training time: 0:15:56 (0:09:30 on hooks)

[01/08 07:36:30] meddlr INFO: Running in debug mode

[01/08 07:36:30] meddlr INFO: Environment info:

------------------- ----------------------------------------------------------------------------------------------

sys.platform linux

Python 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]

numpy 1.20.3

PyTorch 1.7.1 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torch

PyTorch debug build False

CUDA available False

Pillow 8.4.0

torchvision 0.8.2 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torchvision

SLURM_JOB_ID slurm not detected

------------------- ----------------------------------------------------------------------------------------------

PyTorch built with:

- GCC 7.3

- C++ Version: 201402

- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications

- Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)

- OpenMP 201511 (a.k.a. OpenMP 4.5)

- NNPACK is enabled

- CPU capability usage: AVX

- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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,

[01/08 07:36:30] meddlr INFO: Command line arguments: Namespace(auto_version=False, config_file='../configs/tests/basic.yaml', debug=True, devices=None, eval_only=False, num_gpus=1, opts=['DATALOADER.NUM_WORKERS', '8', 'SOLVER.MAX_ITER', '1600', 'SOLVER.CHECKPOINT_PERIOD', '200', 'TEST.EVAL_PERIOD', '200'], reproducible=False, restart_iter=False, resume=False)

[01/08 07:36:30] meddlr INFO: Contents of args.config_file=../configs/tests/basic.yaml:

# Basic testing config

# Use this for any testing you may want to do in the future.

# The model will be trained for 60 iterations (not epochs)

# on the mridata.org 2019 knee dataset.

MODEL:

UNROLLED:

NUM_UNROLLED_STEPS: 8

NUM_RESBLOCKS: 2

NUM_FEATURES: 128

DROPOUT: 0.

DATASETS:

TRAIN: ("mridata_knee_2019_train",)

VAL: ("mridata_knee_2019_val",)

TEST: ("mridata_knee_2019_test",)

DATALOADER:

NUM_WORKERS: 0 # for debugging purposes

SOLVER:

TRAIN_BATCH_SIZE: 1

TEST_BATCH_SIZE: 2

CHECKPOINT_PERIOD: 20

MAX_ITER: 80

TEST:

EVAL_PERIOD: 40

VIS_PERIOD: 20

TIME_SCALE: "iter"

OUTPUT_DIR: "results://tests/basic"

VERSION: 1

[01/08 07:37:52] meddlr INFO: Running in debug mode

[01/08 07:37:54] meddlr INFO: Environment info:

---------------------- ----------------------------------------------------------------------------------------------

sys.platform linux

Python 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]

numpy 1.20.3

PyTorch 1.7.1 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torch

PyTorch debug build False

CUDA available True

GPU 0 GeForce RTX 2080 Ti

CUDA_HOME /usr/local/cuda

NVCC Cuda compilation tools, release 9.0, V9.0.176

Pillow 8.4.0

torchvision 0.8.2 @/bmrNAS/people/arjun/miniconda3/envs/meddlr_env/lib/python3.7/site-packages/torchvision

torchvision arch flags sm_35, sm_50, sm_60, sm_70, sm_75