5.3.3 损失函数
飞桨已经将loss封装好,直接计算损失函数,过程更简洁,速度也更快,推荐大家直接使用飞桨paddle.vision.ops.yolo_lossAPI,关键参数说明如下:
paddle.vision.ops.yolo_loss(x, gt_box, gt_label, anchors, anchor_mask, class_num, ignore_thresh, downsample_ratio, gt_score=None, use_label_smooth=True, name=None, scale_x_y=1.0)
- x: 输出特征图。
- gt_box: 真实框。
- gt_label: 真实框标签。
- ignore_thresh,预测框与真实框IoU阈值超过ignore_thresh时,不作为负样本,YOLOv3模型里设置为0.7。
- downsample_ratio,特征图P0的下采样比例,使用Darknet53骨干网络时为32。
- gt_score,真实框的置信度,在使用了mixup技巧时用到。
- use_label_smooth,一种训练技巧,如不使用,设置为False。
- name,该层的名字,比如'yolov3_loss',默认值为None,一般无需设置。
YOLOv3的模型代码如下所示:
def get_loss(num_classes, outputs, gtbox, gtlabel, gtscore=None,
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326],
anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]],
ignore_thresh=0.7,
use_label_smooth=False):
"""
使用paddle.vision.ops.yolo_loss
"""
losses = []
downsample = 32
for i, out in enumerate(outputs): # 对三个层级分别求损失函数
anchor_mask_i = anchor_masks[i]
loss = paddle.vision.ops.yolo_loss(
x=out, # out是P0, P1, P2中的一个
gt_box=gtbox, # 真实框坐标
gt_label=gtlabel, # 真实框类别
gt_score=gtscore, # 真实框得分,使用mixup训练技巧时需要,不使用该技巧时直接设置为1,形状与gtlabel相同
anchors=anchors, # 锚框尺寸,包含[w0, h0, w1, h1, ..., w8, h8]共9个锚框的尺寸
anchor_mask=anchor_mask_i, # 筛选锚框的mask,例如anchor_mask_i=[3, 4, 5],将anchors中第3、4、5个锚框挑选出来给该层级使用
class_num=num_classes, # 分类类别数
ignore_thresh=ignore_thresh, # 当预测框与真实框IoU > ignore_thresh,标注objectness = -1
downsample_ratio=downsample, # 特征图相对于原图缩小的倍数,例如P0是32, P1是16,P2是8
use_label_smooth=False) # 使用label_smooth训练技巧时会用到,这里没用此技巧,直接设置为False
losses.append(paddle.mean(loss)) #mean对每张图片求和
downsample = downsample // 2 # 下一级特征图的缩放倍数会减半
return sum(losses) # 对每个层级求和
5.3.4 模型训练
训练过程如图3所示,输入图片经过特征提取得到三个层级的输出特征图P0(stride=32)、P1(stride=16)和P2(stride=8),相应的分别使用不同大小的小方块区域去生成对应的锚框和预测框,并对这些锚框进行标注。
- P0层级特征图,对应着使用 32 × 32 32\times32 大小的小方块,在每个区域中心生成大小分别为 [ 116 , 90 ] [116, 90] , [ 156 , 198 ] [156, 198] , [ 373 , 326 ] [373, 326] 的三种锚框。
- P1层级特征图,对应着使用 16 × 16 16\times16 大小的小方块,在每个区域中心生成大小分别为 [ 30 , 61 ] [30, 61] , [ 62 , 45 ] [62, 45] , [ 59 , 119 ] [59, 119] 的三种锚框。
- P2层级特征图,对应着使用 8 × 8 8\times8 大小的小方块,在每个区域中心生成大小分别为 [ 10 , 13 ] [10, 13] , [ 16 , 30 ] [16, 30] , [ 33 , 23 ] [33, 23] 的三种锚框。
将三个层级的特征图与对应锚框之间的标签关联起来,并建立损失函数,总的损失函数等于三个层级的损失函数相加。通过极小化损失函数,可以开启端到端的训练过程。
图3:端到端训练流程
训练过程的具体实现代码如下,训练时长原因,epoch先设置为1,如果训练更优模型,调大该值。
In [22]
############# 这段代码在本地机器上运行请慎重,容易造成死机,我们可以使用较小的MAX_EPOCH运行#######################
import time
import os
import paddle
def get_lr(base_lr = 0.0001, lr_decay = 0.1):
bd = [10000, 20000]
