前言
网上现有的教程几乎全都只是翻译或者直接使用VOC数据集。
我的数据集是从ILSVRC、ImageNet拿来的,颜色通道不统一,xml文件内容格式不统一。
整个过程遇到了大量问题,也写了很多脚本工具。
现在我一一记录下来,造福人类!
一、挑选数据集
我先是从ImageNet官网下载了所有关于杯子的图片
然后从ILSVRC2011,ILSVRC2012,ILSVRC2013和ILSVRC2015数据集通过搜索xml中杯子的代号挑出了包含杯子的数据集。
脚本工具参考:http://blog.csdn.net/renhanchi/article/details/71480835
二、处理xml文件
我只需要杯子的信息,其他物体信息要从xml文件中删掉。否则生成lmdb文件的时候会出现错误,提示“Unknown name: xxxxxxxx”。xxxx就是除了杯子以外的物体的代号。
尝试了很多方法,不多说,看下面具体步骤:
1.将Annotations文件夹改名为:Annos
2.新建一个空文件夹名字为:Annotations
3.修改下面名字为“delete_by_name.py”的python工具代码,只需要修改if not后面内容。引号内为你要保留的数据的代号。
4.运行python工具。
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Tue Oct 31 10:03:03 2017 @author: hans http://blog.csdn.net/renhanchi """ import os import xml.etree.ElementTree as ET origin_ann_dir = 'Annos/' new_ann_dir = 'Annotations/' for dirpaths, dirnames, filenames in os.walk(origin_ann_dir): for filename in filenames: if os.path.isfile(r'%s%s' %(origin_ann_dir, filename)): origin_ann_path = os.path.join(r'%s%s' %(origin_ann_dir, filename)) new_ann_path = os.path.join(r'%s%s' %(new_ann_dir, filename)) tree = ET.parse(origin_ann_path) root = tree.getroot() for object in root.findall('object'): name = str(object.find('name').text) if not (name == "n03147509" or \ name == "n03216710" or \ name == "n03438257" or \ name == "n03797390" or \ name == "n04559910" or \ name == "n07930864"): root.remove(object) tree.write(new_ann_path)
三、生成训练集和验证集txt文件
先新建一个名字为doc的文件夹
下面名字为“cup_list.sh”代码并不是我最终使用的,你们根据自己情况做适当修改。
#!/bin/sh classes=(JPEGImages Annotations) root_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) for dataset in train val do if [ $dataset == "train" ] then data_dir=(ILSVRC2015_train ILSVRC2015_val ILSVRC_train ImageNet) fi if [ $dataset == "val" ] then data_dir=(ILSVRC_val) fi for cla in ${data_dir[@]} do for class in ${classes[@]} do find ./$cla/$class/ -name "*.jpg" >> ${class}_${dataset}.txt done
for class in ${classes[@]} do find ./$cla/$class/ -name "*.jpg" >> ${class}_${dataset}.txt donedone paste -d' ' JPEGImages_${dataset}.txt Annotations_${dataset}.txt >> temp_${dataset}.txt cat temp_${dataset}.txt | awk 'BEGIN{srand()}{print rand()"\t"$0}' | sort -k1,1 -n | cut -f2- > $dataset.txt if [ $dataset == "val" ] then /home/hans/caffe-ssd/build/tools/get_image_size $root_dir $dataset.txt $dataset"_name_size.txt" fi rm temp_${dataset}.txt rm JPEGImages_${dataset}.txt rm Annotations_${dataset}.txtdonemv train.txt doc/mv val.txt doc/mv val_name_size.txt doc/
四、写labelmap_cup.prototxt
这个文件放到doc目录下。
有几个问题需要注意。
1.label 0 必须是background
2.虽然我只检测杯子,但是xml文件中杯子name的代码有好几个。
我一开始将所有label都设置为1,后来生成lmdb文件的时候报错。
我只能乖乖的按顺序写下去,不过问题不大。反正知道1到6都是杯子就好。
五、生成lmdb文件
这先是出现了上面提到的Unknown name错误,通过修改xml解决了。
后来又出现调用caffe模块的Symbol错误,反正你们跟我走就好,错不了。
先修改一个文件caffe-ssd/scripts/create_annoset.py
然后运行cup_data.sh
cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) root_dir=/home/hans/caffe-ssd redo=1 data_root_dir="${cur_dir}" dataset_name="doc" mapfile="${cur_dir}/doc/labelmap_cup.prototxt" anno_type="detection" db="lmdb" min_dim=0 max_dim=0 width=0 height=0 extra_cmd="--encode-type=JPEG --encoded" if [ $redo ] then extra_cmd="$extra_cmd --redo" fi for subset in train val 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 \ $cur_dir/$dataset_name/$subset.txt $data_root_dir/$dataset_name/$subset"_"$db ln/ done rm -rf ln/
六、训练
先去下载预训练模型放到doc目录下。
下载地址: cs.unc.edu/~wliu/projects/ParseNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel
修改训练代码真是一件熬心熬力的事儿,路径太多,问题也不少。还好github issues上作业挺给力。
先放出我的ssd_pascal.py代码:
from __future__ import print_function import sys sys.path.append("/home/hans/caffe-ssd/python") #####改 import caffe from caffe.model_libs import * from google.protobuf import text_format import math import os import shutil import stat import subprocess # Add extra layers on top of a "base" network (e.g. VGGNet or Inception). def AddExtraLayers(net, use_batchnorm=True, lr_mult=1): use_relu = True # Add additional convolutional layers. # 19 x 19 from_layer = net.keys()[-1] # TODO(weiliu89): Construct the name using the last layer to avoid duplication. # 10 x 10 out_layer = "conv6_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1, lr_mult=lr_mult) from_layer = out_layer out_layer = "conv6_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2, lr_mult=lr_mult) # 5 x 5 from_layer = out_layer out_layer = "conv7_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1, lr_mult=lr_mult) from_layer = out_layer out_layer = "conv7_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 2, lr_mult=lr_mult) # 3 x 3 from_layer = out_layer out_layer = "conv8_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1, lr_mult=lr_mult) from_layer = out_layer out_layer = "conv8_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 0, 1, lr_mult=lr_mult) # 1 x 1 from_layer = out_layer out_layer = "conv9_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1, lr_mult=lr_mult) from_layer = out_layer out_layer = "conv9_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 0, 1, lr_mult=lr_mult) 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 = "/home/hans/caffe-ssd" #####改 # 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 = "/home/hans/data/ImageNet/Detection/cup/doc/train_lmdb" #########改 # The database file for testing data. Created by data/VOC0712/create_data.sh test_data = "/home/hans/data/ImageNet/Detection/cup/doc/val_lmdb" ########改 # 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], 'force_color': True, ####改 '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, ], }, 'distort_param': { 'brightness_prob': 0.5, 'brightness_delta': 32, 'contrast_prob': 0.5, 'contrast_lower': 0.5, 'contrast_upper': 1.5, 'hue_prob': 0.5, 'hue_delta': 18, 'saturation_prob': 0.5, 'saturation_lower': 0.5, 'saturation_upper': 1.5, 'random_order_prob': 0.0, }, 'expand_param': { 'prob': 0.5, 'max_expand_ratio': 4.0, }, 'emit_constraint': { 'emit_type': caffe_pb2.EmitConstraint.CENTER, } } test_transform_param = { 'mean_value': [104, 117, 123], 'force_color': True, ####改 '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 lr_mult = 1 # 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 root = "/home/hans/data/ImageNet/Detection/cup" ####改 # Modify the job name if you want. job_name = "SSD_{}".format(resize) ####改 # The name of the model. Modify it if you want. model_name = "VGG_CUP_{}".format(job_name) ####改 # Directory which stores the model .prototxt file. save_dir = "{}/doc/{}".format(root, job_name) ####改 # Directory which stores the snapshot of models. snapshot_dir = "{}/models/{}".format(root, job_name) ####改 # Directory which stores the job script and log file. job_dir = "{}/jobs/{}".format(root, job_name) ####改 # Directory which stores the detection results. output_result_dir = "{}/results/{}".format(root, job_name) ####改 # 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 = "{}/doc/val_name_size.txt".format(root) ####改 # The pretrained model. We use the Fully convolutional reduced (atrous) VGGNet. pretrain_model = "{}/doc/VGG_ILSVRC_16_layers_fc_reduced.caffemodel".format(root) ####改 # Stores LabelMapItem. label_map_file = "{}/doc/labelmap_cup.prototxt".format(root) ####改 # MultiBoxLoss parameters. num_classes = 7 ####改 share_location = True background_label_id=0 train_on_diff_gt = True normalization_mode = P.Loss.VALID code_type = P.PriorBox.CENTER_SIZE ignore_cross_boundary_bbox = False mining_type = P.MultiBoxLoss.MAX_NEGATIVE 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, 'mining_type': mining_type, 'neg_pos_ratio': neg_pos_ratio, 'neg_overlap': 0.5, 'code_type': code_type, 'ignore_cross_boundary_bbox': ignore_cross_boundary_bbox, } 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 # conv9_2 ==> 1 x 1 mbox_source_layers = ['conv4_3', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2'] # in percent % min_ratio = 20 max_ratio = 90 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 = [min_dim * 20 / 100.] + max_sizes steps = [8, 16, 32, 64, 100, 300] aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]] # 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 = False # Solver parameters. # Defining which GPUs to use. gpus = "7" ####改 gpulist = gpus.split(",") num_gpus = len(gpulist) # Divide the mini-batch to different GPUs. batch_size = 32 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. # Evaluate on whole test set. num_test_image = 2000 ####改 test_batch_size = 8 # Ideally test_batch_size should be divisible by num_test_image, # otherwise mAP will be slightly off the true value. test_iter = int(math.ceil(float(num_test_image) / test_batch_size)) solver_param = { # Train parameters 'base_lr': base_lr, 'weight_decay': 0.0005, 'lr_policy': "multistep", 'stepvalue': [80000, 100000, 120000], 'gamma': 0.1, 'momentum': 0.9, 'iter_size': iter_size, 'max_iter': 120000, 'snapshot': 80000, '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': 100, 'eval_type': "detection", 'ap_version': "11point", 'test_initialization': True, } # 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) AddExtraLayers(net, use_batchnorm, lr_mult=lr_mult) 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, steps=steps, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1, lr_mult=lr_mult) # 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) AddExtraLayers(net, use_batchnorm, lr_mult=lr_mult) 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, steps=steps, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1, lr_mult=lr_mult) 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)
上面237行到267行你就慢慢搞吧。当然如果你按照我的文件夹布局来的话,只需要修改237行。
修改179行,是因为训练阶段出现“”OpenCV Error: Assertion failed ((scn == 3 || scn == 4) && (depth == CV_8U ||............" 这个错误。
修改216行,是因为验证阶段出现“Check failed:std::equal(top_shape.begin()+1,top_shape.begin()+4,shape.begin()+1)”这个错误。
修改270行,你的类别数+1。注意这个类别数是labelmap_cup.prototxt中最大索引+1。
修改363行,为你的测试集图片数量。
其他要修改的看上面代码吧。我都标记好了。
其余的参数调节就自己看代码改吧,也不难。
最后运行开始训练。
七、训练输出可视化(2017.11.02)
拿之前给caffe用的改了改。
有一个变动就是增加了一个 倍数time 的变量,因为有时候输出波动太大,按一定倍数取平均会让曲线平滑一点。
第一个参数是log文件路径。
需要修改代码中display和test_iterval的数值个solver.prototxt中一致。
time是倍数,想看原始数据曲线的话就设置为1。
代码:
#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Nov 2 14:35:42 2017 @author: hans http://blog.csdn.net/renhanchi """ import matplotlib.pyplot as plt import numpy as np import commands import argparse parser = argparse.ArgumentParser() parser.add_argument( '-p','--log_path', type = str, default = '', help = """\ path to log file\ """ ) FLAGS = parser.parse_args() train_log_file = FLAGS.log_path display = 10 #solver test_interval = 100 #solver time = 5 train_output = commands.getoutput("cat " + train_log_file + " | grep 'Train net output #0' | awk '{print $11}'") #train mbox_loss accu_output = commands.getoutput("cat " + train_log_file + " | grep 'Test net output #0' | awk '{print $11}'") #test detection_eval train_loss = train_output.split("\n") test_accu = accu_output.split("\n") def reduce_data(data): iteration = len(data)/time*time _data = data[0:iteration] if time > 1: data_ = [] for i in np.arange(len(data)/time): sum_data = 0 for j in np.arange(time): index = i*time + j sum_data += float(_data[index]) data_.append(sum_data/float(time)) else: data_ = data return data_ train_loss_ = reduce_data(train_loss) test_accu_ = reduce_data(test_accu) _,ax1 = plt.subplots() ax2 = ax1.twinx() ax1.plot(time*display*np.arange(len(train_loss_)), train_loss_) ax2.plot(time*test_interval*np.arange(len(test_accu_)), test_accu_, 'r') ax1.set_xlabel('Iteration') ax1.set_ylabel('Train Loss') ax2.set_ylabel('Test Accuracy') plt.show()
八、测试模型效果(2017.11.03)
模型训练好要看最终效果如何。
原作者给了一个python工具,我觉得不好用。你们可以自己看看,名字是“ssd_pascal_webcam.py”
下面我介绍一下自己手动做检测的步骤:
先准备好三个文件,deploy.prototxt,labelmap_cup.prototxt,xxxxx.caffemodel
修改deploy.prototxt文件的第一层和最后一层:
name: "VGG_VOC0712_SSD_300x300_test" layer { name: "data" type: "VideoData" top: "data" transform_param { mean_value: 104.0 mean_value: 117.0 mean_value: 123.0 resize_param { prob: 1.0 resize_mode: WARP height: 300 width: 300 interp_mode: LINEAR } } data_param { batch_size: 1 } video_data_param { video_type: WEBCAM device_id: 0 ####摄像头编号 skip_frames: 0 ####是否跳帧 } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" ... ... ... ... ... ... layer { name: "mbox_conf_flatten" type: "Flatten" bottom: "mbox_conf_softmax" top: "mbox_conf_flatten" flatten_param { axis: 1 } } layer { name: "detection_out" type: "DetectionOutput" bottom: "mbox_loc" bottom: "mbox_conf_flatten" bottom: "mbox_priorbox" bottom: "data" top: "detection_out" include { phase: TEST } transform_param { mean_value: 104.0 mean_value: 117.0 mean_value: 123.0 resize_param { prob: 1.0 resize_mode: WARP height: 480 ####摄像头高宽,可以设置大点,会放大显示 width: 640 interp_mode: LINEAR } } detection_output_param { num_classes: 7 ####类别数 + 1 share_location: true background_label_id: 0 nms_param { nms_threshold: 0.449999988079 top_k: 400 } save_output_param { label_map_file: "labelmap_cup.prototxt" #####改 } code_type: CENTER_SIZE keep_top_k: 200 confidence_threshold: 0.899999976158 visualize: true visualize_threshold: 0.600000023842 ###只显示置信度高于这个值的结果 } } layer { name: "slience" type: "Silence" bottom: "detection_out" include { phase: TEST } }
下面是测试用的脚本内容:
/home/hans/caffe-ssd/build/tools/caffe test \ --model="deploy.prototxt" \ --weights="xxxxx.caffemodel" \ --iterations="536870911" \ --gpu 0
iteration是int类型最大值。
标准杯子还是很稳定的,有时候会把柱状物检测出来。
现在这个模型还不是最终的,在我自己的验证集上detection_eval在0.72左右。
后记
这篇博客我也会持续更新。包括输出结果分析,可视化,更换网络模型等等。
这次用的是VGGnet,后面我还会用到mobileNet。
有一个问题就是均值计算,我还没测试用caffe自带的creat_mean.sh好用不好用。
----【2017.11.20 解决均值问题】--------------------------------------
自带make_mean.sh并不能求均值,发现有两个转lmdb工具,一个带annotation,一个不带。ssd用的带annotation的转换工具。
更具体内容请参考末尾:http://blog.csdn.net/renhanchi/article/details/78423343
----【2017.11.2更新】-----多GPU----------------------------------------
这个框架好像可以直接用多GPU运行的,没验证。
我服务器上已经安装了nccl,但是在make的时候告诉我都已经编译好了。
我没多管直接3个GPU上去试试,可行!不过报错centos kernel: BUG: soft lockup - CPU#3 stuck for 23s! [kworker/3:0:14900]
吓尿!我另一块GPU在跑数据。
后来用两块GPU跑,0次迭代正常,第一次迭代loss就nan了,改了几次参数无果。
还是乖乖的用一块卡跑吧~~
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