Comments (13)
moranxiachong
commented on June 19, 2024
1
fixed vis bugs:
- remove preds transform back process
- vis.py: change these joint[0]s and joint[1]s to local variables
from hrnet-for-fashion-landmark-estimation.pytorch.
moranxiachong
commented on June 19, 2024
1
fixed vis bugs:
- remove preds transform back process
- vis.py: change these joint[0]s and joint[1]s to local variables
I'm unable to understand point-2. Can you provide your vis.py?
all codes like this:
joint[0] = x * width + padding + joint[0]
joint[1] = y * height + padding + joint[1]
modified:
joint_0 = x * width + padding + joint[0]
joint_1 = y * height + padding + joint[1]
and ofcourse codes related:
cv2.circle(ndarr, (int(joint_0), int(joint_1)), 2 ...
point-2 fixed the bug occurred only when multi debug save_image switches opened
from hrnet-for-fashion-landmark-estimation.pytorch.
Dieselmarble
commented on June 19, 2024
1
Seems this works:
In vis.py
scaling_factor_x = ndarr.shape[0]/96
scaling_factor_y = ndarr.shape[1]/72/nrow
joint_x = x * width + padding + joint[0]*scaling_factor_x
joint_y = y * height + padding + joint[1]*scaling_factor_y
from hrnet-for-fashion-landmark-estimation.pytorch.
ShenhanQian
commented on June 19, 2024
Could you please check the mAP metric, so that we can know if the problem is with the visualization script or with the model itself?
from hrnet-for-fashion-landmark-estimation.pytorch.
ousinkou
commented on June 19, 2024
Thanks for the reply. But sorry I can't find the mAP in the log. My log is below.
2020-12-07 16:51:12,290 Namespace(cfg='experiments/deepfashion2/hrnet/w48_384x288_adam_lr1e-3.yaml', dataDir='', logDir='', modelDir='', opts=['TEST.MODEL_FILE', 'work_dir/save/models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth', 'TEST.USE_GT_BBOX', 'True'], prevModelDir='')
2020-12-07 16:51:12,290 AUTO_RESUME: False
CUDNN:
BENCHMARK: True
DETERMINISTIC: False
ENABLED: True
DATASET:
COLOR_RGB: False
DATASET: deepfashion2
DATA_FORMAT: jpg
FLIP: True
HYBRID_JOINTS_TYPE:
MINI_DATASET: False
NUM_JOINTS_HALF_BODY: 8
PROB_HALF_BODY: 0.3
ROOT: data/deepfashion2/
ROT_FACTOR: 15
SCALE_FACTOR: 0.1
SELECT_CAT: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
SELECT_DATA: False
TEST_SET: validation
TRAIN_SET: train
DATA_DIR:
DEBUG:
DEBUG: True
SAVE_BATCH_IMAGES_GT: False
SAVE_BATCH_IMAGES_GT_PRED: True
SAVE_BATCH_IMAGES_PRED: False
SAVE_HEATMAPS_GT: False
SAVE_HEATMAPS_PRED: False
GPUS: (1,)
LOG_DIR: log
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: [48, 96]
NUM_MODULES: 1
STAGE3:
BLOCK: BASIC
FUSE_METHOD: SUM
NUM_BLOCKS: [4, 4, 4]
NUM_BRANCHES: 3
NUM_CHANNELS: [48, 96, 192]
NUM_MODULES: 4
STAGE4:
BLOCK: BASIC
FUSE_METHOD: SUM
NUM_BLOCKS: [4, 4, 4, 4]
NUM_BRANCHES: 4
NUM_CHANNELS: [48, 96, 192, 384]
NUM_MODULES: 3
GCN_INPUT_FEATURE_DIM: 48
GCN_NODE_FEATURE_DIM: 256
GCN_NUM_LAYERS: 3
GCN_NUM_NODES: 294
HEATMAP_SIZE: [72, 96]
IMAGE_SIZE: [288, 384]
INIT_WEIGHTS: True
NAME: pose_hrnet
NUM_JOINTS: 294
PRETRAINED:
SIGMA: 2
TAG_PER_JOINT: True
TARGET_TYPE: gaussian
OUTPUT_DIR: output
PIN_MEMORY: True
PRINT_FREQ: 100
RANK: 0
TAG:
TEST:
BATCH_SIZE_PER_GPU: 8
BBOX_THRE: 1.0
COCO_BBOX_FILE:
DEEPFASHION2_BBOX_FILE:
FLIP_TEST: True
IMAGE_THRE: 0.0
IN_VIS_THRE: 0.2
MODEL_FILE: work_dir/save/models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth
NMS_THRE: 1.0
OKS_THRE: 0.9
POST_PROCESS: True
SHIFT_HEATMAP: True
SOFT_NMS: False
USE_GT_BBOX: True
TRAIN:
BATCH_SIZE_PER_GPU: 8
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: 0.0
WORKERS: 4
2020-12-07 16:51:12,305 Let's use 1 GPUs!