lr = [base_lr, base_lr * lr_decay, base_lr * lr_decay * lr_decay]
learning_rate = paddle.optimizer.lr.PiecewiseDecay(boundaries=bd, values=lr)
return learning_rate
# MAX_EPOCH = 200
MAX_EPOCH = 1
ANCHORS = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
ANCHOR_MASKS = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
IGNORE_THRESH = .7
NUM_CLASSES = 7
TRAINDIR = '/home/aistudio/work/insects/train'
TESTDIR = '/home/aistudio/work/insects/test'
VALIDDIR = '/home/aistudio/work/insects/val'
paddle.set_device("gpu:0")
# 创建数据读取类
train_dataset = TrainDataset(TRAINDIR, mode='train')
valid_dataset = TrainDataset(VALIDDIR, mode='valid')
test_dataset = TrainDataset(VALIDDIR, mode='valid')
# 使用paddle.io.DataLoader创建数据读取器,并设置batchsize,进程数量num_workers等参数
train_loader = paddle.io.DataLoader(train_dataset, batch_size=10, shuffle=True, num_workers=0, drop_last=True, use_shared_memory=False)
valid_loader = paddle.io.DataLoader(valid_dataset, batch_size=10, shuffle=False, num_workers=0, drop_last=False, use_shared_memory=False)
model = YOLOv3(num_classes = NUM_CLASSES) #创建模型
learning_rate = get_lr()
opt = paddle.optimizer.Momentum(
learning_rate=learning_rate,
momentum=0.9,
weight_decay=paddle.regularizer.L2Decay(0.0005),
parameters=model.parameters()) #创建优化器
# opt = paddle.optimizer.Adam(learning_rate=learning_rate, weight_decay=paddle.regularizer.L2Decay(0.0005), parameters=model.parameters())
if __name__ == '__main__':
for epoch in range(MAX_EPOCH):
for i, data in enumerate(train_loader()):
img, gt_boxes, gt_labels, img_scale = data
gt_scores = np.ones(gt_labels.shape).astype('float32')
gt_scores = paddle.to_tensor(gt_scores)
img = paddle.to_tensor(img)
gt_boxes = paddle.to_tensor(gt_boxes)
gt_labels = paddle.to_tensor(gt_labels)
outputs = model(img) #前向传播,输出[P0, P1, P2]
loss = get_loss(NUM_CLASSES, outputs, gt_boxes, gt_labels, gtscore=gt_scores,
anchors = ANCHORS,
anchor_masks = ANCHOR_MASKS,
ignore_thresh=IGNORE_THRESH,
use_label_smooth=False) # 计算损失函数
loss.backward() # 反向传播计算梯度
opt.step() # 更新参数
opt.clear_grad()
if i % 10 == 0:
timestring = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time()))
print('{}[TRAIN]epoch {}, iter {}, output loss: {}'.format(timestring, epoch, i, loss.numpy()))
# save params of model
if (epoch % 5 == 0) or (epoch == MAX_EPOCH -1):
paddle.save(model.state_dict(), 'yolo_epoch{}'.format(epoch))
# 每个epoch结束之后在验证集上进行测试
model.eval()
for i, data in enumerate(valid_loader()):
img, gt_boxes, gt_labels, img_scale = data
gt_scores = np.ones(gt_labels.shape).astype('float32')
