def add_ResNet_roi_conv5_head_for_keypoints(
model, blob_in, dim_in, spatial_scale
):
"""Add a ResNet "conv5" / "stage5" head for Mask R-CNN keypoint prediction.
"""
model.RoIFeatureTransform(
blob_in,
'_[pose]_pool5',
blob_rois='keypoint_rois',
method=cfg.KRCNN.ROI_XFORM_METHOD,
resolution=cfg.KRCNN.ROI_XFORM_RESOLUTION,
sampling_ratio=cfg.KRCNN.ROI_XFORM_SAMPLING_RATIO,
spatial_scale=spatial_scale
)
# Using the prefix '_[pose]_' to 'res5' enables initializing the head's
# parameters using pretrained 'res5' parameters if given (see
# utils.net.initialize_from_weights_file)
s, dim_in = ResNet.add_stage(
model,
'_[pose]_res5',
'_[pose]_pool5',
3,
dim_in,
2048,
512,
cfg.KRCNN.DILATION,
stride_init=int(cfg.KRCNN.ROI_XFORM_RESOLUTION / 7)
)
return s, 2048
def add_ResNet_roi_conv5_head_for_bodyUV(model, blob_in, dim_in,
spatial_scale):
"""Add a ResNet "conv5" / "stage5" head for body UV prediction."""
model.RoIFeatureTransform(
blob_in,
'_[body_uv]_pool5',
blob_rois='body_uv_rois',
method=cfg.BODY_UV_RCNN.ROI_XFORM_METHOD,
resolution=cfg.BODY_UV_RCNN.ROI_XFORM_RESOLUTION,
sampling_ratio=cfg.BODY_UV_RCNN.ROI_XFORM_SAMPLING_RATIO,
spatial_scale=spatial_scale)
# Using the prefix '_[body_uv]_' to 'res5' enables initializing the head's
# parameters using pretrained 'res5' parameters if given (see
# utils.net.initialize_from_weights_file)
s, dim_in = ResNet.add_stage(
model,
'_[body_uv]_res5',
'_[body_uv]_pool5',
3,
dim_in,
2048,
512,
cfg.BODY_UV_RCNN.DILATION,
stride_init=int(cfg.BODY_UV_RCNN.ROI_XFORM_RESOLUTION / 7))
return s, 2048
def add_ResNet_roi_conv5_head_for_masks(model, blob_in, dim_in, spatial_scale):
"""Add a ResNet "conv5" / "stage5" head for predicting masks."""
model.RoIFeatureTransform(
blob_in,
blob_out='_[mask]_pool5',
blob_rois='mask_rois',
method=cfg.MRCNN.ROI_XFORM_METHOD,
resolution=cfg.MRCNN.ROI_XFORM_RESOLUTION,
sampling_ratio=cfg.MRCNN.ROI_XFORM_SAMPLING_RATIO,
spatial_scale=spatial_scale)
dilation = cfg.MRCNN.DILATION
stride_init = int(cfg.MRCNN.ROI_XFORM_RESOLUTION / 7) # by default: 2
s, dim_in = ResNet.add_stage(model,
'_[mask]_res5',
'_[mask]_pool5',
3,
dim_in,
2048,
512,
dilation,
stride_init=stride_init)
return s, 2048
def add_ResNet_roi_conv5_head_for_masks(model, blob_in, dim_in, spatial_scale):
"""Add a ResNet "conv5" / "stage5" head for predicting masks."""
model.RoIFeatureTransform(
blob_in,
blob_out='_[mask]_pool5',
blob_rois='mask_rois',
method=cfg.MRCNN.ROI_XFORM_METHOD,
resolution=cfg.MRCNN.ROI_XFORM_RESOLUTION,
sampling_ratio=cfg.MRCNN.ROI_XFORM_SAMPLING_RATIO,
spatial_scale=spatial_scale
)
dilation = cfg.MRCNN.DILATION
stride_init = int(cfg.MRCNN.ROI_XFORM_RESOLUTION / 7) # by default: 2
s, dim_in = ResNet.add_stage(
model,
'_[mask]_res5',
'_[mask]_pool5',
3,
dim_in,
2048,
512,
dilation,
stride_init=stride_init
)
return s, 2048