2020-12-07 16:51:12,713 => loading model from work_dir/save/models/pose_hrnet-w48_384x288-deepfashion2_mAP_0.7017.pth
2020-12-07 16:51:21,696 => classes: ['background', 'short_sleeved_shirt', 'long_sleeved_shirt', 'short_sleeved_outwear', 'long_sleeved_outwear', 'vest', 'sling', 'shorts', 'trousers', 'skirt', 'short_sleeved_dress', 'long_sleeved_dress', 'vest_dress', 'sling_dress']
2020-12-07 16:51:21,696 => num_images: 32153
2020-12-07 16:51:38,257 => load 52239 samples
2020-12-07 16:51:38,258 => Start testing...
2020-12-07 16:51:39,959 Test: [0/6530] Time 1.699 (1.699) Loss 0.00073 (0.00073) acc 0.762 (0.762)
2020-12-07 16:52:31,337 Test: [100/6530] Time 0.517 (0.526) Loss 0.00072 (0.00071) acc 0.829 (0.830)
2020-12-07 16:53:24,311 Test: [200/6530] Time 0.521 (0.528) Loss 0.00049 (0.00070) acc 0.900 (0.828)
2020-12-07 16:54:17,274 Test: [300/6530] Time 0.529 (0.528) Loss 0.00049 (0.00070) acc 0.857 (0.824)
2020-12-07 16:55:11,253 Test: [400/6530] Time 0.532 (0.531) Loss 0.00105 (0.00070) acc 0.720 (0.828)
2020-12-07 16:56:04,546 Test: [500/6530] Time 0.527 (0.532) Loss 0.00077 (0.00069) acc 0.819 (0.827)
2020-12-07 16:56:58,243 Test: [600/6530] Time 0.538 (0.532) Loss 0.00066 (0.00069) acc 0.814 (0.828)
2020-12-07 16:57:51,840 Test: [700/6530] Time 0.568 (0.533) Loss 0.00043 (0.00069) acc 0.944 (0.829)
2020-12-07 16:58:45,210 Test: [800/6530] Time 0.527 (0.533) Loss 0.00097 (0.00069) acc 0.695 (0.830)
2020-12-07 16:59:38,753 Test: [900/6530] Time 0.528 (0.533) Loss 0.00040 (0.00068) acc 0.942 (0.831)
2020-12-07 17:00:32,049 Test: [1000/6530] Time 0.529 (0.533) Loss 0.00067 (0.00069) acc 0.885 (0.829)
2020-12-07 17:01:25,238 Test: [1100/6530] Time 0.524 (0.533) Loss 0.00051 (0.00069) acc 0.861 (0.828)
2020-12-07 17:02:18,380 Test: [1200/6530] Time 0.538 (0.533) Loss 0.00057 (0.00069) acc 0.891 (0.829)
2020-12-07 17:03:11,405 Test: [1300/6530] Time 0.526 (0.533) Loss 0.00072 (0.00069) acc 0.773 (0.828)
2020-12-07 17:04:04,520 Test: [1400/6530] Time 0.525 (0.533) Loss 0.00042 (0.00069) acc 0.941 (0.828)
2020-12-07 17:04:58,150 Test: [1500/6530] Time 0.531 (0.533) Loss 0.00044 (0.00069) acc 0.973 (0.827)
2020-12-07 17:05:51,959 Test: [1600/6530] Time 0.534 (0.533) Loss 0.00071 (0.00069) acc 0.880 (0.827)
2020-12-07 17:06:45,249 Test: [1700/6530] Time 0.530 (0.533) Loss 0.00066 (0.00069) acc 0.782 (0.827)
2020-12-07 17:07:38,836 Test: [1800/6530] Time 0.530 (0.533) Loss 0.00082 (0.00069) acc 0.743 (0.827)
2020-12-07 17:08:32,303 Test: [1900/6530] Time 0.535 (0.533) Loss 0.00093 (0.00069) acc 0.764 (0.828)