gt_scores = paddle.to_tensor(gt_scores)
img = paddle.to_tensor(img)
gt_boxes = paddle.to_tensor(gt_boxes)
gt_labels = paddle.to_tensor(gt_labels)
outputs = model(img)
loss = get_loss(NUM_CLASSES,outputs, gt_boxes, gt_labels, gtscore=gt_scores,
anchors = ANCHORS,
anchor_masks = ANCHOR_MASKS,
ignore_thresh=IGNORE_THRESH,
use_label_smooth=False)
if i % 1 == 0:
timestring = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(time.time()))
print('{}[VALID]epoch {}, iter {}, output loss: {}'.format(timestring, epoch, i, loss.numpy()))
model.train()
5.3.5 模型评估
先定义上一节讲解的非极大值抑制,当数据集中含有多个类别的物体时,需要做多分类非极大值抑制,其实现原理与非极大值抑制相同,区别在于需要对每个类别都做非极大值抑制,实现代码如下面的multiclass_nms所示。
# 计算IoU,矩形框的坐标形式为xyxy,这个函数会被保存在box_utils.py文件中
def box_iou_xyxy(box1, box2):
# 获取box1左上角和右下角的坐标
x1min, y1min, x1max, y1max = box1[0], box1[1], box1[2], box1[3]
# 计算box1的面积
s1 = (y1max - y1min + 1.) * (x1max - x1min + 1.)
# 获取box2左上角和右下角的坐标
x2min, y2min, x2max, y2max = box2[0], box2[1], box2[2], box2[3]
# 计算box2的面积
s2 = (y2max - y2min + 1.) * (x2max - x2min + 1.)
# 计算相交矩形框的坐标
xmin = np.maximum(x1min, x2min)
ymin = np.maximum(y1min, y2min)
xmax = np.minimum(x1max, x2max)
ymax = np.minimum(y1max, y2max)
# 计算相交矩形行的高度、宽度、面积
inter_h = np.maximum(ymax - ymin + 1., 0.)
inter_w = np.maximum(xmax - xmin + 1., 0.)
intersection = inter_h * inter_w
# 计算相并面积
union = s1 + s2 - intersection
# 计算交并比
iou = intersection / union
return iou
# 非极大值抑制
def nms(bboxes, scores, score_thresh, nms_thresh, pre_nms_topk, i=0, c=0):
"""
nms
"""
inds = np.argsort(scores)
inds = inds[::-1]
keep_inds = []
while(len(inds) > 0):
cur_ind = inds[0]
cur_score = scores[cur_ind]
# if score of the box is less than score_thresh, just drop it
if cur_score < score_thresh:
break
keep = True
for ind in keep_inds:
current_box = bboxes[cur_ind]
remain_box = bboxes[ind]
iou = box_iou_xyxy(current_box, remain_box)
if iou > nms_thresh:
keep = False
break
if i == 0 and c == 4 and cur_ind == 951:
print('suppressed, ', keep, i, c, cur_ind, ind, iou)
if keep:
keep_inds.append(cur_ind)
inds = inds[1:]
return np.array(keep_inds)
# 多分类非极大值抑制
def multiclass_nms(bboxes, scores, score_thresh=0.01, nms_thresh=0.45, pre_nms_topk=1000, pos_nms_topk=100):
"""
This is for multiclass_nms
"""
batch_size = bboxes.shape[0]
class_num = scores.shape[1]
rets = []
for i in range(batch_size):
bboxes_i = bboxes[i]
scores_i = scores[i]
ret = []
for c in range(class_num):
scores_i_c = scores_i[c]
keep_inds = nms(bboxes_i, scores_i_c, score_thresh, nms_thresh, pre_nms_topk, i=i, c=c)
if len(keep_inds) < 1:
continue
keep_bboxes = bboxes_i[keep_inds]
keep_scores = scores_i_c[keep_inds]
keep_results = np.zeros([keep_scores.shape[0], 6])
keep_results[:, 0] = c
keep_results[:, 1] = keep_scores[:]
keep_results[:, 2:6] = keep_bboxes[:, :]
ret.append(keep_results)
if len(ret) < 1:
rets.append(ret)
continue
ret_i = np.concatenate(ret, axis=0)
scores_i = ret_i[:, 1]
if len(scores_i) > pos_nms_topk:
inds = np.argsort(scores_i)[::-1]
inds = inds[:pos_nms_topk]
ret_i = ret_i[inds]
rets.append(ret_i)
return rets
运行如下代码启动评估,需要指定待评估的图片文件存放路径和需要使用到的模型参数。评估结果会被保存在pred_results.json文件中。
为了演示计算过程,下面使用的是验证集下的图片"insects/val/images",在提交比赛结果的时候,请使用测试集图片"insects/test/images"
这里提供的yolo_epoch50.pdparams 是未充分训练好的权重参数,请在比赛时换成自己训练好的权重参数
import json
import paddle
ANCHORS = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
ANCHOR_MASKS = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
VALID_THRESH = 0.01
NMS_TOPK = 400
NMS_POSK = 100
NMS_THRESH = 0.45
NUM_CLASSES = 7
# 在验证集val上评估训练模型
TESTDIR = '/home/aistudio/work/insects/val/images' #请将此目录修改成用户自己保存测试图片的路径
WEIGHT_FILE = '/home/aistudio/yolo_epoch50.pdparams' # 请将此文件名修改成用户自己训练好的权重参数存放路径
# 在测试集test上评估训练模型
# TESTDIR = '/home/aistudio/work/insects/test/images'
# WEIGHT_FILE = '/home/aistudio/yolo_epoch50.pdparams'
if __name__ == '__main__':
model = YOLOv3(num_classes=NUM_CLASSES)
params_file_path = WEIGHT_FILE
model_state_dict = paddle.load(params_file_path)
model.load_dict(model_state_dict)
model.eval()
total_results = []
test_loader = test_data_loader(TESTDIR, batch_size= 1, mode='test')
for i, data in enumerate(test_loader()):
img_name, img_data, img_scale_data = data
img = paddle.to_tensor(img_data)
img_scale = paddle.to_tensor(img_scale_data)
outputs = model.forward(img)
bboxes, scores = model.get_pred(outputs,
im_shape=img_scale,
anchors=ANCHORS,
anchor_masks=ANCHOR_MASKS,
valid_thresh = VALID_THRESH)
bboxes_data = bboxes.numpy()
scores_data = scores.numpy()
result = multiclass_nms(bboxes_data, scores_data,
score_thresh=VALID_THRESH,
nms_thresh=NMS_THRESH,
pre_nms_topk=NMS_TOPK,
pos_nms_topk=NMS_POSK)
for j in range(len(result)):
result_j = result[j]
img_name_j = img_name[j]
total_results.append([img_name_j, result_j.tolist()])
print('processed {} pictures'.format(len(total_results)))
print('')
json.dump(total_results, open('pred_results.json', 'w'))
json文件中保存着测试结果,是包含所有图片预测结果的list,其构成如下:
[[img_name, [[label, score, x1, y1, x2, y2], ..., [label, score, x1, y1, x2, y2]]],
[img_name, [[label, score, x1, y1, x2, y2], ..., [label, score, x1, y1, x2, y2]]],
...
