SSD demo中详细介绍了如何在VOC数据集上使用SSD进行物体检测的训练和验证。本文介绍如何使用SSD实现对自己数据集的训练和验证过程,内容包括:
1 数据集的标注2 数据集的转换3 使用SSD如何训练4 使用SSD如何测试 1 数据集的标注 数据的标注使用BBox-Label-Tool工具,该工具使用python实现,使用简单方便。该工具生成的标签格式是:object_numberx1min y1min x1max y1maxx2min y2min x2max y2max...2 数据集的转换 caffe训练使用LMDB格式的数据,ssd框架中提供了voc数据格式转换成LMDB格式的脚本。所以实践中先将BBox-Label-Tool标注的数据转换成voc数据格式,然后再转换成LMDB格式。 2.1 voc数据格式
(1)Annotations中保存的是xml格式的label信息
<?xml version="1.0" ?> <annotation> <folder>VOC2007</folder> <filename>1.jpg</filename> <source> <database>My Database</database> <annotation>VOC2007</annotation> <image>flickr</image> <flickrid>NULL</flickrid> </source> <owner> <flickrid>NULL</flickrid> <name>idaneel</name> </owner> <size> <width>320</width> <height>240</height> <depth>3</depth> </size> <segmented>0</segmented> <object> <name>door</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>109</xmin> <ymin>3</ymin> <xmax>199</xmax> <ymax>204</ymax> </bndbox> </object> </annotation>
VOC XML内容信息
(2)ImageSet目录下的Main目录里存放的是用于表示训练的图片集和测试的图片集
(3)JPEGImages目录下存放所有图片集
(4)label目录下保存的是BBox-Label-Tool工具标注好的bounding box坐标文件,该目录下的文件就是待转换的label标签文件。
2.2 Label转换成VOC数据格式
BBox-Label-Tool工具标注好的bounding box坐标文件转换成VOC数据格式的形式.具体的转换过程包括了两个步骤:(1)将BBox-Label-Tool下的txt格式保存的bounding box信息转换成VOC数据格式下以xml方式表示;(2)生成用于训练的数据集和用于测试的数据集。用python实现了上述两个步骤的换转。createXml.py 完成txt到xml的转换; 执行脚本./createXml.py %classname%
#!/usr/bin/env python import os import sys import cv2 from itertools import islice from xml.dom.minidom import Document labels=‘label‘ imgpath=‘JPEGImages/‘ xmlpath_new=‘Annotations/‘ foldername=‘VOC2007‘ try: labelName = sys.argv[1] except: print ‘Please input class name‘ print ‘./createXml dog‘ os._exit(0) def insertObject(doc, datas): obj = doc.createElement(‘object‘) name = doc.createElement(‘name‘) name.appendChild(doc.createTextNode(labelName)) obj.appendChild(name) pose = doc.createElement(‘pose‘) pose.appendChild(doc.createTextNode(‘Unspecified‘)) obj.appendChild(pose) truncated = doc.createElement(‘truncated‘) truncated.appendChild(doc.createTextNode(str(0))) obj.appendChild(truncated) difficult = doc.createElement(‘difficult‘) difficult.appendChild(doc.createTextNode(str(0))) obj.appendChild(difficult) bndbox = doc.createElement(‘bndbox‘) xmin = doc.createElement(‘xmin‘) xmin.appendChild(doc.createTextNode(str(datas[0]))) bndbox.appendChild(xmin) ymin = doc.createElement(‘ymin‘) ymin.appendChild(doc.createTextNode(str(datas[1]))) bndbox.appendChild(ymin) xmax = doc.createElement(‘xmax‘) xmax.appendChild(doc.createTextNode(str(datas[2]))) bndbox.appendChild(xmax) ymax = doc.createElement(‘ymax‘) ymax.appendChild(doc.createTextNode(str(datas[3])[0:-1])) bndbox.appendChild(ymax) obj.appendChild(bndbox) return obj def create(): for walk in os.walk(labels): for each in walk[2]: fidin=open(walk[0] + ‘/‘+ each,‘r‘) objIndex = 0 for data in islice(fidin, 1, None): objIndex += 1 data=data.strip(‘\n‘) datas = data.split(‘ ‘) pictureName = each.replace(‘.txt‘, ‘.jpg‘) imageFile = imgpath + pictureName img = cv2.imread(imageFile) imgSize = img.shape if 1 == objIndex: xmlName = each.replace(‘.txt‘, ‘.xml‘) f = open(xmlpath_new + xmlName, "w") doc = Document() annotation = doc.createElement(‘annotation‘) doc.appendChild(annotation) folder = doc.createElement(‘folder‘) folder.appendChild(doc.createTextNode(foldername)) annotation.appendChild(folder) filename = doc.createElement(‘filename‘) filename.appendChild(doc.createTextNode(pictureName)) annotation.appendChild(filename) source = doc.createElement(‘source‘) database = doc.createElement(‘database‘) database.appendChild(doc.createTextNode(‘My Database‘)) source.appendChild(database) source_annotation = doc.createElement(‘annotation‘) source_annotation.appendChild(doc.createTextNode(foldername)) source.appendChild(source_annotation) image = doc.createElement(‘image‘) image.appendChild(doc.createTextNode(‘flickr‘)) source.appendChild(image) flickrid = doc.createElement(‘flickrid‘) flickrid.appendChild(doc.createTextNode(‘NULL‘)) source.appendChild(flickrid) annotation.appendChild(source) owner = doc.createElement(‘owner‘) flickrid = doc.createElement(‘flickrid‘) flickrid.appendChild(doc.createTextNode(‘NULL‘)) owner.appendChild(flickrid) name = doc.createElement(‘name‘) name.appendChild(doc.createTextNode(‘idaneel‘)) owner.appendChild(name) annotation.appendChild(owner) size = doc.createElement(‘size‘) width = doc.createElement(‘width‘) width.appendChild(doc.createTextNode(str(imgSize[1]))) size.appendChild(width) height = doc.createElement(‘height‘) height.appendChild(doc.createTextNode(str(imgSize[0]))) size.appendChild(height) depth = doc.createElement(‘depth‘) depth.appendChild(doc.createTextNode(str(imgSize[2]))) size.appendChild(depth) annotation.appendChild(size) segmented = doc.createElement(‘segmented‘) segmented.appendChild(doc.createTextNode(str(0))) annotation.appendChild(segmented) annotation.appendChild(insertObject(doc, datas)) else: annotation.appendChild(insertObject(doc, datas)) try: f.write(doc.toprettyxml(indent = ‘ ‘)) f.close() fidin.close() except: pass if __name__ == ‘__main__‘: create()
createXml.py
createTest.py 生成训练集和测试集标识文件; 执行脚本
./createTest.py %startID% %endID% %testNumber%
#!/usr/bin/env python import os import sys import random try: start = int(sys.argv[1]) end = int(sys.argv[2]) test = int(sys.argv[3]) allNum = end-start+1 except: print ‘Please input picture range‘ print ‘./createTest.py 1 1500 500‘ os._exit(0) b_list = range(start,end) blist_webId = random.sample(b_list, test) blist_webId = sorted(blist_webId) allFile = [] testFile = open(‘ImageSets/Main/test.txt‘, ‘w‘) trainFile = open(‘ImageSets/Main/trainval.txt‘, ‘w‘) for i in range(allNum): allFile.append(i+1) for test in blist_webId: allFile.remove(test) testFile.write(str(test) + ‘\n‘) for train in allFile: trainFile.write(str(train) + ‘\n‘) testFile.close() trainFile.close()
createTest.py
说明: 由于BBox-Label-Tool实现相对简单,该工具每次只能对一个类别进行打标签,所以转换脚本
每一次也是对一个类别进行数据的转换,这个问题后续需要优化改进。
2.3 VOC数据转换成LMDB数据
SSD提供了VOC数据到LMDB数据的转换脚本 data/VOC0712/create_list.sh 和 ./data/VOC0712/create_data.sh,这两个脚本是完全针对VOC0712目录下的数据进行的转换。 实现中为了不破坏VOC0712目录下的数据内容,针对我们自己的数据集,修改了上面这两个脚本,将脚本中涉及到VOC0712的信息替换成我们自己的目录信息。在处理我们的数据集时,将VOC0712替换成indoor。具体的步骤如下: (1) 在 $HOME/data/VOCdevkit目录下创建indoor目录,该目录中存放自己转换完成的VOC数据集; (2) $CAFFE_ROOT/examples目录下创建indoor目录; (3) $CAFFE_ROOT/data目录下创建indoor目录,同时将data/VOC0712下的create_list.sh,create_data.sh,labelmap_voc.prototxt这三个文件copy到indoor目录下,分别重命名为create_list_indoor.sh,create_data_indoor.sh, labelmap_indoor.prototxt (4)对上面新生成的两个create文件进行修改,主要修改是将VOC0712相关的信息替换成indoor 修改后的这两个文件分别为:
