| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import mxnet as mx |
| from symbol.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.symbol.contrib.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.symbol.contrib.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.softmax(data=cls_preds, axis=1, name='cls_prob') |
| out = mx.symbol.contrib.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 |
