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nohup.out_test_immagine_singola
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Namespace(cfg='experiments/babypose/w32/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300.yaml', opts=['TEST.MODEL_FILE', 'output/baby_pose_kpt_carbon_image_split/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300/model_best.pth.tar', 'DATASET.MAX_NUM_PEOPLE', '1'])
AUTO_RESUME: True
CUDNN:
BENCHMARK: True
DETERMINISTIC: False
ENABLED: True
DATASET:
BG_WEIGHT: 0.1
CENTER_SIGMA: 4.0
DATASET: baby_pose_kpt
DATASET_TEST: baby_pose
DATA_FORMAT: no_zip
FLIP: 0.5
INPUT_SIZE: 384
MAX_NUM_PEOPLE: 1
MAX_ROTATION: 30
MAX_SCALE: 1.5
MAX_TRANSLATE: 40
MIN_SCALE: 0.75
NUM_JOINTS: 14
OFFSET_RADIUS: 4
OUTPUT_SIZE: 96
ROOT: data/babypose
SCALE_TYPE: short
SIGMA: 2.0
TEST: test
TRAIN: train
VAL: val
DATA_DIR:
DIST_BACKEND: nccl
GPUS: (0,)
LOG_DIR: log
LOSS:
HEATMAPS_LOSS_FACTOR: 1.0
OFFSETS_LOSS_FACTOR: 0.03
WITH_HEATMAPS_LOSS: True
WITH_OFFSETS_LOSS: True
MODEL:
INIT_WEIGHTS: True
NAME: hrnet_dekr
NUM_JOINTS: 14
PRETRAINED: model/pose_crowdpose/pose_dekr_hrnetw32_crowdpose.pth
SPEC:
FINAL_CONV_KERNEL: 1
HEAD_HEATMAP:
BLOCK: BASIC
DILATION_RATE: 1
NUM_BLOCKS: 1
NUM_CHANNELS: 32
HEAD_OFFSET:
BLOCK: ADAPTIVE
DILATION_RATE: 1
NUM_BLOCKS: 2
NUM_CHANNELS_PERKPT: 15
PRETRAINED_LAYERS: ['*']
STAGES:
BLOCK: ['BASIC', 'BASIC', 'BASIC']
FUSE_METHOD: ['SUM', 'SUM', 'SUM']
NUM_BLOCKS: [[4, 4], [4, 4, 4], [4, 4, 4, 4]]
NUM_BRANCHES: [2, 3, 4]
NUM_CHANNELS: [[32, 64], [32, 64, 128], [32, 64, 128, 256]]
NUM_MODULES: [1, 4, 3]
NUM_STAGES: 3
MULTIPROCESSING_DISTRIBUTED: True
NAME: regression
OUTPUT_DIR: output
PIN_MEMORY: True
PRINT_FREQ: 10
RANK: 0
RESCORE:
BATCHSIZE: 1024
DATA_FILE: data/rescore_data/rescore_dataset_train_coco_kpt
END_EPOCH: 20
GET_DATA: False
HIDDEN_LAYER: 256
LR: 0.001
MODEL_FILE: model/rescore/final_rescore_baby_pose_kpt.pth
VALID: True
TEST:
ADJUST_THRESHOLD: 0.05
DECREASE: 0.8
FLIP_TEST: True
GUASSIAN_KERNEL: 6
IMAGES_PER_GPU: 1
KEYPOINT_THRESHOLD: 0.01
LOG_PROGRESS: True
MATCH_HMP: False
MAX_ABSORB_DISTANCE: 75
MODEL_FILE: output/baby_pose_kpt_carbon_image_split/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300/model_best.pth.tar
NMS_NUM_THRE: 7
NMS_THRE: 0.05
POOL_THRESHOLD1: 300
POOL_THRESHOLD2: 200
SCALE_FACTOR: [1]
TRAIN:
BEGIN_EPOCH: 0
CHECKPOINT:
END_EPOCH: 10
GAMMA1: 0.99
GAMMA2: 0.0
IMAGES_PER_GPU: 7
LR: 0.001
LR_FACTOR: 0.1
LR_STEP: [200, 260]
MOMENTUM: 0.9
NESTEROV: False
OPTIMIZER: adam
RESUME: False
SHUFFLE: True
WD: 0.0001
VERBOSE: False
WORKERS: 1
=> loading model from output/baby_pose_kpt_carbon_image_split/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300/model_best.pth.tar
=> classes: ['__background__', 'person']
[codecarbon INFO @ 11:50:35] offline tracker init
[codecarbon INFO @ 11:50:35] [setup] RAM Tracking...
