import mxnet as mx from common import multi_layer_feature, multibox_layer def import_module(module_name): """Helper function to import module""" import sys, os import importlib sys.path.append(os.path.dirname(__file__)) return importlib.import_module(module_name) def get_symbol_train(network, num_classes, from_layers, num_filters, strides, pads, sizes, ratios, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network symbol for training SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ label = mx.sym.Variable('label') body = import_module(network).get_symbol(num_classes, **kwargs) layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) tmp = mx.contrib.symbol.MultiBoxTarget( *[anchor_boxes, label, cls_preds], overlap_threshold=.5, \ ignore_label=-1, negative_mining_ratio=3, minimum_negative_samples=0, \ negative_mining_thresh=.5, variances=(0.1, 0.1, 0.2, 0.2), name="multibox_target") loc_target = tmp[0] loc_target_mask = tmp[1] cls_target = tmp[2] cls_prob = mx.symbol.SoftmaxOutput(data=cls_preds, label=cls_target, \ ignore_label=-1, use_ignore=True, grad_scale=1., multi_output=True, \ normalization='valid', name="cls_prob") loc_loss_ = mx.symbol.smooth_l1(name="loc_loss_", \ data=loc_target_mask * (loc_preds - loc_target), scalar=1.0) loc_loss = mx.symbol.MakeLoss(loc_loss_, grad_scale=1., \ normalization='valid', name="loc_loss") # monitoring training status cls_label = mx.symbol.MakeLoss(data=cls_target, grad_scale=0, name="cls_label") det = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) det = mx.symbol.MakeLoss(data=det, grad_scale=0, name="det_out") # group output out = mx.symbol.Group([cls_prob, loc_loss, cls_label, det]) return out def get_symbol(network, num_classes, from_layers, num_filters, sizes, ratios, strides, pads, normalizations=-1, steps=[], min_filter=128, nms_thresh=0.5, force_suppress=False, nms_topk=400, **kwargs): """Build network for testing SSD Parameters ---------- network : str base network symbol name num_classes : int number of object classes not including background from_layers : list of str feature extraction layers, use '' for add extra layers For example: from_layers = ['relu4_3', 'fc7', '', '', '', ''] which means extract feature from relu4_3 and fc7, adding 4 extra layers on top of fc7 num_filters : list of int number of filters for extra layers, you can use -1 for extracted features, however, if normalization and scale is applied, the number of filter for that layer must be provided. For example: num_filters = [512, -1, 512, 256, 256, 256] strides : list of int strides for the 3x3 convolution appended, -1 can be used for extracted feature layers pads : list of int paddings for the 3x3 convolution, -1 can be used for extracted layers sizes : list or list of list [min_size, max_size] for all layers or [[], [], []...] for specific layers ratios : list or list of list [ratio1, ratio2...] for all layers or [[], [], ...] for specific layers normalizations : int or list of int use normalizations value for all layers or [...] for specific layers, -1 indicate no normalizations and scales steps : list specify steps for each MultiBoxPrior layer, leave empty, it will calculate according to layer dimensions min_filter : int minimum number of filters used in 1x1 convolution nms_thresh : float non-maximum suppression threshold force_suppress : boolean whether suppress different class objects nms_topk : int apply NMS to top K detections Returns ------- mx.Symbol """ body = import_module(network).get_symbol(num_classes, **kwargs) layers = multi_layer_feature(body, from_layers, num_filters, strides, pads, min_filter=min_filter) loc_preds, cls_preds, anchor_boxes = multibox_layer(layers, \ num_classes, sizes=sizes, ratios=ratios, normalization=normalizations, \ num_channels=num_filters, clip=False, interm_layer=0, steps=steps) cls_prob = mx.symbol.SoftmaxActivation(data=cls_preds, mode='channel', \ name='cls_prob') out = mx.contrib.symbol.MultiBoxDetection(*[cls_prob, loc_preds, anchor_boxes], \ name="detection", nms_threshold=nms_thresh, force_suppress=force_suppress, variances=(0.1, 0.1, 0.2, 0.2), nms_topk=nms_topk) return out