Python detectron.utils.c2.const_fill() Examples

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def add_cluster_rcnn_outputs(model, blob_in, dim):
    """Add Cluster RoI classification and bounding box regression output ops."""
    # cluster Box classification layer
    model.FC(
        blob_in,
        'cluster_cls_score',
        dim,
        2,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cluster_cls_score', 'cluster_cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = 2
    model.FC(
        blob_in,
        'cluster_bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    ) 

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def mask_rcnn_fcn_head_v0up(model, blob_in, dim_in, spatial_scale):
    """v0up design: conv5, deconv 2x2 (no weight sharing with the box head)."""
    blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
        model,
        blob_in,
        dim_in,
        spatial_scale
    )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=('GaussianFill', {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def add_cascade_rcnn_outputs(model, blob_in, dim, stage):
    """Add RoI classification and bounding box regression output ops."""
    stage_name = "_{}".format(stage)
    model.FC(
        blob_in,
        "cls_score" + stage_name,
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0),
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax("cls_score" + stage_name, "cls_prob" + stage_name, engine="CUDNN")

    num_bbox_reg_classes = 2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    model.FC(
        blob_in,
        "bbox_pred" + stage_name,
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0),
    )
    # add stage parameters to list
    if str(stage) not in model.stage_params:
        model.stage_params[str(stage)] = []
    for idx in range(-2, 0):
        model.stage_params[str(stage)].append(model.weights[idx])
        model.stage_params[str(stage)].append(model.biases[idx])
    return "cls_prob" + stage_name, "bbox_pred" + stage_name 

def add_topdown_lateral_module(
    model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral
):
    """Add a top-down lateral module."""
    # Lateral 1x1 conv
    if cfg.FPN.USE_GN:
        # use GroupNorm
        lat = model.ConvGN(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            group_gn=get_group_gn(dim_top),
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL
                else ('XavierFill', {})),
            bias_init=const_fill(0.0)
        )
    else:
        lat = model.Conv(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0)
                if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {})
            ),
            bias_init=const_fill(0.0)
        )
    # Top-down 2x upsampling
    td = model.net.UpsampleNearest(fpn_top, fpn_bottom + '_topdown', scale=2)
    # Sum lateral and top-down
    model.net.Sum([lat, td], fpn_bottom) 

def add_cascade_rcnn_outputs(model, blob_in, dim, stage):
    """Add RoI classification and bounding box regression output ops."""
    stage_name = "_{}".format(stage)
    model.FC(
        blob_in,
        "cls_score" + stage_name,
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0),
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax("cls_score" + stage_name, "cls_prob" + stage_name, engine="CUDNN")

    num_bbox_reg_classes = 2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    model.FC(
        blob_in,
        "bbox_pred" + stage_name,
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0),
    )
    # add stage parameters to list
    if str(stage) not in model.stage_params:
        model.stage_params[str(stage)] = []
    for idx in range(-2, 0):
        model.stage_params[str(stage)].append(model.weights[idx])
        model.stage_params[str(stage)].append(model.biases[idx])
    return "cls_prob" + stage_name, "bbox_pred" + stage_name 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    )
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    )

    if cfg.MODEL.CASCADE_ON:
        # add stage parameters to list
        if '1' not in model.stage_params:
            model.stage_params['1'] = []
        for idx in range(-2, 0):
            model.stage_params['1'].append(model.weights[idx])
            model.stage_params['1'].append(model.biases[idx]) 

def mask_rcnn_fcn_head_v0upshare(model, blob_in, dim_in, spatial_scale):
    """Use a ResNet "conv5" / "stage5" head for mask prediction. Weights and
    computation are shared with the conv5 box head. Computation can only be
    shared during training, since inference is cascaded.

    v0upshare design: conv5, convT 2x2.
    """
    # Since box and mask head are shared, these must match
    assert cfg.MRCNN.ROI_XFORM_RESOLUTION == cfg.FAST_RCNN.ROI_XFORM_RESOLUTION

    if model.train:  # share computation with bbox head at training time
        dim_conv5 = 2048
        blob_conv5 = model.net.SampleAs(
            ['res5_2_sum', 'roi_has_mask_int32'],
            ['_[mask]_res5_2_sum_sliced']
        )
    else:  # re-compute at test time
        blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
            model,
            blob_in,
            dim_in,
            spatial_scale
        )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    blob_mask = model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),  # std only for gauss
        bias_init=const_fill(0.0)
    )
    model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def _add_image_level_classifier(model, blob_in, dim_in, spatial_scale_in):
    from detectron.utils.c2 import const_fill
    from detectron.utils.c2 import gauss_fill
    
