Visuliaztion problem

[Rudy112]

First, thanks for sharing this great work! Here is some issue that I met.

I try to visualize the result by running the script###

python tools/test.py --cfg experiments/deepfashion2/hrnet/w48_384x288_adam_lr1e-3.yaml TEST.MODEL_FILE models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth TEST.USE_GT_BBOX True DATASET.MINI_DATASET True TAG 'experiment description' WORKERS 4 TEST.BATCH_SIZE_PER_GPU 8 TRAIN.BATCH_SIZE_PER_GPU 8

the config file is

AUTO_RESUME: false #
CUDNN:
BENCHMARK: true
DETERMINISTIC: false
ENABLED: true
DATA_DIR: ''
GPUS: (1,)
OUTPUT_DIR: 'output'
LOG_DIR: 'log'
WORKERS: 8
PRINT_FREQ: 100
PIN_MEMORY: true

DATASET:
COLOR_RGB: false
DATASET: 'deepfashion2'
DATA_FORMAT: jpg
FLIP: true
NUM_JOINTS_HALF_BODY: 8
PROB_HALF_BODY: 0.3
ROOT: 'data/deepfashion2/'
ROT_FACTOR: 15 #45
SCALE_FACTOR: 0.1 #0.35
TEST_SET: 'validation'
TRAIN_SET: 'train'
MINI_DATASET: True
SELECT_CAT: [1,2,3,4,5,6,7,8,9,10,11,12,13]
MODEL:
INIT_WEIGHTS: true
NAME: pose_hrnet
NUM_JOINTS: 294
PRETRAINED: ''
TARGET_TYPE: gaussian
IMAGE_SIZE:

• 288
• 384
  HEATMAP_SIZE:
• 72
• 96
  SIGMA: 2 # 3
  EXTRA:
  PRETRAINED_LAYERS:
  • 'conv1'
  • 'bn1'
  • 'conv2'
  • 'bn2'
  • 'layer1'
  • 'transition1'
  • 'stage2'
  • 'transition2'
  • 'stage3'
  • 'transition3'
  • 'stage4'
    FINAL_CONV_KERNEL: 1
    STAGE2:
    NUM_MODULES: 1
    NUM_BRANCHES: 2
    BLOCK: BASIC
    NUM_BLOCKS:
    • 4
    • 4
      NUM_CHANNELS:
    • 48
    • 96
      FUSE_METHOD: SUM
      STAGE3:
      NUM_MODULES: 4
      NUM_BRANCHES: 3
      BLOCK: BASIC
      NUM_BLOCKS:
    • 4
    • 4
    • 4
      NUM_CHANNELS:
    • 48
    • 96
    • 192
      FUSE_METHOD: SUM
      STAGE4:
      NUM_MODULES: 3
      NUM_BRANCHES: 4
      BLOCK: BASIC
      NUM_BLOCKS:
    • 4
    • 4
    • 4
    • 4
      NUM_CHANNELS:
    • 48
    • 96
    • 192
    • 384
      FUSE_METHOD: SUM
      LOSS:
      USE_TARGET_WEIGHT: true
      TRAIN:
      BATCH_SIZE_PER_GPU: 8
      SHUFFLE: true
      BEGIN_EPOCH: 0
      END_EPOCH: 210
      OPTIMIZER: adam
      LR: 0.001 #0.001
      LR_FACTOR: 0.1
      LR_STEP:
• 170
• 200
  WD: 0.
  GAMMA1: 0.99
  GAMMA2: 0.0
  MOMENTUM: 0.9
  NESTEROV: false
  TEST:
  BATCH_SIZE_PER_GPU: 8
  COCO_BBOX_FILE: ''
  DEEPFASHION2_BBOX_FILE: ''
  BBOX_THRE: 1.0
  IMAGE_THRE: 0.0 # threshold for detected bbox to be feed into HRNet
  IN_VIS_THRE: 0.2
  MODEL_FILE: ''
  NMS_THRE: 1.0
  OKS_THRE: 0.9 # the lower threshold for a peak point in a heatmap to be kept
  USE_GT_BBOX: true
  FLIP_TEST: true
  POST_PROCESS: true
  SHIFT_HEATMAP: true
  DEBUG:
  DEBUG: True
  SAVE_BATCH_IMAGES_GT: false
  SAVE_BATCH_IMAGES_PRED: false
  SAVE_BATCH_IMAGES_GT_PRED: True
  SAVE_HEATMAPS_GT: false
  SAVE_HEATMAPS_PRED: false

I change the CONFIG parameter to True, however it still does not save any image. The image saving only works when I change the BATH_SIZE_PER_GPU to 1. However, the image-saving function is based on a torch grid, thus result in a very wired visualization since the scale of keypoint and output is different. Could you please try to solve the problem? I am using a single GPU RTX 3080TI with Ubuntu 18.04.

[BastianSch]

Hi,

in lib/core/function.py
the iterator i from line 142

HRNet-for-Fashion-Landmark-Estimation.PyTorch/lib/core/function.py

Line 142 in f4ac2e1

for i, (input, target, target_weight, meta) in enumerate(val_loader):

is reused in line 195

HRNet-for-Fashion-Landmark-Estimation.PyTorch/lib/core/function.py

Line 195 in f4ac2e1

for i in range(preds_local.shape[0]):

So I changed it to j:
for j in range(preds_local.shape[0]):
preds[j] = transform_preds( preds_local[j], c[j], s[j], [config.MODEL.HEATMAP_SIZE[0], config.MODEL.HEATMAP_SIZE[1]] )

Did you resolve the issue with the scalings of the keypoints?