2020-12-07 17:09:26,540 Test: [2000/6530] Time 0.537 (0.534) Loss 0.00024 (0.00068) acc 1.000 (0.829)
2020-12-07 17:10:20,748 Test: [2100/6530] Time 0.536 (0.534) Loss 0.00066 (0.00068) acc 0.906 (0.832)
2020-12-07 17:11:14,560 Test: [2200/6530] Time 0.535 (0.534) Loss 0.00076 (0.00067) acc 0.865 (0.834)
2020-12-07 17:12:08,278 Test: [2300/6530] Time 0.534 (0.535) Loss 0.00055 (0.00067) acc 0.872 (0.836)
2020-12-07 17:13:01,882 Test: [2400/6530] Time 0.528 (0.535) Loss 0.00035 (0.00067) acc 0.994 (0.838)
2020-12-07 17:13:55,797 Test: [2500/6530] Time 0.545 (0.535) Loss 0.00050 (0.00067) acc 0.910 (0.839)
2020-12-07 17:14:49,718 Test: [2600/6530] Time 0.532 (0.535) Loss 0.00035 (0.00066) acc 0.994 (0.840)
2020-12-07 17:15:43,308 Test: [2700/6530] Time 0.531 (0.535) Loss 0.00053 (0.00066) acc 0.781 (0.842)
2020-12-07 17:16:36,493 Test: [2800/6530] Time 0.534 (0.535) Loss 0.00072 (0.00065) acc 0.871 (0.843)
2020-12-07 17:17:30,637 Test: [2900/6530] Time 0.533 (0.535) Loss 0.00074 (0.00065) acc 0.850 (0.845)
2020-12-07 17:18:24,826 Test: [3000/6530] Time 0.530 (0.535) Loss 0.00041 (0.00065) acc 0.882 (0.846)
2020-12-07 17:19:18,696 Test: [3100/6530] Time 0.544 (0.535) Loss 0.00039 (0.00065) acc 0.934 (0.847)
2020-12-07 17:20:12,405 Test: [3200/6530] Time 0.585 (0.536) Loss 0.00082 (0.00065) acc 0.778 (0.847)
2020-12-07 17:21:06,034 Test: [3300/6530] Time 0.536 (0.536) Loss 0.00064 (0.00064) acc 0.882 (0.849)
2020-12-07 17:21:58,961 Test: [3400/6530] Time 0.528 (0.535) Loss 0.00053 (0.00064) acc 0.922 (0.849)
2020-12-07 17:22:53,730 Test: [3500/6530] Time 0.624 (0.536) Loss 0.00049 (0.00064) acc 0.881 (0.851)
2020-12-07 17:23:48,798 Test: [3600/6530] Time 0.530 (0.536) Loss 0.00050 (0.00064) acc 0.931 (0.852)
2020-12-07 17:24:42,763 Test: [3700/6530] Time 0.570 (0.536) Loss 0.00060 (0.00064) acc 0.906 (0.853)
2020-12-07 17:25:37,084 Test: [3800/6530] Time 0.527 (0.536) Loss 0.00081 (0.00064) acc 0.855 (0.854)
2020-12-07 17:26:30,096 Test: [3900/6530] Time 0.527 (0.536) Loss 0.00070 (0.00064) acc 0.864 (0.854)
2020-12-07 17:27:23,219 Test: [4000/6530] Time 0.525 (0.536) Loss 0.00057 (0.00064) acc 0.932 (0.855)
2020-12-07 17:28:18,799 Test: [4100/6530] Time 0.603 (0.537) Loss 0.00036 (0.00064) acc 0.995 (0.856)
2020-12-07 17:29:16,117 Test: [4200/6530] Time 0.558 (0.537) Loss 0.00046 (0.00063) acc 0.894 (0.856)
2020-12-07 17:30:12,638 Test: [4300/6530] Time 0.552 (0.538) Loss 0.00069 (0.00063) acc 0.823 (0.857)