[img_name, [[label, score, x1, y1, x2, y2],..., [label, score, x1, y1, x2, y2]]]]
list中的每一个元素是一张图片的预测结果,list的总长度等于图片的数目,每张图片预测结果的格式是:
[img_name, [[label, score, x1, y1, x2, y2],..., [label, score, x1, y1, x2, y2]]]
其中第一个元素是图片名称image_name,第二个元素是包含该图片所有预测框的list, 预测框列表:
[[label, score, x1, x2, y1, y2],..., [label, score, x1, y1, x2, y2]]
预测框列表中每个元素[label, score, x1, y1, x2, y2]描述了一个预测框,label是预测框所属类别标签,score是预测框的得分;x1, y1, x2, y2对应预测框左上角坐标(x1, y1),右下角坐标(x2, y2)。每张图片可能有很多个预测框,则将其全部放在预测框列表中。
计算精度指标
通过运行如下代码计算最终精度指标mAP
同学们训练完之后,可以在val数据集上计算mAP查看结果,所以下面用到的是val标注数据"insects/val/annotations/xmls"
提交比赛成绩的话需要在测试集上计算mAP,本地没有测试集的标注,只能提交json文件到比赛服务器上查看成绩
# -*- coding: utf-8 -*-
import os
import json
import numpy as np
import xml.etree.ElementTree as ET
from map_utils import DetectionMAP
# 存放测试集标签的目录
annotations_dir ="/home/aistudio/work/insects/val/annotations/xmls"
# 存放预测结果的文件
pred_result_file = "pred_results.json"
# pred_result_file 中保存着测试结果,是包含所有图片预测结果的list,其构成如下
# [[img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]],
# [img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]],
# ...
# [img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]]]
# list中的每一个元素是一张图片的预测结果,list的总长度等于图片的数目
# 每张图片预测结果的格式是: [img_name, [[label, score, x1, x2, y1, y2],..., [label, score, x1, x2, y1, y2]]]
# 其中第一个元素是图片名称image_name,第二个元素是包含所有预测框的列表
# 预测框列表中每个元素[label, score, x1, x2, y1, y2]描述了一个预测框,
# label是预测框所属类别标签,score是预测框的得分
# x1, x2, y1, y2对应预测框左上角坐标(x1, y1),右下角坐标(x2, y2)
# 每张图片可能有很多个预测框,则将其全部放在预测框列表中
overlap_thresh = 0.5
map_type = '11point'
insect_names = ['Boerner', 'Leconte', 'Linnaeus', 'acuminatus', 'armandi', 'coleoptera', 'linnaeus']
if __name__ == '__main__':
cname2cid = {}
for i, item in enumerate(insect_names):
cname2cid[item] = i
filename = pred_result_file
results = json.load(open(filename))
num_classes = len(insect_names)
detection_map = DetectionMAP(class_num=num_classes,
overlap_thresh=overlap_thresh,
map_type=map_type,
is_bbox_normalized=False,
evaluate_difficult=False)
for result in results:
image_name = str(result[0])
bboxes = np.array(result[1]).astype('float32')
anno_file = os.path.join(annotations_dir, image_name + '.xml')
tree = ET.parse(anno_file)
objs = tree.findall('object')
im_w = float(tree.find('size').find('width').text)
im_h = float(tree.find('size').find('height').text)
gt_bbox = np.zeros((len(objs), 4), dtype=np.float32)
gt_class = np.zeros((len(objs), 1), dtype=np.int32)
difficult = np.zeros((len(objs), 1), dtype=np.int32)
for i, obj in enumerate(objs):
cname = obj.find('name').text
gt_class[i][0] = cname2cid[cname]
_difficult = int(obj.find('difficult').text)
x1 = float(obj.find('bndbox').find('xmin').text)
y1 = float(obj.find('bndbox').find('ymin').text)
x2 = float(obj.find('bndbox').find('xmax').text)
y2 = float(obj.find('bndbox').find('ymax').text)
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
gt_bbox[i] = [x1, y1, x2, y2]
difficult[i][0] = _difficult
detection_map.update(bboxes, gt_bbox, gt_class, difficult)
print("Accumulating evaluatation results...")
detection_map.accumulate()
map_stat = 100. * detection_map.get_map()
print("mAP({:.2f}, {}) = {:.2f}".format(overlap_thresh,
map_type, map_stat))
Accumulating evaluatation results...