[codecarbon INFO @ 11:50:35] [setup] GPU Tracking...
[codecarbon INFO @ 11:50:35] Tracking Nvidia GPU via pynvml
[codecarbon INFO @ 11:50:35] [setup] CPU Tracking...
[codecarbon WARNING @ 11:50:35] No CPU tracking mode found. Falling back on CPU constant mode.
[codecarbon INFO @ 11:50:37] CPU Model on constant consumption mode: Intel(R) Core(TM) i7-3930K CPU @ 3.20GHz
[codecarbon INFO @ 11:50:37] >>> Tracker's metadata:
[codecarbon INFO @ 11:50:37] Platform system: Linux-5.19.0-46-generic-x86_64-with-glibc2.34
[codecarbon INFO @ 11:50:37] Python version: 3.8.16
[codecarbon INFO @ 11:50:37] CodeCarbon version: 2.2.4
[codecarbon INFO @ 11:50:37] Available RAM : 11.616 GB
[codecarbon INFO @ 11:50:37] CPU count: 12
[codecarbon INFO @ 11:50:37] CPU model: Intel(R) Core(TM) i7-3930K CPU @ 3.20GHz
[codecarbon INFO @ 11:50:37] GPU count: 1
[codecarbon INFO @ 11:50:37] GPU model: 1 x NVIDIA GeForce GTX 1060 6GB
=> creating output/baby_pose_kpt/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300
=> creating log/baby_pose_kpt/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300_2023-07-08-11-50
loading annotations into memory...
Done (t=0.02s)
creating index...
index created!
0%| | 0/1 [00:00<?, ?it/s]/home/claudio/.pyenv/versions/3.8.16/lib/python3.8/site-packages/torch/nn/functional.py:3737: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
/home/claudio/.pyenv/versions/3.8.16/lib/python3.8/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
100%|██████████| 1/1 [00:01<00:00, 1.53s/it]100%|██████████| 1/1 [00:01<00:00, 1.53s/it]
=> Writing results json to output/baby_pose_kpt/hrnet_dekr/w32_4x_reg03_bs10_512_adam_lr1e-3_babypose_x300/results/keypoints_testregression_results.json
| Arch | AP | Ap .5 | AP .75 | AR | AR .5 | AR .75 | AP (easy) | AP (medium) | AP (hard) |
|---|---|---|---|---|---|---|---|---|---|
| hrnet_dekr | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | -1.000 | -1.000 |
****************************************************
Valori medi totali
left_arm : 0.992
right_arm : 0.989
left_leg : 0.993
right_leg : 0.994
Total : 0.992
[codecarbon INFO @ 11:50:39] Energy consumed for RAM : 0.000002 kWh. RAM Power : 4.355958938598633 W
[codecarbon INFO @ 11:50:39] Energy consumed for all GPUs : 0.000015 kWh. Total GPU Power : 34.698 W
[codecarbon INFO @ 11:50:39] Energy consumed for all CPUs : 0.000029 kWh. Total CPU Power : 65.0 W
[codecarbon INFO @ 11:50:39] 0.000046 kWh of electricity used since the beginning.
Testing regression
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *keypoints*
DONE (t=0.00s).
Accumulating evaluation results...
DONE (t=0.00s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 1.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 1.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 1.000
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 1.000
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 1.000
Average Recall (AR) @[ IoU=0.50:0.95 | type= easy | maxDets= 20 ] = 1.000
Average Recall (AR) @[ IoU=0.50:0.95 | type=medium | maxDets= 20 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | type= hard | maxDets= 20 ] = -1.000
Predizioni per immagine: 602546
left_arm : 0.992
right_arm : 0.989
left_leg : 0.993
right_leg : 0.994
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