2020-12-07 17:31:09,642 Test: [4400/6530] Time 0.554 (0.539) Loss 0.00054 (0.00063) acc 0.914 (0.858)
2020-12-07 17:32:05,689 Test: [4500/6530] Time 0.547 (0.539) Loss 0.00040 (0.00063) acc 0.907 (0.858)
2020-12-07 17:33:01,255 Test: [4600/6530] Time 0.537 (0.540) Loss 0.00074 (0.00063) acc 0.822 (0.858)
2020-12-07 17:33:54,588 Test: [4700/6530] Time 0.529 (0.540) Loss 0.00065 (0.00063) acc 0.830 (0.859)
2020-12-07 17:34:48,093 Test: [4800/6530] Time 0.533 (0.539) Loss 0.00043 (0.00063) acc 0.945 (0.860)
2020-12-07 17:35:41,775 Test: [4900/6530] Time 0.530 (0.539) Loss 0.00052 (0.00062) acc 0.854 (0.861)
2020-12-07 17:36:36,157 Test: [5000/6530] Time 0.564 (0.539) Loss 0.00052 (0.00062) acc 0.925 (0.861)
2020-12-07 17:37:33,585 Test: [5100/6530] Time 0.554 (0.540) Loss 0.00030 (0.00062) acc 1.000 (0.862)
2020-12-07 17:38:31,235 Test: [5200/6530] Time 0.532 (0.541) Loss 0.00056 (0.00062) acc 0.820 (0.862)
2020-12-07 17:39:24,817 Test: [5300/6530] Time 0.532 (0.541) Loss 0.00051 (0.00062) acc 0.878 (0.862)
2020-12-07 17:40:18,777 Test: [5400/6530] Time 0.543 (0.541) Loss 0.00067 (0.00062) acc 0.912 (0.863)
2020-12-07 17:41:27,945 Test: [5500/6530] Time 0.577 (0.543) Loss 0.00049 (0.00062) acc 0.942 (0.863)
2020-12-07 17:42:45,015 Test: [5600/6530] Time 0.604 (0.548) Loss 0.00073 (0.00062) acc 0.828 (0.864)
2020-12-07 17:43:45,152 Test: [5700/6530] Time 0.636 (0.548) Loss 0.00055 (0.00062) acc 0.927 (0.864)
2020-12-07 17:44:46,309 Test: [5800/6530] Time 0.623 (0.550) Loss 0.00092 (0.00062) acc 0.761 (0.864)
2020-12-07 17:45:46,572 Test: [5900/6530] Time 0.593 (0.550) Loss 0.00043 (0.00062) acc 0.992 (0.864)
2020-12-07 17:46:42,447 Test: [6000/6530] Time 0.552 (0.551) Loss 0.00046 (0.00062) acc 0.950 (0.865)
2020-12-07 17:47:37,999 Test: [6100/6530] Time 0.569 (0.551) Loss 0.00052 (0.00062) acc 0.983 (0.865)
2020-12-07 17:48:33,600 Test: [6200/6530] Time 0.554 (0.551) Loss 0.00047 (0.00062) acc 0.898 (0.865)
2020-12-07 17:49:28,888 Test: [6300/6530] Time 0.544 (0.551) Loss 0.00083 (0.00062) acc 0.803 (0.865)
2020-12-07 17:50:23,733 Test: [6400/6530] Time 0.542 (0.551) Loss 0.00058 (0.00062) acc 0.936 (0.866)
2020-12-07 17:51:17,464 Test: [6500/6530] Time 0.538 (0.551) Loss 0.00054 (0.00062) acc 0.912 (0.866)
2020-12-07 17:52:19,706 => writing results json to output/deepfashion2/pose_hrnet/w48_384x288_adam_lr1e-3/2020-12-07-16-51/results/keypoints_validation_results_0.json
In the keypoints_validation_results_0.json file, there is only scores for every image.
from hrnet-for-fashion-landmark-estimation.pytorch.