    def negateGrad(inputs, outputs):
        outputs[0].feed(inputs[0].data)
    def grad_negateGrad(inputs, outputs):
        scale = cfg.TRAIN.DA_IMG_GRL_WEIGHT
        grad_output = inputs[-1]
        outputs[0].reshape(grad_output.shape)
        outputs[0].data[...] = -1.0*scale*grad_output.data
    
    model.GradientScalerLayer([blob_in], ['da_grl'], -1.0*cfg.TRAIN.DA_IMG_GRL_WEIGHT)
    model.Conv('da_grl', 'da_conv_1', dim_in, 512, kernel=1, pad=0, stride=1, weight_init=gauss_fill(0.001), bias_init=const_fill(0.0))    
    model.Relu('da_conv_1', 'da_conv_1')
    model.Conv('da_conv_1', 'da_conv_2',
        512,
        1,
        kernel=1,
        pad=0,
        stride=1,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    )
    if model.train:
        model.net.SpatialNarrowAs(
            ['da_label_wide', 'da_conv_2'], 'da_label'
        )
        loss_da = model.net.SigmoidCrossEntropyLoss(
            ['da_conv_2', 'da_label'],
            'loss_da',
            scale=model.GetLossScale()
        )
        loss_gradient = blob_utils.get_loss_gradients(model, [loss_da])
        model.AddLosses('loss_da')
        return loss_gradient
    else:
        return None 

def add_topdown_lateral_module(
    model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral
):
    """Add a top-down lateral module."""
    # Lateral 1x1 conv
    if cfg.FPN.USE_GN:
        # use GroupNorm
        lat = model.ConvGN(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            group_gn=get_group_gn(dim_top),
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL
                else ('XavierFill', {})),
            bias_init=const_fill(0.0)
        )
    else:
        lat = model.Conv(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0)
                if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {})
            ),
            bias_init=const_fill(0.0)
        )
    # Top-down 2x upsampling
    td = model.net.UpsampleNearest(fpn_top, fpn_bottom + '_topdown', scale=2)
    # Sum lateral and top-down
    model.net.Sum([lat, td], fpn_bottom) 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    )
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    ) 

def add_topdown_lateral_module(
    model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral
):
    """Add a top-down lateral module."""
    # Lateral 1x1 conv
    if cfg.FPN.USE_GN:
        # use GroupNorm
        lat = model.ConvGN(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            group_gn=get_group_gn(dim_top),
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL
                else ('XavierFill', {})),
            bias_init=const_fill(0.0)
        )
    else:
        lat = model.Conv(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0)
                if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {})
            ),
            bias_init=const_fill(0.0)
        )
    # Top-down 2x upsampling
    td = model.net.UpsampleNearest(fpn_top, fpn_bottom + '_topdown', scale=2)
    # Sum lateral and top-down
    model.net.Sum([lat, td], fpn_bottom) 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    )
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    ) 

def mask_rcnn_fcn_head_v0upshare(model, blob_in, dim_in, spatial_scale):
    """Use a ResNet "conv5" / "stage5" head for mask prediction. Weights and
    computation are shared with the conv5 box head. Computation can only be
    shared during training, since inference is cascaded.

    v0upshare design: conv5, convT 2x2.
    """
    # Since box and mask head are shared, these must match
    assert cfg.MRCNN.ROI_XFORM_RESOLUTION == cfg.FAST_RCNN.ROI_XFORM_RESOLUTION

    if model.train:  # share computation with bbox head at training time
        dim_conv5 = 2048
        blob_conv5 = model.net.SampleAs(
            ['res5_2_sum', 'roi_has_mask_int32'],
            ['_[mask]_res5_2_sum_sliced']
        )
    else:  # re-compute at test time
        blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
            model,
            blob_in,
            dim_in,
            spatial_scale
        )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    blob_mask = model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),  # std only for gauss
        bias_init=const_fill(0.0)
    )
    model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def mask_rcnn_fcn_head_v0upshare(model, blob_in, dim_in, spatial_scale):
    """Use a ResNet "conv5" / "stage5" head for mask prediction. Weights and
    computation are shared with the conv5 box head. Computation can only be
    shared during training, since inference is cascaded.

