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
def create_faster_rcnn_model(features, scaled_gt_boxes, dims_input, cfg):
# Load the pre-trained classification net and clone layers
base_model = load_model(cfg['BASE_MODEL_PATH'])
conv_layers = clone_conv_layers(base_model, cfg)
fc_layers = clone_model(base_model, [cfg["MODEL"].POOL_NODE_NAME],
[cfg["MODEL"].LAST_HIDDEN_NODE_NAME],
clone_method=CloneMethod.clone)
# Normalization and conv layers
feat_norm = features - Constant([[[v]]
for v in cfg["MODEL"].IMG_PAD_COLOR])
conv_out = conv_layers(feat_norm)
# RPN and prediction targets
rpn_rois, rpn_losses = create_rpn(conv_out, scaled_gt_boxes, dims_input,
cfg)
rois, label_targets, bbox_targets, bbox_inside_weights = \
create_proposal_target_layer(rpn_rois, scaled_gt_boxes, cfg)
# Fast RCNN and losses
cls_score, bbox_pred = create_fast_rcnn_predictor(conv_out, rois,
fc_layers, cfg)
detection_losses = create_detection_losses(cls_score, label_targets,
bbox_pred, rois, bbox_targets,
bbox_inside_weights, cfg)
loss = rpn_losses + detection_losses
pred_error = classification_error(cls_score, label_targets, axis=1)
return loss, pred_error
def create_faster_rcnn_model(features, scaled_gt_boxes, dims_input, cfg):
# Load the pre-trained classification net and clone layers
base_model = load_model(cfg['BASE_MODEL_PATH'])
conv_layers = clone_conv_layers(base_model, cfg)
fc_layers = clone_model(base_model, [cfg["MODEL"].POOL_NODE_NAME], [cfg["MODEL"].LAST_HIDDEN_NODE_NAME], clone_method=CloneMethod.clone)
# Normalization and conv layers
feat_norm = features - Constant([[[v]] for v in cfg["MODEL"].IMG_PAD_COLOR])
conv_out = conv_layers(feat_norm)
# RPN and prediction targets
rpn_rois, rpn_losses = create_rpn(conv_out, scaled_gt_boxes, dims_input, cfg)
rois, label_targets, bbox_targets, bbox_inside_weights = \
create_proposal_target_layer(rpn_rois, scaled_gt_boxes, cfg)
# Fast RCNN and losses
cls_score, bbox_pred = create_fast_rcnn_predictor(conv_out, rois, fc_layers, cfg)
detection_losses = create_detection_losses(cls_score, label_targets, bbox_pred, rois, bbox_targets, bbox_inside_weights, cfg)
loss = rpn_losses + detection_losses
pred_error = classification_error(cls_score, label_targets, axis=1)
return loss, pred_error
def train_faster_rcnn_alternating(cfg):
'''
4-Step Alternating Training scheme from the Faster R-CNN paper:
# Create initial network, only rpn, without detection network
# --> train only the rpn (and conv3_1 and up for VGG16)
# buffer region proposals from rpn
# Create full network, initialize conv layers with imagenet, use buffered proposals
# --> train only detection network (and conv3_1 and up for VGG16)
# Keep conv weights from detection network and fix them
# --> train only rpn
# buffer region proposals from rpn
# Keep conv and rpn weights from step 3 and fix them
# --> train only detection network
'''
# setting pre- and post-nms top N to training values since buffered proposals are used for further training
test_pre = cfg["TEST"].RPN_PRE_NMS_TOP_N
test_post = cfg["TEST"].RPN_POST_NMS_TOP_N
cfg["TEST"].RPN_PRE_NMS_TOP_N = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
cfg["TEST"].RPN_POST_NMS_TOP_N = cfg["TRAIN"].RPN_POST_NMS_TOP_N
# Learning parameters
rpn_lr_factor = cfg["MODEL"].RPN_LR_FACTOR
rpn_lr_per_sample_scaled = [x * rpn_lr_factor for x in cfg["CNTK"].RPN_LR_PER_SAMPLE]
frcn_lr_factor = cfg["MODEL"].FRCN_LR_FACTOR
frcn_lr_per_sample_scaled = [x * frcn_lr_factor for x in cfg["CNTK"].FRCN_LR_PER_SAMPLE]
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
rpn_epochs = cfg["CNTK"].RPN_EPOCHS
frcn_epochs = cfg["CNTK"].FRCN_EPOCHS
feature_node_name = cfg["MODEL"].FEATURE_NODE_NAME
last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("rpn_lr_per_sample: {}".format(rpn_lr_per_sample_scaled))
print("frcn_lr_per_sample: {}".format(frcn_lr_per_sample_scaled))
debug_output=cfg["CNTK"].DEBUG_OUTPUT
if debug_output:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
# Input variables denoting features, labeled ground truth rois (as 5-tuples per roi) and image dimensions
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT, cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=feature_node_name)
feat_norm = image_input - Constant([[[v]] for v in cfg["MODEL"].IMG_PAD_COLOR])
roi_input = input_variable((cfg.INPUT_ROIS_PER_IMAGE, 5), dynamic_axes=[Axis.default_batch_axis()])
scaled_gt_boxes = alias(roi_input, name='roi_input')
dims_input = input_variable((6), dynamic_axes=[Axis.default_batch_axis()])
dims_node = alias(dims_input, name='dims_input')
rpn_rois_input = input_variable((cfg["TRAIN"].RPN_POST_NMS_TOP_N, 4), dynamic_axes=[Axis.default_batch_axis()])
rpn_rois_buf = alias(rpn_rois_input, name='rpn_rois')
# base image classification model (e.g. VGG16 or AlexNet)
base_model = load_model(cfg['BASE_MODEL_PATH'])
print("stage 1a - rpn")
if True:
# Create initial network, only rpn, without detection network
# initial weights train?
# conv: base_model only conv3_1 and up
# rpn: init new yes
# frcn: - -
# conv layers
conv_layers = clone_conv_layers(base_model, cfg)
conv_out = conv_layers(feat_norm)
# RPN and losses
rpn_rois, rpn_losses = create_rpn(conv_out, scaled_gt_boxes, dims_node, cfg)
stage1_rpn_network = combine([rpn_rois, rpn_losses])
# train
if debug_output: plot(stage1_rpn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage1a_rpn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, rpn_losses, rpn_losses,
rpn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, rpn_epochs, cfg)
print("stage 1a - buffering rpn proposals")
buffered_proposals_s1 = compute_rpn_proposals(stage1_rpn_network, image_input, roi_input, dims_input, cfg)
print("stage 1b - frcn")
if True:
# Create full network, initialize conv layers with imagenet, fix rpn weights
# initial weights train?
# conv: base_model only conv3_1 and up
# rpn: stage1a rpn model no --> use buffered proposals
# frcn: base_model + new yes
# conv_layers
conv_layers = clone_conv_layers(base_model, cfg)
conv_out = conv_layers(feat_norm)
# use buffered proposals in target layer
rois, label_targets, bbox_targets, bbox_inside_weights = \
create_proposal_target_layer(rpn_rois_buf, scaled_gt_boxes, cfg)
# Fast RCNN and losses
fc_layers = clone_model(base_model, [cfg["MODEL"].POOL_NODE_NAME], [cfg["MODEL"].LAST_HIDDEN_NODE_NAME], CloneMethod.clone)
cls_score, bbox_pred = create_fast_rcnn_predictor(conv_out, rois, fc_layers, cfg)
detection_losses = create_detection_losses(cls_score, label_targets, bbox_pred, rois, bbox_targets, bbox_inside_weights, cfg)
pred_error = classification_error(cls_score, label_targets, axis=1, name="pred_error")
stage1_frcn_network = combine([rois, cls_score, bbox_pred, detection_losses, pred_error])
# train
if debug_output: plot(stage1_frcn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage1b_frcn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, detection_losses, pred_error,
frcn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, frcn_epochs, cfg,
rpn_rois_input=rpn_rois_input, buffered_rpn_proposals=buffered_proposals_s1)
buffered_proposals_s1 = None
print("stage 2a - rpn")
if True:
# Keep conv weights from detection network and fix them
# initial weights train?
# conv: stage1b frcn model no
# rpn: stage1a rpn model yes
# frcn: - -
# conv_layers
conv_layers = clone_model(stage1_frcn_network, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
# RPN and losses
rpn = clone_model(stage1_rpn_network, [last_conv_node_name, "roi_input", "dims_input"], ["rpn_rois", "rpn_losses"], CloneMethod.clone)
rpn_net = rpn(conv_out, dims_node, scaled_gt_boxes)
rpn_rois = rpn_net.outputs[0]
rpn_losses = rpn_net.outputs[1]
stage2_rpn_network = combine([rpn_rois, rpn_losses])
# train
if debug_output: plot(stage2_rpn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage2a_rpn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, rpn_losses, rpn_losses,
rpn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, rpn_epochs, cfg)
print("stage 2a - buffering rpn proposals")
buffered_proposals_s2 = compute_rpn_proposals(stage2_rpn_network, image_input, roi_input, dims_input, cfg)
print("stage 2b - frcn")
if True:
# Keep conv and rpn weights from step 3 and fix them
# initial weights train?
# conv: stage2a rpn model no
# rpn: stage2a rpn model no --> use buffered proposals
# frcn: stage1b frcn model yes -
# conv_layers
conv_layers = clone_model(stage2_rpn_network, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
# Fast RCNN and losses
frcn = clone_model(stage1_frcn_network, [last_conv_node_name, "rpn_rois", "roi_input"],
["cls_score", "bbox_regr", "rpn_target_rois", "detection_losses", "pred_error"], CloneMethod.clone)
stage2_frcn_network = frcn(conv_out, rpn_rois_buf, scaled_gt_boxes)
detection_losses = stage2_frcn_network.outputs[3]
pred_error = stage2_frcn_network.outputs[4]
# train
if debug_output: plot(stage2_frcn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage2b_frcn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, detection_losses, pred_error,
frcn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, frcn_epochs, cfg,
rpn_rois_input=rpn_rois_input, buffered_rpn_proposals=buffered_proposals_s2)
buffered_proposals_s2 = None
# resetting config values to original test values
cfg["TEST"].RPN_PRE_NMS_TOP_N = test_pre
cfg["TEST"].RPN_POST_NMS_TOP_N = test_post
return create_faster_rcnn_eval_model(stage2_frcn_network, image_input, dims_input, cfg, rpn_model=stage2_rpn_network)
def train_faster_rcnn_alternating(cfg):
'''
4-Step Alternating Training scheme from the Faster R-CNN paper:
# Create initial network, only rpn, without detection network
# --> train only the rpn (and conv3_1 and up for VGG16)
# buffer region proposals from rpn
# Create full network, initialize conv layers with imagenet, use buffered proposals
# --> train only detection network (and conv3_1 and up for VGG16)
# Keep conv weights from detection network and fix them
# --> train only rpn
# buffer region proposals from rpn
# Keep conv and rpn weights from step 3 and fix them
# --> train only detection network
'''
# setting pre- and post-nms top N to training values since buffered proposals are used for further training
test_pre = cfg["TEST"].RPN_PRE_NMS_TOP_N
test_post = cfg["TEST"].RPN_POST_NMS_TOP_N
cfg["TEST"].RPN_PRE_NMS_TOP_N = cfg["TRAIN"].RPN_PRE_NMS_TOP_N
cfg["TEST"].RPN_POST_NMS_TOP_N = cfg["TRAIN"].RPN_POST_NMS_TOP_N
# Learning parameters
rpn_lr_factor = cfg["MODEL"].RPN_LR_FACTOR
rpn_lr_per_sample_scaled = [x * rpn_lr_factor for x in cfg["CNTK"].RPN_LR_PER_SAMPLE]
frcn_lr_factor = cfg["MODEL"].FRCN_LR_FACTOR
frcn_lr_per_sample_scaled = [x * frcn_lr_factor for x in cfg["CNTK"].FRCN_LR_PER_SAMPLE]
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
rpn_epochs = cfg["CNTK"].RPN_EPOCHS
frcn_epochs = cfg["CNTK"].FRCN_EPOCHS
feature_node_name = cfg["MODEL"].FEATURE_NODE_NAME
last_conv_node_name = cfg["MODEL"].LAST_CONV_NODE_NAME
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("rpn_lr_per_sample: {}".format(rpn_lr_per_sample_scaled))
print("frcn_lr_per_sample: {}".format(frcn_lr_per_sample_scaled))
debug_output=cfg["CNTK"].DEBUG_OUTPUT
if debug_output:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
# Input variables denoting features, labeled ground truth rois (as 5-tuples per roi) and image dimensions
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT, cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=feature_node_name)
feat_norm = image_input - Constant([[[v]] for v in cfg["MODEL"].IMG_PAD_COLOR])
roi_input = input_variable((cfg.INPUT_ROIS_PER_IMAGE, 5), dynamic_axes=[Axis.default_batch_axis()])
scaled_gt_boxes = alias(roi_input, name='roi_input')
dims_input = input_variable((6), dynamic_axes=[Axis.default_batch_axis()])
dims_node = alias(dims_input, name='dims_input')
rpn_rois_input = input_variable((cfg["TRAIN"].RPN_POST_NMS_TOP_N, 4), dynamic_axes=[Axis.default_batch_axis()])
rpn_rois_buf = alias(rpn_rois_input, name='rpn_rois')
# base image classification model (e.g. VGG16 or AlexNet)
base_model = load_model(cfg['BASE_MODEL_PATH'])
print("stage 1a - rpn")
if True:
# Create initial network, only rpn, without detection network
# initial weights train?
# conv: base_model only conv3_1 and up
# rpn: init new yes
# frcn: - -
# conv layers
conv_layers = clone_conv_layers(base_model, cfg)
conv_out = conv_layers(feat_norm)
# RPN and losses
rpn_rois, rpn_losses = create_rpn(conv_out, scaled_gt_boxes, dims_node, cfg)
stage1_rpn_network = combine([rpn_rois, rpn_losses])
# train
if debug_output: plot(stage1_rpn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage1a_rpn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, rpn_losses, rpn_losses,
rpn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, rpn_epochs, cfg)
print("stage 1a - buffering rpn proposals")
buffered_proposals_s1 = compute_rpn_proposals(stage1_rpn_network, image_input, roi_input, dims_input, cfg)
print("stage 1b - frcn")
if True:
# Create full network, initialize conv layers with imagenet, fix rpn weights
# initial weights train?
# conv: base_model only conv3_1 and up
# rpn: stage1a rpn model no --> use buffered proposals
# frcn: base_model + new yes
# conv_layers
conv_layers = clone_conv_layers(base_model, cfg)
conv_out = conv_layers(feat_norm)
# use buffered proposals in target layer
rois, label_targets, bbox_targets, bbox_inside_weights = \
create_proposal_target_layer(rpn_rois_buf, scaled_gt_boxes, cfg)
# Fast RCNN and losses
fc_layers = clone_model(base_model, [cfg["MODEL"].POOL_NODE_NAME], [cfg["MODEL"].LAST_HIDDEN_NODE_NAME], CloneMethod.clone)
cls_score, bbox_pred = create_fast_rcnn_predictor(conv_out, rois, fc_layers, cfg)
detection_losses = create_detection_losses(cls_score, label_targets, bbox_pred, rois, bbox_targets, bbox_inside_weights, cfg)
pred_error = classification_error(cls_score, label_targets, axis=1, name="pred_error")
stage1_frcn_network = combine([rois, cls_score, bbox_pred, detection_losses, pred_error])
# train
if debug_output: plot(stage1_frcn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage1b_frcn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, detection_losses, pred_error,
frcn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, frcn_epochs, cfg,
rpn_rois_input=rpn_rois_input, buffered_rpn_proposals=buffered_proposals_s1)
buffered_proposals_s1 = None
print("stage 2a - rpn")
if True:
# Keep conv weights from detection network and fix them
# initial weights train?
# conv: stage1b frcn model no
# rpn: stage1a rpn model yes
# frcn: - -
# conv_layers
conv_layers = clone_model(stage1_frcn_network, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
# RPN and losses
rpn = clone_model(stage1_rpn_network, [last_conv_node_name, "roi_input", "dims_input"], ["rpn_rois", "rpn_losses"], CloneMethod.clone)
rpn_net = rpn(conv_out, dims_node, scaled_gt_boxes)
rpn_rois = rpn_net.outputs[0]
rpn_losses = rpn_net.outputs[1]
stage2_rpn_network = combine([rpn_rois, rpn_losses])
# train
if debug_output: plot(stage2_rpn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage2a_rpn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, rpn_losses, rpn_losses,
rpn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, rpn_epochs, cfg)
print("stage 2a - buffering rpn proposals")
buffered_proposals_s2 = compute_rpn_proposals(stage2_rpn_network, image_input, roi_input, dims_input, cfg)
print("stage 2b - frcn")
if True:
# Keep conv and rpn weights from step 3 and fix them
# initial weights train?
# conv: stage2a rpn model no
# rpn: stage2a rpn model no --> use buffered proposals
# frcn: stage1b frcn model yes -
# conv_layers
conv_layers = clone_model(stage2_rpn_network, [feature_node_name], [last_conv_node_name], CloneMethod.freeze)
conv_out = conv_layers(image_input)
# Fast RCNN and losses
frcn = clone_model(stage1_frcn_network, [last_conv_node_name, "rpn_rois", "roi_input"],
["cls_score", "bbox_regr", "rpn_target_rois", "detection_losses", "pred_error"], CloneMethod.clone)
stage2_frcn_network = frcn(conv_out, rpn_rois_buf, scaled_gt_boxes)
detection_losses = stage2_frcn_network.outputs[3]
pred_error = stage2_frcn_network.outputs[4]
# train
if debug_output: plot(stage2_frcn_network, os.path.join(cfg.OUTPUT_PATH, "graph_frcn_train_stage2b_frcn." + cfg["CNTK"].GRAPH_TYPE))
train_model(image_input, roi_input, dims_input, detection_losses, pred_error,
frcn_lr_per_sample_scaled, mm_schedule, l2_reg_weight, frcn_epochs, cfg,
rpn_rois_input=rpn_rois_input, buffered_rpn_proposals=buffered_proposals_s2)
buffered_proposals_s2 = None
# resetting config values to original test values
cfg["TEST"].RPN_PRE_NMS_TOP_N = test_pre
cfg["TEST"].RPN_POST_NMS_TOP_N = test_post
return create_faster_rcnn_eval_model(stage2_frcn_network, image_input, dims_input, cfg, rpn_model=stage2_rpn_network)
