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DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml
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DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml
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MODEL:
TYPE: generalized_rcnn
CONV_BODY: FPN.add_fpn_ResNet50_conv5_body
NUM_CLASSES: 2
FASTER_RCNN: True
BODY_UV_ON: True
MASK_ON: True
KEYPOINTS_ON: True
NUM_GPUS: 8
SOLVER:
WEIGHT_DECAY: 0.0001
LR_POLICY: steps_with_decay
GAMMA: 0.1
WARM_UP_ITERS: 3000
WARM_UP_FACTOR: 0.0000001
# Linear scaling rule:
# 1 GPU:
# BASE_LR: 0.00025
# MAX_ITER: 720000
# STEPS: [0, 480000, 640000]
# 2 GPUs:
# BASE_LR: 0.0005
# MAX_ITER: 360000
# STEPS: [0, 240000, 320000]
# 4 GPUs:
# BASE_LR: 0.001
# MAX_ITER: 180000
# STEPS: [0, 120000, 160000]
#8 GPUs:
BASE_LR: 0.002
MAX_ITER: 230000
STEPS: [0, 150000, 200000]
FPN:
FPN_ON: True
MULTILEVEL_ROIS: True
MULTILEVEL_RPN: True
FAST_RCNN:
ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14
ROI_XFORM_SAMPLING_RATIO: 2
BODY_UV_RCNN:
ROI_HEAD: body_uv_rcnn_heads.add_roi_body_uv_head_v1convX
NUM_STACKED_CONVS: 8
NUM_PATCHES: 24
USE_DECONV_OUTPUT: True
CONV_INIT: MSRAFill
CONV_HEAD_DIM: 512
UP_SCALE: 2
HEATMAP_SIZE: 56
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14
ROI_XFORM_SAMPLING_RATIO: 2
##
# Loss weights for annotation masks.(14 Parts)
INDEX_WEIGHTS : 2.0
# Loss weights for surface parts. (24 Parts)
PART_WEIGHTS : 0.3
# Loss weights for UV regression.
POINT_REGRESSION_WEIGHTS : 0.1
##
BODY_UV_IMS: True
MRCNN:
ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
RESOLUTION: 28
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14
ROI_XFORM_SAMPLING_RATIO: 2 # default 0
DILATION: 1 # default 2
CONV_INIT: MSRAFill # default: GaussianFill
KRCNN:
ROI_KEYPOINTS_HEAD: keypoint_rcnn_heads.add_roi_pose_head_v1convX
NUM_STACKED_CONVS: 8
NUM_KEYPOINTS: 17
USE_DECONV_OUTPUT: True
CONV_INIT: MSRAFill
CONV_HEAD_DIM: 512
UP_SCALE: 2
HEATMAP_SIZE: 56 # ROI_XFORM_RESOLUTION (14) * UP_SCALE (2) * USE_DECONV_OUTPUT (2)
ROI_XFORM_METHOD: RoIAlign
ROI_XFORM_RESOLUTION: 14
ROI_XFORM_SAMPLING_RATIO: 2
KEYPOINT_CONFIDENCE: bbox
TRAIN:
WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
SCALES: (640, 672, 704, 736, 768, 800)
MAX_SIZE: 1333
IMS_PER_BATCH: 1
BATCH_SIZE_PER_IM: 512
USE_FLIPPED: True
RPN_PRE_NMS_TOP_N: 2000 # Per FPN level
TEST:
DATASETS: ('dense_coco_2014_minival',)
PROPOSAL_LIMIT: 1000
SCALE: 800
MAX_SIZE: 1333
NMS: 0.5
FORCE_JSON_DATASET_EVAL: True
DETECTIONS_PER_IM: 20
RPN_PRE_NMS_TOP_N: 1000 # Per FPN level
RPN_POST_NMS_TOP_N: 1000
OUTPUT_DIR: ''