    v0upshare design: conv5, convT 2x2.
    """
    # Since box and mask head are shared, these must match
    assert cfg.MRCNN.ROI_XFORM_RESOLUTION == cfg.FAST_RCNN.ROI_XFORM_RESOLUTION

    if model.train:  # share computation with bbox head at training time
        dim_conv5 = 2048
        blob_conv5 = model.net.SampleAs(
            ['res5_2_sum', 'roi_has_mask_int32'],
            ['_[mask]_res5_2_sum_sliced']
        )
    else:  # re-compute at test time
        blob_conv5, dim_conv5 = add_ResNet_roi_conv5_head_for_masks(
            model,
            blob_in,
            dim_in,
            spatial_scale
        )

    dim_reduced = cfg.MRCNN.DIM_REDUCED

    blob_mask = model.ConvTranspose(
        blob_conv5,
        'conv5_mask',
        dim_conv5,
        dim_reduced,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),  # std only for gauss
        bias_init=const_fill(0.0)
    )
    model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_reduced 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        cfg.MODEL.NUM_CLASSES if not cfg.MODEL.Cluster_RCNN_ON else cfg.MODEL.NUM_CLASSES-1,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG  else cfg.MODEL.NUM_CLASSES
        if not cfg.MODEL.Cluster_RCNN_ON else cfg.MODEL.NUM_CLASSES-1
    )
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    )

    if cfg.MODEL.CASCADE_ON:
        # add stage parameters to list
        if '1' not in model.stage_params:
            model.stage_params['1'] = []
        for idx in range(-2, 0):
            model.stage_params['1'].append(model.weights[idx])
            model.stage_params['1'].append(model.biases[idx]) 

def add_topdown_lateral_module(
    model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral
):
    """Add a top-down lateral module."""
    # Lateral 1x1 conv
    if cfg.FPN.USE_GN:
        # use GroupNorm
        lat = model.ConvGN(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            group_gn=get_group_gn(dim_top),
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL
                else ('XavierFill', {})),
            bias_init=const_fill(0.0)
        )
    else:
        lat = model.Conv(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0)
                if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {})
            ),
            bias_init=const_fill(0.0)
        )
    # Top-down 2x upsampling
    td = model.net.UpsampleNearest(fpn_top, fpn_bottom + '_topdown', scale=2)
    # Sum lateral and top-down
    model.net.Sum([lat, td], fpn_bottom) 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    )
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    )

    if cfg.MODEL.CASCADE_ON:
        # add stage parameters to list
        if '1' not in model.stage_params:
            model.stage_params['1'] = []
        for idx in range(-2, 0):
            model.stage_params['1'].append(model.weights[idx])
            model.stage_params['1'].append(model.biases[idx]) 

def add_topdown_lateral_module(
    model, fpn_top, fpn_lateral, fpn_bottom, dim_top, dim_lateral
):
    """Add a top-down lateral module."""
    # Lateral 1x1 conv
    if cfg.FPN.USE_GN:
        # use GroupNorm
        lat = model.ConvGN(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            group_gn=get_group_gn(dim_top),
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0) if cfg.FPN.ZERO_INIT_LATERAL
                else ('XavierFill', {})),
            bias_init=const_fill(0.0)
        )
    else:
        lat = model.Conv(
            fpn_lateral,
            fpn_bottom + '_lateral',
            dim_in=dim_lateral,
            dim_out=dim_top,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(
                const_fill(0.0)
                if cfg.FPN.ZERO_INIT_LATERAL else ('XavierFill', {})
            ),
            bias_init=const_fill(0.0)
        )
    # Top-down 2x upsampling
    td = model.net.UpsampleNearest(fpn_top, fpn_bottom + '_topdown', scale=2)
    # Sum lateral and top-down
    model.net.Sum([lat, td], fpn_bottom) 

def add_cascade_rcnn_outputs(model, blob_in, dim, stage):
    """Add RoI classification and bounding box regression output ops."""
    stage_name = "_{}".format(stage)
    model.FC(
        blob_in,
        "cls_score" + stage_name,
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0),
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax("cls_score" + stage_name, "cls_prob" + stage_name, engine="CUDNN")

    num_bbox_reg_classes = 2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    model.FC(
        blob_in,
        "bbox_pred" + stage_name,
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0),
    )
    # add stage parameters to list
    if str(stage) not in model.stage_params:
        model.stage_params[str(stage)] = []
    for idx in range(-2, 0):
        model.stage_params[str(stage)].append(model.weights[idx])
        model.stage_params[str(stage)].append(model.biases[idx])
    return "cls_prob" + stage_name, "bbox_pred" + stage_name 

