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Руслан Бундюк
2025-01-28 21:48:35 +00:00
commit 6e660ddb3c
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47
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/Base-DensePose-RCNN-FPN.yaml Normal file
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
# Detectron1 uses 2000 proposals per-batch,
# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
DENSEPOSE_ON: True
ROI_HEADS:
NAME: "DensePoseROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
NUM_CLASSES: 1
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
POOLER_SAMPLING_RATIO: 2
POOLER_TYPE: "ROIAlign"
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseV1ConvXHead"
POOLER_TYPE: "ROIAlign"
NUM_COARSE_SEGM_CHANNELS: 2
DATASETS:
TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival")
TEST: ("densepose_coco_2014_minival",)
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.01
STEPS: (60000, 80000)
MAX_ITER: 90000
WARMUP_FACTOR: 0.1
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_WC1_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
UV_CONFIDENCE:
ENABLED: True
TYPE: "iid_iso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_WC2_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
UV_CONFIDENCE:
ENABLED: True
TYPE: "indep_aniso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)

10
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_DL_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
SOLVER:
MAX_ITER: 130000
STEPS: (100000, 120000)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC1_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "iid_iso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)
WARMUP_FACTOR: 0.025

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_WC2_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "indep_aniso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)
WARMUP_FACTOR: 0.025

8
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
SOLVER:
MAX_ITER: 130000
STEPS: (100000, 120000)

17
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_101_FPN_s1x_legacy.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
RESNETS:
DEPTH: 101
ROI_DENSEPOSE_HEAD:
NUM_COARSE_SEGM_CHANNELS: 15
POOLER_RESOLUTION: 14
HEATMAP_SIZE: 56
INDEX_WEIGHTS: 2.0
PART_WEIGHTS: 0.3
POINT_REGRESSION_WEIGHTS: 0.1
DECODER_ON: False
SOLVER:
BASE_LR: 0.002
MAX_ITER: 130000
STEPS: (100000, 120000)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_WC1_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
UV_CONFIDENCE:
ENABLED: True
TYPE: "iid_iso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_WC2_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
UV_CONFIDENCE:
ENABLED: True
TYPE: "indep_aniso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)

10
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_DL_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
SOLVER:
MAX_ITER: 130000
STEPS: (100000, 120000)

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC1_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "iid_iso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)
WARMUP_FACTOR: 0.025

16
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_WC2_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "indep_aniso"
POINT_REGRESSION_WEIGHTS: 0.0005
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 130000
STEPS: (100000, 120000)
WARMUP_FACTOR: 0.025

8
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
SOLVER:
MAX_ITER: 130000
STEPS: (100000, 120000)

17
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x_legacy.yaml Normal file
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_BASE_: "Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
NUM_COARSE_SEGM_CHANNELS: 15
POOLER_RESOLUTION: 14
HEATMAP_SIZE: 56
INDEX_WEIGHTS: 2.0
PART_WEIGHTS: 0.3
POINT_REGRESSION_WEIGHTS: 0.1
DECODER_ON: False
SOLVER:
BASE_LR: 0.002
MAX_ITER: 130000
STEPS: (100000, 120000)

91
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/evolution/Base-RCNN-FPN-MC.yaml Normal file
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MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
# Detectron1 uses 2000 proposals per-batch,
# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "StandardROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
NUM_CLASSES: 1
ROI_BOX_HEAD:
NAME: "FastRCNNConvFCHead"
NUM_FC: 2
POOLER_RESOLUTION: 7
ROI_MASK_HEAD:
NAME: "MaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
DATASETS:
TRAIN: ("base_coco_2017_train",)
TEST: ("base_coco_2017_val", "densepose_chimps")
CATEGORY_MAPS:
"base_coco_2017_train":
"16": 1 # bird -> person
"17": 1 # cat -> person
"18": 1 # dog -> person
"19": 1 # horse -> person
"20": 1 # sheep -> person
"21": 1 # cow -> person
"22": 1 # elephant -> person
"23": 1 # bear -> person
"24": 1 # zebra -> person
"25": 1 # girafe -> person
"base_coco_2017_val":
"16": 1 # bird -> person
"17": 1 # cat -> person
"18": 1 # dog -> person
"19": 1 # horse -> person
"20": 1 # sheep -> person
"21": 1 # cow -> person
"22": 1 # elephant -> person
"23": 1 # bear -> person
"24": 1 # zebra -> person
"25": 1 # girafe -> person
WHITELISTED_CATEGORIES:
"base_coco_2017_train":
- 1 # person
- 16 # bird
- 17 # cat
- 18 # dog
- 19 # horse
- 20 # sheep
- 21 # cow
- 22 # elephant
- 23 # bear
- 24 # zebra
- 25 # girafe
"base_coco_2017_val":
- 1 # person
- 16 # bird
- 17 # cat
- 18 # dog
- 19 # horse
- 20 # sheep
- 21 # cow
- 22 # elephant
- 23 # bear
- 24 # zebra
- 25 # girafe
SOLVER:
IMS_PER_BATCH: 16
BASE_LR: 0.02
STEPS: (60000, 80000)
MAX_ITER: 90000
INPUT:
MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
VERSION: 2