#!/bin/bash root_dir=$HOME/data/VOCdevkit/ sub_dir=ImageSets/Main bash_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" for dataset in trainval test do dst_file=$bash_dir/$dataset.txt if [ -f $dst_file ] then rm -f $dst_file fi for name in indoor do if [[ $dataset == "test" && $name == "VOC2012" ]] then continue fi echo "Create list for $name $dataset..." dataset_file=$root_dir/$name/$sub_dir/$dataset.txt img_file=$bash_dir/$dataset"_img.txt" cp $dataset_file $img_file sed -i "s/^/$name\/JPEGImages\//g" $img_file sed -i "s/$/.jpg/g" $img_file label_file=$bash_dir/$dataset"_label.txt" cp $dataset_file $label_file sed -i "s/^/$name\/Annotations\//g" $label_file sed -i "s/$/.xml/g" $label_file paste -d‘ ‘ $img_file $label_file >> $dst_file rm -f $label_file rm -f $img_file done # Generate image name and size infomation. if [ $dataset == "test" ] then $bash_dir/../../build/tools/get_image_size $root_dir $dst_file $bash_dir/$dataset"_name_size.txt" fi # Shuffle trainval file. if [ $dataset == "trainval" ] then rand_file=$dst_file.random cat $dst_file | perl -MList::Util=shuffle -e ‘print shuffle(<STDIN>);‘ > $rand_file mv $rand_file $dst_file fi done
create_list_indoor.sh
cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) root_dir=$cur_dir/../.. cd $root_dir redo=1 data_root_dir="$HOME/data/VOCdevkit" dataset_name="indoor" mapfile="$root_dir/data/$dataset_name/labelmap_indoor.prototxt" anno_type="detection" db="lmdb" min_dim=0 max_dim=0 width=0 height=0 extra_cmd="--encode-type=jpg --encoded" if [ $redo ] then extra_cmd="$extra_cmd --redo" fi for subset in test trainval do python $root_dir/scripts/create_annoset.py --anno-type=$anno_type --label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim --resize-width=$width --resize-height=$height --check-label $extra_cmd $data_root_dir $root_dir/data/$dataset_name/$subset.txt $data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$db examples/$dataset_name done
create_data_indoor.sh
(5)修改labelmap_indoor.prototxt,将该文件中的类别修改成和自己的数据集相匹配,注意需要保留一个label 0 , background类别
item { name: "none_of_the_above" label: 0 display_name: "background" } item { name: "door" label: 1 display_name: "door" }
labelmap_indoor.prototxt
完成上面步骤的修改后,可以开始LMDB数据数据的制作,在$CAFFE_ROOT目录下分别运行:
./data/indoor/create_list_indoor.sh
./data/indoor/create_data_indoor.sh
命令执行完毕后,可以在$CAFFE_ROOT/indoor目录下查看转换完成的LMDB数据数据。
3 使用SSD进行自己数据集的训练
训练时使用ssd demo中提供的预训练好的VGGnet model : VGG_ILSVRC_16_layers_fc_reduced.caffemodel将该模型保存到$CAFFE_ROOT/models/VGGNet下。将ssd_pascal.py copy一份 ssd_pascal_indoor.py文件, 根据自己的数据集修改ssd_pascal_indoor.py
主要修改点: (1)train_data和test_data修改成指向自己的数据集LMDB train_data = "examples/indoor/indoor_trainval_lmdb" test_data = "examples/indoor/indoor_test_lmdb"(2) num_test_image该变量修改成自己数据集中测试数据的数量
(3)num_classes 该变量修改成自己数据集中 标签类别数量数 + 1
针对我的数据集,ssd_pascal_indoor.py
的内容为:
from __future__ import print_function import caffe from caffe.model_libs import * from google.protobuf import text_format import math import os import shutil import stat import subprocess import sys # Add extra layers on top of a "base" network (e.g. VGGNet or Inception). def AddExtraLayers(net, use_batchnorm=True): use_relu = True # Add additional convolutional layers. from_layer = net.keys()[-1] # TODO(weiliu89): Construct the name using the last layer to avoid duplication. out_layer = "conv6_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1) from_layer = out_layer out_layer = "conv6_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2) for i in xrange(7, 9): from_layer = out_layer out_layer = "conv{}_1".format(i) ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1) from_layer = out_layer out_layer = "conv{}_2".format(i) ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 2) # Add global pooling layer. name = net.keys()[-1] net.pool6 = L.Pooling(net[name], pool=P.Pooling.AVE, global_pooling=True) return net ### Modify the following parameters accordingly ### # The directory which contains the caffe code. # We assume you are running the script at the CAFFE_ROOT. caffe_root = os.getcwd() # Set true if you want to start training right after generating all files. run_soon = True # Set true if you want to load from most recently saved snapshot. # Otherwise, we will load from the pretrain_model defined below. resume_training = True # If true, Remove old model files. remove_old_models = False # The database file for training data. Created by data/VOC0712/create_data.sh train_data = "examples/indoor/indoor_trainval_lmdb" # The database file for testing data. Created by data/VOC0712/create_data.sh test_data = "examples/indoor/indoor_test_lmdb" # Specify the batch sampler. resize_width = 300 resize_height = 300 resize = "{}x{}".format(resize_width, resize_height) batch_sampler = [ { ‘sampler‘: { }, ‘max_trials‘: 1, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘min_jaccard_overlap‘: 0.1, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘min_jaccard_overlap‘: 0.3, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘min_jaccard_overlap‘: 0.5, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘min_jaccard_overlap‘: 0.7, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘min_jaccard_overlap‘: 0.9, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, { ‘sampler‘: { ‘min_scale‘: 0.3, ‘max_scale‘: 1.0, ‘min_aspect_ratio‘: 0.5, ‘max_aspect_ratio‘: 2.0, }, ‘sample_constraint‘: { ‘max_jaccard_overlap‘: 1.0, }, ‘max_trials‘: 50, ‘max_sample‘: 1, }, ] train_transform_param = { ‘mirror‘: True, ‘mean_value‘: [104, 117, 123], ‘resize_param‘: { ‘prob‘: 1, ‘resize_mode‘: P.Resize.WARP, ‘height‘: resize_height, ‘width‘: resize_width, ‘interp_mode‘: [ P.Resize.LINEAR, P.Resize.AREA, P.Resize.NEAREST, P.Resize.CUBIC, P.Resize.LANCZOS4, ], }, ‘emit_constraint‘: { ‘emit_type‘: caffe_pb2.EmitConstraint.CENTER, } } test_transform_param = { ‘mean_value‘: [104, 117, 123], ‘resize_param‘: { ‘prob‘: 1, ‘resize_mode‘: P.Resize.WARP, ‘height‘: resize_height, ‘width‘: resize_width, ‘interp_mode‘: [P.Resize.LINEAR], }, } # If true, use batch norm for all newly added layers. # Currently only the non batch norm version has been tested. use_batchnorm = False # Use different initial learning rate. if use_batchnorm: base_lr = 0.0004 else: # A learning rate for batch_size = 1, num_gpus = 1. base_lr = 0.00004 # Modify the job name if you want. job_name = "SSD_{}".format(resize) # The name of the model. Modify it if you want. model_name = "VGG_VOC0712_{}".format(job_name) # Directory which stores the model .prototxt file. save_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the snapshot of models. snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the job script and log file. job_dir = "jobs/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the detection results. output_result_dir = "{}/data/VOCdevkit/results/VOC2007/{}/Main".format(os.environ[‘HOME‘], job_name) # model definition files. train_net_file = "{}/train.prototxt".format(save_dir) test_net_file = "{}/test.prototxt".format(save_dir) deploy_net_file = "{}/deploy.prototxt".format(save_dir) solver_file = "{}/solver.prototxt".format(save_dir) # snapshot prefix. snapshot_prefix = "{}/{}".format(snapshot_dir, model_name) # job script path. job_file = "{}/{}.sh".format(job_dir, model_name) # Stores the test image names and sizes. Created by data/VOC0712/create_list.sh name_size_file = "data/indoor/test_name_size.txt" # The pretrained model. We use the Fully convolutional reduced (atrous) VGGNet. pretrain_model = "models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" # Stores LabelMapItem. label_map_file = "data/indoor/labelmap_indoor.prototxt" # MultiBoxLoss parameters. num_classes = 2 share_location = True background_label_id=0 train_on_diff_gt = True normalization_mode = P.Loss.VALID code_type = P.PriorBox.CENTER_SIZE neg_pos_ratio = 3. loc_weight = (neg_pos_ratio + 1.) / 4. multibox_loss_param = { ‘loc_loss_type‘: P.MultiBoxLoss.SMOOTH_L1, ‘conf_loss_type‘: P.MultiBoxLoss.SOFTMAX, ‘loc_weight‘: loc_weight, ‘num_classes‘: num_classes, ‘share_location‘: share_location, ‘match_type‘: P.MultiBoxLoss.PER_PREDICTION, ‘overlap_threshold‘: 0.5, ‘use_prior_for_matching‘: True, ‘background_label_id‘: background_label_id, ‘use_difficult_gt‘: train_on_diff_gt, ‘do_neg_mining‘: True, ‘neg_pos_ratio‘: neg_pos_ratio, ‘neg_overlap‘: 0.5, ‘code_type‘: code_type, } loss_param = { ‘normalization‘: normalization_mode, } # parameters for generating priors. # minimum dimension of input image min_dim = 300 # conv4_3 ==> 38 x 38 # fc7 ==> 19 x 19 # conv6_2 ==> 10 x 10 # conv7_2 ==> 5 x 5 # conv8_2 ==> 3 x 3 # pool6 ==> 1 x 1 mbox_source_layers = [‘conv4_3‘, ‘fc7‘, ‘conv6_2‘, ‘conv7_2‘, ‘conv8_2‘, ‘pool6‘] # in percent % min_ratio = 20 max_ratio = 95 step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2))) min_sizes = [] max_sizes = [] for ratio in xrange(min_ratio, max_ratio + 1, step): min_sizes.append(min_dim * ratio / 100.) max_sizes.append(min_dim * (ratio + step) / 100.) min_sizes = [min_dim * 10 / 100.] + min_sizes max_sizes = [[]] + max_sizes aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]] # L2 normalize conv4_3. normalizations = [20, -1, -1, -1, -1, -1] # variance used to encode/decode prior bboxes. if code_type == P.PriorBox.CENTER_SIZE: prior_variance = [0.1, 0.1, 0.2, 0.2] else: prior_variance = [0.1] flip = True clip = True # Solver parameters. # Defining which GPUs to use. gpus = "0" gpulist = gpus.split(",") num_gpus = len(gpulist) # Divide the mini-batch to different GPUs. batch_size = 4 accum_batch_size = 32 iter_size = accum_batch_size / batch_size solver_mode = P.Solver.CPU device_id = 0 batch_size_per_device = batch_size if num_gpus > 0: batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus)) iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus))) solver_mode = P.Solver.GPU device_id = int(gpulist[0]) if normalization_mode == P.Loss.NONE: base_lr /= batch_size_per_device elif normalization_mode == P.Loss.VALID: base_lr *= 25. / loc_weight elif normalization_mode == P.Loss.FULL: # Roughly there are 2000 prior bboxes per image. # TODO(weiliu89): Estimate the exact # of priors. base_lr *= 2000. # Which layers to freeze (no backward) during training. freeze_layers = [‘conv1_1‘, ‘conv1_2‘, ‘conv2_1‘, ‘conv2_2‘] # Evaluate on whole test set. num_test_image = 800 test_batch_size = 1 test_iter = num_test_image / test_batch_size solver_param = { # Train parameters ‘base_lr‘: base_lr, ‘weight_decay‘: 0.0005, ‘lr_policy‘: "step", ‘stepsize‘: 40000, ‘gamma‘: 0.1, ‘momentum‘: 0.9, ‘iter_size‘: iter_size, ‘max_iter‘: 60000, ‘snapshot‘: 40000, ‘display‘: 10, ‘average_loss‘: 10, ‘type‘: "SGD", ‘solver_mode‘: solver_mode, ‘device_id‘: device_id, ‘debug_info‘: False, ‘snapshot_after_train‘: True, # Test parameters ‘test_iter‘: [test_iter], ‘test_interval‘: 10000, ‘eval_type‘: "detection", ‘ap_version‘: "11point", ‘test_initialization‘: False, } # parameters for generating detection output. det_out_param = { ‘num_classes‘: num_classes, ‘share_location‘: share_location, ‘background_label_id‘: background_label_id, ‘nms_param‘: {‘nms_threshold‘: 0.45, ‘top_k‘: 400}, ‘save_output_param‘: { ‘output_directory‘: output_result_dir, ‘output_name_prefix‘: "comp4_det_test_", ‘output_format‘: "VOC", ‘label_map_file‘: label_map_file, ‘name_size_file‘: name_size_file, ‘num_test_image‘: num_test_image, }, ‘keep_top_k‘: 200, ‘confidence_threshold‘: 0.01, ‘code_type‘: code_type, } # parameters for evaluating detection results. det_eval_param = { ‘num_classes‘: num_classes, ‘background_label_id‘: background_label_id, ‘overlap_threshold‘: 0.5, ‘evaluate_difficult_gt‘: False, ‘name_size_file‘: name_size_file, } ### Hopefully you don‘t need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir) # Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device, train=True, output_label=True, label_map_file=label_map_file, transform_param=train_transform_param, batch_sampler=batch_sampler) VGGNetBody(net, from_layer=‘data‘, fully_conv=True, reduced=True, dilated=True, dropout=False, freeze_layers=freeze_layers) AddExtraLayers(net, use_batchnorm) mbox_layers = CreateMultiBoxHead(net, data_layer=‘data‘, from_layers=mbox_source_layers, use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes, aspect_ratios=aspect_ratios, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1) # Create the MultiBoxLossLayer. name = "mbox_loss" mbox_layers.append(net.label) net[name] = L.MultiBoxLoss(*mbox_layers, multibox_loss_param=multibox_loss_param, loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value(‘TRAIN‘)), propagate_down=[True, True, False, False]) with open(train_net_file, ‘w‘) as f: print(‘name: "{}_train"‘.format(model_name), file=f) print(net.to_proto(), file=f) shutil.copy(train_net_file, job_dir) # Create test net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size, train=False, output_label=True, label_map_file=label_map_file, transform_param=test_transform_param) VGGNetBody(net, from_layer=‘data‘, fully_conv=True, reduced=True, dilated=True, dropout=False, freeze_layers=freeze_layers) AddExtraLayers(net, use_batchnorm) mbox_layers = CreateMultiBoxHead(net, data_layer=‘data‘, from_layers=mbox_source_layers, use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes, aspect_ratios=aspect_ratios, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1) conf_name = "mbox_conf" if multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.SOFTMAX: reshape_name = "{}_reshape".format(conf_name) net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, num_classes])) softmax_name = "{}_softmax".format(conf_name) net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "{}_flatten".format(conf_name) net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_layers[1] = net[flatten_name] elif multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "{}_sigmoid".format(conf_name) net[sigmoid_name] = L.Sigmoid(net[conf_name]) mbox_layers[1] = net[sigmoid_name] net.detection_out = L.DetectionOutput(*mbox_layers, detection_output_param=det_out_param, include=dict(phase=caffe_pb2.Phase.Value(‘TEST‘))) net.detection_eval = L.DetectionEvaluate(net.detection_out, net.label, detection_evaluate_param=det_eval_param, include=dict(phase=caffe_pb2.Phase.Value(‘TEST‘))) with open(test_net_file, ‘w‘) as f: print(‘name: "{}_test"‘.format(model_name), file=f) print(net.to_proto(), file=f) shutil.copy(test_net_file, job_dir) # Create deploy net. # Remove the first and last layer from test net. deploy_net = net with open(deploy_net_file, ‘w‘) as f: net_param = deploy_net.to_proto() # Remove the first (AnnotatedData) and last (DetectionEvaluate) layer from test net. del net_param.layer[0] del net_param.layer[-1] net_param.name = ‘{}_deploy‘.format(model_name) net_param.input.extend([‘data‘]) net_param.input_shape.extend([ caffe_pb2.BlobShape(dim=[1, 3, resize_height, resize_width])]) print(net_param, file=f) shutil.copy(deploy_net_file, job_dir) # Create solver. solver = caffe_pb2.SolverParameter( train_net=train_net_file, test_net=[test_net_file], snapshot_prefix=snapshot_prefix, **solver_param) with open(solver_file, ‘w‘) as f: print(solver, file=f) shutil.copy(solver_file, job_dir) max_iter = 0 # Find most recent snapshot. for file in os.listdir(snapshot_dir): if file.endswith(".solverstate"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if iter > max_iter: max_iter = iter train_src_param = ‘--weights="{}" \\\n‘.format(pretrain_model) if resume_training: if max_iter > 0: train_src_param = ‘--snapshot="{}_iter_{}.solverstate" \\\n‘.format(snapshot_prefix, max_iter) if remove_old_models: # Remove any snapshots smaller than max_iter. for file in os.listdir(snapshot_dir): if file.endswith(".solverstate"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if max_iter > iter: os.remove("{}/{}".format(snapshot_dir, file)) if file.endswith(".caffemodel"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if max_iter > iter: os.remove("{}/{}".format(snapshot_dir, file)) # Create job file. with open(job_file, ‘w‘) as f: f.write(‘cd {}\n‘.format(caffe_root)) f.write(‘./build/tools/caffe train \\\n‘) f.write(‘--solver="{}" \\\n‘.format(solver_file)) f.write(train_src_param) if solver_param[‘solver_mode‘] == P.Solver.GPU: f.write(‘--gpu {} 2>&1 | tee {}/{}.log\n‘.format(gpus, job_dir, model_name)) else: f.write(‘2>&1 | tee {}/{}.log\n‘.format(job_dir, model_name)) # Copy the python script to job_dir. py_file = os.path.abspath(__file__) shutil.copy(py_file, job_dir) # Run the job. os.chmod(job_file, stat.S_IRWXU) if run_soon: subprocess.call(job_file, shell=True)
ssd_pascal_indoor.py
训练命令:python examples/ssd/ssd_pascal_indoor.py
未完待续......
时间: 2024-11-08 22:47:50