def add_mask_rcnn_outputs(model, blob_in, dim):
    """Add Mask R-CNN specific outputs: either mask logits or probs."""
    num_cls = cfg.MODEL.NUM_CLASSES if cfg.MRCNN.CLS_SPECIFIC_MASK else 1

    if cfg.MRCNN.USE_FC_OUTPUT:
        # Predict masks with a fully connected layer (ignore 'fcn' in the blob
        # name)
        dim_fc = int(dim * (cfg.MRCNN.RESOLUTION / cfg.MRCNN.UPSAMPLE_RATIO)**2)
        blob_out = model.FC(
            blob_in,
            'mask_fcn_logits',
            dim_fc,
            num_cls * cfg.MRCNN.RESOLUTION**2,
            weight_init=gauss_fill(0.001),
            bias_init=const_fill(0.0)
        )
    else:
        # Predict mask using Conv

        # Use GaussianFill for class-agnostic mask prediction; fills based on
        # fan-in can be too large in this case and cause divergence
        fill = (
            cfg.MRCNN.CONV_INIT
            if cfg.MRCNN.CLS_SPECIFIC_MASK else 'GaussianFill'
        )
        blob_out = model.Conv(
            blob_in,
            'mask_fcn_logits',
            dim,
            num_cls,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(fill, {'std': 0.001}),
            bias_init=const_fill(0.0)
        )

        if cfg.MRCNN.UPSAMPLE_RATIO > 1:
            blob_out = model.BilinearInterpolation(
                'mask_fcn_logits', 'mask_fcn_logits_up', num_cls, num_cls,
                cfg.MRCNN.UPSAMPLE_RATIO
            )

    if not model.train:  # == if test
        blob_out = model.net.Sigmoid(blob_out, 'mask_fcn_probs')

    return blob_out 

def add_mask_rcnn_outputs(model, blob_in, dim):
    """Add Mask R-CNN specific outputs: either mask logits or probs."""
    num_cls = cfg.MODEL.NUM_CLASSES if cfg.MRCNN.CLS_SPECIFIC_MASK else 1

    if cfg.MRCNN.USE_FC_OUTPUT:
        # Predict masks with a fully connected layer (ignore 'fcn' in the blob
        # name)
        dim_fc = int(dim * (cfg.MRCNN.RESOLUTION / cfg.MRCNN.UPSAMPLE_RATIO)**2)
        blob_out = model.FC(
            blob_in,
            'mask_fcn_logits',
            dim_fc,
            num_cls * cfg.MRCNN.RESOLUTION**2,
            weight_init=gauss_fill(0.001),
            bias_init=const_fill(0.0)
        )
    else:
        # Predict mask using Conv

        # Use GaussianFill for class-agnostic mask prediction; fills based on
        # fan-in can be too large in this case and cause divergence
        fill = (
            cfg.MRCNN.CONV_INIT
            if cfg.MRCNN.CLS_SPECIFIC_MASK else 'GaussianFill'
        )
        blob_out = model.Conv(
            blob_in,
            'mask_fcn_logits',
            dim,
            num_cls,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(fill, {'std': 0.001}),
            bias_init=const_fill(0.0)
        )

        if cfg.MRCNN.UPSAMPLE_RATIO > 1:
            blob_out = model.BilinearInterpolation(
                'mask_fcn_logits', 'mask_fcn_logits_up', num_cls, num_cls,
                cfg.MRCNN.UPSAMPLE_RATIO
            )

    if not model.train:  # == if test
        blob_out = model.net.Sigmoid(blob_out, 'mask_fcn_probs')

    return blob_out 

def mask_rcnn_fcn_head_v1upXconvs_gn(
    model, blob_in, dim_in, spatial_scale, num_convs
):
    """v1upXconvs design: X * (conv 3x3), convT 2x2, with GroupNorm"""
    current = model.RoIFeatureTransform(
        blob_in,
        blob_out='_mask_roi_feat',
        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
    dim_inner = cfg.MRCNN.DIM_REDUCED

    for i in range(num_convs):
        current = model.ConvGN(
            current,
            '_mask_fcn' + str(i + 1),
            dim_in,
            dim_inner,
            group_gn=get_group_gn(dim_inner),
            kernel=3,
            pad=1 * dilation,
            stride=1,
            weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),
            bias_init=('ConstantFill', {'value': 0.})
        )
        current = model.Relu(current, current)
        dim_in = dim_inner

