Python ResNet.add_stage Beispiele

Beispiel #1

Datei: mask_rcnn_heads.py Projekt: NTT-Gasyo-Intern/Inception_Augmented_FPN

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

Beispiel #2

Datei: keypoint_rcnn_heads.py Projekt: Jakaria08/Detectron

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

Beispiel #3

Datei: shape_points_rcnn_heads.py Projekt: keeploading/Detectron

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='shape_points_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

Beispiel #4

Datei: mask_rcnn_heads.py Projekt: Jakaria08/Detectron

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