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TrainerLog.txt
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TrainerLog.txt
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Namespace(cfg='experiments/coco/hrnet/w32_256x256_adam_lr1e-3_ENVISAT-1.yaml', dataDir='', logDir='', modelDir='', opts=[], prevModelDir='')
AUTO_RESUME: True
CUDNN:
BENCHMARK: True
DETERMINISTIC: False
ENABLED: True
DATASET:
COLOR_RGB: False
DATASET: coco
DATA_FORMAT: jpg
FLIP: True
HYBRID_JOINTS_TYPE:
IMG_PREFIX: img
NUM_JOINTS_HALF_BODY: 12
PROB_HALF_BODY: 0.3
ROOT: ../data/Envisat_Set1/
ROT_FACTOR: 45
SCALE_FACTOR: 0.35
SELECT_DATA: False
TEST_SET: val
TRAIN_SET: train
DATA_DIR:
DEBUG:
DEBUG: True
SAVE_BATCH_IMAGES_GT: True
SAVE_BATCH_IMAGES_PRED: True
SAVE_HEATMAPS_GT: True
SAVE_HEATMAPS_PRED: True
SAVE_HEATMAPS_TEST_ALL: False
GPUS: (0,)
LOG_DIR: log/Envisat_Set1
LOSS:
TOPK: 8
USE_DIFFERENT_JOINTS_WEIGHT: False
USE_OHKM: False
USE_TARGET_WEIGHT: True
MODEL:
EXTRA:
FINAL_CONV_KERNEL: 1
PRETRAINED_LAYERS: ['conv1', 'bn1', 'conv2', 'bn2', 'layer1', 'transition1', 'stage2', 'transition2', 'stage3', 'transition3', 'stage4']
STAGE2:
BLOCK: BASIC
FUSE_METHOD: SUM
NUM_BLOCKS: [4, 4]
NUM_BRANCHES: 2
NUM_CHANNELS: [32, 64]
NUM_MODULES: 1
STAGE3:
BLOCK: BASIC
FUSE_METHOD: SUM
NUM_BLOCKS: [4, 4, 4]
NUM_BRANCHES: 3
NUM_CHANNELS: [32, 64, 128]
NUM_MODULES: 4
STAGE4:
BLOCK: BASIC
FUSE_METHOD: SUM
NUM_BLOCKS: [4, 4, 4, 4]
NUM_BRANCHES: 4
NUM_CHANNELS: [32, 64, 128, 256]
NUM_MODULES: 3
HEATMAP_SIZE: [64, 64]
IMAGE_SIZE: [256, 256]
INIT_WEIGHTS: True
NAME: pose_hrnet
NUM_JOINTS: 16
PRETRAINED: pretrained/pose_hrnet_w32_256x256.pth
SIGMA: 2
TAG_PER_JOINT: True
TARGET_TYPE: gaussian
OUTPUT_DIR: output/Envisat_Set1
PIN_MEMORY: True
PRINT_FREQ: 100
RANK: 0
TEST:
BATCH_SIZE_PER_GPU: 32
BBOX_THRE: 1.0
COCO_BBOX_FILE: ../data/Envisat_Set1/val.json
FLIP_TEST: False
IMAGE_THRE: 0.0
IN_VIS_THRE: 0.2
MODEL_FILE:
NMS_THRE: 1.0
OKS_THRE: 0.9
POST_PROCESS: True
SHIFT_HEATMAP: False
SOFT_NMS: False
USE_GT_BBOX: True
TRAIN:
BATCH_SIZE_PER_GPU: 32
BEGIN_EPOCH: 0
CHECKPOINT:
END_EPOCH: 210
GAMMA1: 0.99
GAMMA2: 0.0
LR: 0.001
LR_FACTOR: 0.1
LR_STEP: [170, 200]
MOMENTUM: 0.9
NESTEROV: False
OPTIMIZER: adam
RESUME: False
SHUFFLE: True
WD: 0.0001
WORKERS: 24
=> init weights from normal distribution
=> loading pretrained model pretrained/pose_hrnet_w32_256x256.pth
Generating grammar tables from /usr/lib/python3.6/lib2to3/Grammar.txt
Generating grammar tables from /usr/lib/python3.6/lib2to3/PatternGrammar.txt
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/upsampling.py:129: UserWarning: nn.Upsample is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.{} is deprecated. Use nn.functional.interpolate instead.".format(self.name))
Total Parameters: 28,536,080
----------------------------------------------------------------------------------------------------------------------------------
Total Multiply Adds (For Convolution and Linear Layers only): 9.4931640625 GFLOPs
----------------------------------------------------------------------------------------------------------------------------------
Number of Layers
Conv2d : 293 layers BatchNorm2d : 292 layers ReLU : 261 layers Bottleneck : 4 layers BasicBlock : 104 layers Upsample : 28 layers HighResolutionModule : 8 layers
=> classes: ['__background__', 'Envisat']
=> num_images: 35000
=> load 35000 samples
=> classes: ['__background__', 'Envisat']
=> num_images: 9604
=> load 9604 samples
=> loading checkpoint 'output/Envisat_Set1/coco/pose_hrnet/w32_256x256_adam_lr1e-3_ENVISAT-1/checkpoint.pth'
=> loaded checkpoint 'output/Envisat_Set1/coco/pose_hrnet/w32_256x256_adam_lr1e-3_ENVISAT-1/checkpoint.pth' (epoch 72)
=> creating output/Envisat_Set1/coco/pose_hrnet/w32_256x256_adam_lr1e-3_ENVISAT-1
=> creating log/Envisat_Set1/coco/pose_hrnet/w32_256x256_adam_lr1e-3_ENVISAT-1_2020-08-03-01-13
loading annotations into memory...
Done (t=1.02s)
creating index...
index created!
loading annotations into memory...
Done (t=0.23s)
creating index...
index created!