    # upsample layer
    model.ConvTranspose(
        current,
        'conv5_mask',
        dim_inner,
        dim_inner,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_inner 

def mask_rcnn_fcn_head_v1upXconvs(
    model, blob_in, dim_in, spatial_scale, num_convs
):
    """v1upXconvs design: X * (conv 3x3), convT 2x2."""
    current = model.RoIFeatureTransform(
        blob_in,
        blob_out='_[mask]_roi_feat',
        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
    dim_inner = cfg.MRCNN.DIM_REDUCED

    for i in range(num_convs):
        current = model.Conv(
            current,
            '_[mask]_fcn' + str(i + 1),
            dim_in,
            dim_inner,
            kernel=3,
            dilation=dilation,
            pad=1 * dilation,
            stride=1,
            weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),
            bias_init=('ConstantFill', {'value': 0.})
        )
        current = model.Relu(current, current)
        dim_in = dim_inner

    # upsample layer
    model.ConvTranspose(
        current,
        'conv5_mask',
        dim_inner,
        dim_inner,
        kernel=2,
        pad=0,
        stride=2,
        weight_init=(cfg.MRCNN.CONV_INIT, {'std': 0.001}),
        bias_init=const_fill(0.0)
    )
    blob_mask = model.Relu('conv5_mask', 'conv5_mask')

    return blob_mask, dim_inner 

def add_mask_rcnn_outputs(model, blob_in, dim):
    """Add Mask R-CNN specific outputs: either mask logits or probs."""
    num_cls = cfg.MODEL.NUM_CLASSES if cfg.MRCNN.CLS_SPECIFIC_MASK else 1

    if cfg.MRCNN.USE_FC_OUTPUT:
        # Predict masks with a fully connected layer (ignore 'fcn' in the blob
        # name)
        dim_fc = int(dim * (cfg.MRCNN.RESOLUTION / cfg.MRCNN.UPSAMPLE_RATIO)**2)
        blob_out = model.FC(
            blob_in,
            'mask_fcn_logits',
            dim_fc,
            num_cls * cfg.MRCNN.RESOLUTION**2,
            weight_init=gauss_fill(0.001),
            bias_init=const_fill(0.0)
        )
    else:
        # Predict mask using Conv

        # Use GaussianFill for class-agnostic mask prediction; fills based on
        # fan-in can be too large in this case and cause divergence
        fill = (
            cfg.MRCNN.CONV_INIT
            if cfg.MRCNN.CLS_SPECIFIC_MASK else 'GaussianFill'
        )
        blob_out = model.Conv(
            blob_in,
            'mask_fcn_logits',
            dim,
            num_cls,
            kernel=1,
            pad=0,
            stride=1,
            weight_init=(fill, {'std': 0.001}),
            bias_init=const_fill(0.0)
        )

        if cfg.MRCNN.UPSAMPLE_RATIO > 1:
            blob_out = model.BilinearInterpolation(
                'mask_fcn_logits', 'mask_fcn_logits_up', num_cls, num_cls,
                cfg.MRCNN.UPSAMPLE_RATIO
            )

    if not model.train:  # == if test
        blob_out = model.net.Sigmoid(blob_out, 'mask_fcn_probs')

    return blob_out 

def add_fast_rcnn_outputs(model, blob_in, dim):
    """Add RoI classification and bounding box regression output ops."""
    # Box classification layer
    model.FC(
        blob_in,
        'cls_score',
        dim,
        model.num_classes,
        weight_init=gauss_fill(0.01),
        bias_init=const_fill(0.0)
    )
    if not model.train:  # == if test
        # Only add softmax when testing; during training the softmax is combined
        # with the label cross entropy loss for numerical stability
        model.Softmax('cls_score', 'cls_prob', engine='CUDNN')
    # Box regression layer
    num_bbox_reg_classes = (
        2 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else model.num_classes
    )
    
    model.FC(
        blob_in,
        'bbox_pred',
        dim,
        num_bbox_reg_classes * 4,
        weight_init=gauss_fill(0.001),
        bias_init=const_fill(0.0)
    )
    if cfg.PRED_STD:
        if cfg.PRED_STD_LOG:
            bias = 0.
            model.FC(
                blob_in, #'blob_in0'
                'bbox_pred_std',
                dim,
                num_bbox_reg_classes * 4,
                weight_init=gauss_fill(0.0001),
                bias_init=const_fill(bias)
            )
            model.net.Copy('bbox_pred_std', 'bbox_pred_std_abs')
            #model.Relu('bbox_pred_std', 'bbox_pred_std_abs')
            #model.net.Sigmoid('bbox_pred_std', 'bbox_pred_std_abs')