def add_VGG16_roi_fc_head_rel_spo_late_fusion()

in lib/modeling/VGG16_rel_softmaxed_triplet.py [0:0]

• 73 lines of code
• 4 McCabe index (conditional complexity)

def add_VGG16_roi_fc_head_rel_spo_late_fusion(
        model, blob_in, dim_in, spatial_scale):

    model.RoIFeatureTransform(
        blob_in, 'rel_roi_pool_sbj',
        blob_rois='rel_rois_sbj',
        method=cfg.FAST_RCNN.ROI_XFORM_METHOD,
        resolution=7,
        sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO,
        spatial_scale=spatial_scale)
    model.RoIFeatureTransform(
        blob_in, 'rel_roi_pool_obj',
        blob_rois='rel_rois_obj',
        method=cfg.FAST_RCNN.ROI_XFORM_METHOD,
        resolution=7,
        sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO,
        spatial_scale=spatial_scale)
    model.RoIFeatureTransform(
        blob_in, 'rel_roi_pool_prd',
        blob_rois='rel_rois_prd',
        method=cfg.FAST_RCNN.ROI_XFORM_METHOD,
        resolution=7,
        sampling_ratio=cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO,
        spatial_scale=spatial_scale)

    model.add_FC_layer_with_weight_name(
        'fc6', 'rel_roi_pool_sbj', 'rel_sbj_fc6', dim_in * 7 * 7, 4096)
    model.Relu('rel_sbj_fc6', 'rel_sbj_fc6')
    model.Dropout(
        'rel_sbj_fc6', 'rel_sbj_fc6_dropout',
        ratio=dropout_ratio, is_test=(not model.train))
    model.add_FC_layer_with_weight_name(
        'fc7', 'rel_sbj_fc6_dropout', 'rel_sbj_fc7', 4096, 4096)
    model.Relu('rel_sbj_fc7', 'rel_sbj_fc7')
    s_sbj = model.Dropout(
        'rel_sbj_fc7', 'rel_sbj_fc7_dropout',
        ratio=dropout_ratio, is_test=(not model.train))
    if cfg.MODEL.SPECS.find('not_stop_gradient') < 0:
        model.StopGradient(s_sbj, s_sbj)  # this is to stop the gradient from p branch

    model.add_FC_layer_with_weight_name(
        'fc6', 'rel_roi_pool_obj', 'rel_obj_fc6', dim_in * 7 * 7, 4096)
    model.Relu('rel_obj_fc6', 'rel_obj_fc6')
    model.Dropout(
        'rel_obj_fc6', 'rel_obj_fc6_dropout',
        ratio=dropout_ratio, is_test=(not model.train))
    model.add_FC_layer_with_weight_name(
        'fc7', 'rel_obj_fc6_dropout', 'rel_obj_fc7', 4096, 4096)
    model.Relu('rel_obj_fc7', 'rel_obj_fc7')
    s_obj = model.Dropout(
        'rel_obj_fc7', 'rel_obj_fc7_dropout',
        ratio=dropout_ratio, is_test=(not model.train))
    if cfg.MODEL.SPECS.find('not_stop_gradient') < 0:
        model.StopGradient(s_obj, s_obj)  # this is to stop the gradient from p branch

    model.FC(
        'rel_roi_pool_prd', 'rel_fc6_prd', dim_in * 7 * 7, 4096,
        weight_init=('GaussianFill', {'std': 0.01}),
        bias_init=('ConstantFill', {'value': 0.}))
    model.Relu('rel_fc6_prd', 'rel_fc6_prd')
    model.Dropout(
        'rel_fc6_prd', 'rel_fc6_prd_dropout',
        ratio=dropout_ratio, is_test=(not model.train))
    model.FC(
        'rel_fc6_prd_dropout', 'rel_fc7_prd', 4096, 4096,
        weight_init=('GaussianFill', {'std': 0.01}),
        bias_init=('ConstantFill', {'value': 0.}))
    model.Relu('rel_fc7_prd', 'rel_fc7_prd')
    s_prd = model.Dropout(
        'rel_fc7_prd', 'rel_fc7_prd_dropout',
        ratio=dropout_ratio, is_test=(not model.train))

    if cfg.MODEL.TYPE.find('spo_for_p') >= 0:
        s_rel = model.Concat([s_sbj, s_obj, s_prd], 'rel_concat')
        dim_s_rel = 4096 * 3
        return s_rel, dim_s_rel, s_sbj, 4096, s_obj, 4096, spatial_scale
    else:
        return s_prd, 4096, s_sbj, 4096, s_obj, 4096, spatial_scale