def create_faster_rcnn_eval_model(model, image_input, dims_input, cfg, rpn_model=None):
print("creating eval model")
last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
conv_layers = clone_model(model, [cfg["MODEL"].FEATURE_NODE_NAME], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
model_with_rpn = model if rpn_model is None else rpn_model
rpn = clone_model(model_with_rpn, [last_conv_node_name], ["rpn_cls_prob_reshape", "rpn_bbox_pred"], CloneMethod.freeze)
rpn_out = rpn(conv_out)
# we need to add the proposal layer anew to account for changing configs when buffering proposals in 4-stage training
rpn_rois = create_proposal_layer(rpn_out.outputs[0], rpn_out.outputs[1], dims_input, cfg)
roi_fc_layers = clone_model(model, [last_conv_node_name, "rpn_target_rois"], ["cls_score", "bbox_regr"], CloneMethod.freeze)
pred_net = roi_fc_layers(conv_out, rpn_rois)
cls_score = pred_net.outputs[0]
bbox_regr = pred_net.outputs[1]
if cfg.BBOX_NORMALIZE_TARGETS:
num_boxes = int(bbox_regr.shape[1] / 4)
bbox_normalize_means = np.array(cfg.BBOX_NORMALIZE_MEANS * num_boxes)
bbox_normalize_stds = np.array(cfg.BBOX_NORMALIZE_STDS * num_boxes)
bbox_regr = plus(element_times(bbox_regr, bbox_normalize_stds), bbox_normalize_means, name='bbox_regr')
cls_pred = softmax(cls_score, axis=1, name='cls_pred')
eval_model = combine([cls_pred, rpn_rois, bbox_regr])
return eval_model
def create_faster_rcnn_eval_model(model, image_input, dims_input, cfg, rpn_model=None):
print("creating eval model")
last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
conv_layers = clone_model(model, [cfg["MODEL"].FEATURE_NODE_NAME], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
model_with_rpn = model if rpn_model is None else rpn_model
rpn = clone_model(model_with_rpn, [last_conv_node_name], ["rpn_cls_prob_reshape", "rpn_bbox_pred"], CloneMethod.freeze)
rpn_out = rpn(conv_out)
# we need to add the proposal layer anew to account for changing configs when buffering proposals in 4-stage training
rpn_rois = create_proposal_layer(rpn_out.outputs[0], rpn_out.outputs[1], dims_input, cfg)
roi_fc_layers = clone_model(model, [last_conv_node_name, "rpn_target_rois"], ["cls_score", "bbox_regr"], CloneMethod.freeze)
pred_net = roi_fc_layers(conv_out, rpn_rois)
cls_score = pred_net.outputs[0]
bbox_regr = pred_net.outputs[1]
if cfg.BBOX_NORMALIZE_TARGETS:
num_boxes = int(bbox_regr.shape[1] / 4)
bbox_normalize_means = np.array(cfg.BBOX_NORMALIZE_MEANS * num_boxes)
bbox_normalize_stds = np.array(cfg.BBOX_NORMALIZE_STDS * num_boxes)
bbox_regr = plus(element_times(bbox_regr, bbox_normalize_stds), bbox_normalize_means, name='bbox_regr')
cls_pred = softmax(cls_score, axis=1, name='cls_pred')
eval_model = combine([cls_pred, rpn_rois, bbox_regr])
return eval_model