ShenhanQian
commented on June 19, 2024
Since the acc value on the last of each line is quite high, I think the problem is in the visualization script. Let's debug.
from hrnet-for-fashion-landmark-estimation.pytorch.
chiraq440
commented on June 19, 2024
@ousinkou were you able to resolve the issue since my log looks exactly as yours.
from hrnet-for-fashion-landmark-estimation.pytorch.
AstitvaSri
commented on June 19, 2024
fixed vis bugs:
- remove preds transform back process
- vis.py: change these joint[0]s and joint[1]s to local variables
I'm unable to understand point-2. Can you provide your vis.py?
from hrnet-for-fashion-landmark-estimation.pytorch.
AstitvaSri
commented on June 19, 2024
fixed vis bugs:
- remove preds transform back process
- vis.py: change these joint[0]s and joint[1]s to local variables
I'm unable to understand point-2. Can you provide your vis.py?
all codes like this:
joint[0] = x * width + padding + joint[0]
joint[1] = y * height + padding + joint[1]modified:
joint_0 = x * width + padding + joint[0]
joint_1 = y * height + padding + joint[1]and ofcourse codes related:
cv2.circle(ndarr, (int(joint_0), int(joint_1)), 2 ...point-2 fixed the bug occurred only when multi debug save_image switches opened
@moranxiachong Thanks. It worked.
from hrnet-for-fashion-landmark-estimation.pytorch.
chiraq440
commented on June 19, 2024
fixed vis bugs:
- remove preds transform back process
- vis.py: change these joint[0]s and joint[1]s to local variables
I'm unable to understand point-2. Can you provide your vis.py?
all codes like this:
joint[0] = x * width + padding + joint[0]
joint[1] = y * height + padding + joint[1]
modified:
joint_0 = x * width + padding + joint[0]
joint_1 = y * height + padding + joint[1]
and ofcourse codes related:
cv2.circle(ndarr, (int(joint_0), int(joint_1)), 2 ...
point-2 fixed the bug occurred only when multi debug save_image switches opened
@AstitvaSri Unable to understand point-1?
from hrnet-for-fashion-landmark-estimation.pytorch.
chiraq440
commented on June 19, 2024
fixed vis bugs:
1. remove preds transform back process 2. vis.py: change these joint[0]s and joint[1]s to local variables
@moranxiachong Unable to understand point 1.
from hrnet-for-fashion-landmark-estimation.pytorch.
Gzzgz
commented on June 19, 2024
from hrnet-for-fashion-landmark-estimation.pytorch.
Dieselmarble
commented on June 19, 2024
@Gzzgz Are you able to solve this issue? It seems the key-points are affine transformed.
from hrnet-for-fashion-landmark-estimation.pytorch.
Related Issues (13)
- Object Keypoint Similarity HOT 3
- infer on test image HOT 1
- Bbox detector model missing HOT 2
- Landmark key points visualization problem HOT 2
- for catId in catIds} File "/home/sa/anaconda3/envs/torch1.7/lib/python3.6/site-packages/pycocotools/cocoeval.py", line 229, in computeOks e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2 ValueError: operands could not be broadcast together with shapes (294,) (17,) HOT 1
- Visuliaztion problem HOT 1
- how to apply it on new dataset
- Onedrive link has been deleted! HOT 5
- Question about the test result HOT 1
- is top2down or botton2top? i find the model input is raw image not crop image HOT 2
- File 'bbox_result_val.pkl' is not in the OneDrive link HOT 3
- How to predict landmark for new images? HOT 1
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