7
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/evolution/faster_rcnn_R_50_FPN_1x_MC.yaml Normal file
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_BASE_: "Base-RCNN-FPN-MC.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
MASK_ON: False
DENSEPOSE_ON: False
RESNETS:
DEPTH: 50

11
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_DL_instant_test.yaml Normal file
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_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
ROI_DENSEPOSE_HEAD:
NAME: "DensePoseDeepLabHead"
DATASETS:
TRAIN: ("densepose_coco_2014_minival_100",)
TEST: ("densepose_coco_2014_minival_100",)
SOLVER:
MAX_ITER: 40
STEPS: (30,)

13
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_TTA_inference_acc_test.yaml Normal file
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_BASE_: "../densepose_rcnn_R_50_FPN_s1x.yaml"
MODEL:
WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl"
DATASETS:
TRAIN: ()
TEST: ("densepose_coco_2014_minival_100",)
TEST:
AUG:
ENABLED: True
MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
MAX_SIZE: 4000
FLIP: True
EXPECTED_RESULTS: [["bbox_TTA", "AP", 61.74, 0.03], ["densepose_gps_TTA", "AP", 60.22, 0.03], ["densepose_gpsm_TTA", "AP", 63.85, 0.03]]

19
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_WC1_instant_test.yaml Normal file
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_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "iid_iso"
POINT_REGRESSION_WEIGHTS: 0.0005
DATASETS:
TRAIN: ("densepose_coco_2014_minival_100",)
TEST: ("densepose_coco_2014_minival_100",)
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 40
STEPS: (30,)
WARMUP_FACTOR: 0.025

19
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_WC2_instant_test.yaml Normal file
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_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
RESNETS:
DEPTH: 50
ROI_DENSEPOSE_HEAD:
UV_CONFIDENCE:
ENABLED: True
TYPE: "indep_aniso"
POINT_REGRESSION_WEIGHTS: 0.0005
DATASETS:
TRAIN: ("densepose_coco_2014_minival_100",)
TEST: ("densepose_coco_2014_minival_100",)
SOLVER:
CLIP_GRADIENTS:
ENABLED: True
MAX_ITER: 40
STEPS: (30,)
WARMUP_FACTOR: 0.025

8
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_inference_acc_test.yaml Normal file
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_BASE_: "../densepose_rcnn_R_50_FPN_s1x.yaml"
MODEL:
WEIGHTS: "https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl"
DATASETS:
TRAIN: ()
TEST: ("densepose_coco_2014_minival_100",)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 59.27, 0.025], ["densepose_gps", "AP", 60.11, 0.02], ["densepose_gpsm", "AP", 64.20, 0.02]]

9
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_instant_test.yaml Normal file
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_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
DATASETS:
TRAIN: ("densepose_coco_2014_minival_100",)
TEST: ("densepose_coco_2014_minival_100",)
SOLVER:
MAX_ITER: 40
STEPS: (30,)

14
vton-api/preprocess/humanparsing/mhp_extension/detectron2/projects/DensePose/configs/quick_schedules/densepose_rcnn_R_50_FPN_training_acc_test.yaml Normal file
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_BASE_: "../Base-DensePose-RCNN-FPN.yaml"
MODEL:
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
ROI_HEADS:
NUM_CLASSES: 1
DATASETS:
TRAIN: ("densepose_coco_2014_minival",)
TEST: ("densepose_coco_2014_minival",)
SOLVER:
MAX_ITER: 6000
STEPS: (5500, 5800)
TEST:
EXPECTED_RESULTS: [["bbox", "AP", 58.27, 1.0], ["densepose_gps", "AP", 42.47, 1.5], ["densepose_gpsm", "AP", 49.20, 1.5]]