mAP(0.50, 11point) = 71.97
5.3.6 模型预测
预测过程流程图4如下所示:
图4:预测流程
预测过程可以分为两步:
- 通过
get_pred
函数预测框位置和所属类别的得分。 - 使用非极大值抑制来消除重叠较大的预测框。
首先定义绘制预测框的画图函数,代码如下。
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.image import imread
import math
# 定义画图函数
INSECT_NAMES = ['Boerner', 'Leconte', 'Linnaeus',
'acuminatus', 'armandi', 'coleoptera', 'linnaeus']
# 定义画矩形框的函数
def draw_rectangle(currentAxis, bbox, edgecolor = 'k', facecolor = 'y', fill=False, linestyle='-'):
# currentAxis,坐标轴,通过plt.gca()获取
# bbox,边界框,包含四个数值的list, [x1, y1, x2, y2]
# edgecolor,边框线条颜色
# facecolor,填充颜色
# fill, 是否填充
# linestype,边框线型
# patches.Rectangle需要传入左上角坐标、矩形区域的宽度、高度等参数
rect=patches.Rectangle((bbox[0], bbox[1]), bbox[2]-bbox[0]+1, bbox[3]-bbox[1]+1, linewidth=1,
edgecolor=edgecolor,facecolor=facecolor,fill=fill, linestyle=linestyle)
currentAxis.add_patch(rect)
# 定义绘制预测结果的函数
def draw_results(result, filename, draw_thresh=0.5):
plt.figure(figsize=(10, 10))
im = cv2.imread(filename)
plt.imshow(im)
currentAxis=plt.gca()
colors = ['r', 'g', 'b', 'k', 'y', 'c', 'purple']
for item in result:
box = item[2:6]
label = int(item[0])
name = INSECT_NAMES[label]
if item[1] > draw_thresh:
draw_rectangle(currentAxis, box, edgecolor = colors[label])
plt.text(box[0], box[1], name, fontsize=12, color=colors[label])
使用5.3.1.4 小节定义的test_data_loader
函数读取指定的图片,输入网络并计算出预测框和得分,然后使用多分类非极大值抑制消除冗余的框。将最终结果画图展示出来。
import json
import paddle
ANCHORS = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
ANCHOR_MASKS = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
VALID_THRESH = 0.01
NMS_TOPK = 400
NMS_POSK = 100
NMS_THRESH = 0.45
NUM_CLASSES = 7
if __name__ == '__main__':
image_name = '/home/aistudio/work/insects/test/images/2642.jpeg'
params_file_path = '/home/aistudio/yolo_epoch50.pdparams'
model = YOLOv3(num_classes=NUM_CLASSES)
model_state_dict = paddle.load(params_file_path)
model.load_dict(model_state_dict)
model.eval()
total_results = []
test_loader = test_data_loader(image_name, mode='test')
for i, data in enumerate(test_loader()):
img_name, img_data, img_scale_data = data
img = paddle.to_tensor(img_data)
img_scale = paddle.to_tensor(img_scale_data)
outputs = model.forward(img)
bboxes, scores = model.get_pred(outputs,
im_shape=img_scale,
anchors=ANCHORS,
anchor_masks=ANCHOR_MASKS,
valid_thresh = VALID_THRESH)
bboxes_data = bboxes.numpy()
scores_data = scores.numpy()
results = multiclass_nms(bboxes_data, scores_data,
score_thresh=VALID_THRESH,
nms_thresh=NMS_THRESH,
pre_nms_topk=NMS_TOPK,
pos_nms_topk=NMS_POSK)
result = results[0]
draw_results(result, image_name, draw_thresh=0.4)
<Figure size 720x720 with 1 Axes>
通过上面的程序,清晰的给读者展示了如何使用训练好的权重,对图片进行预测并将结果可视化。最终输出的图片上,检测出了每个昆虫,标出了它们的边界框和具体类别。
提升方案
这里给出的是一份基础版本的代码,可以在上面继续改进提升,可以使用的改进方案有:
1、使用其它模型如faster rcnn等 (难度系数5)
2、使用数据增多,可以对原图进行翻转、裁剪等操作 (难度系数3)
3、修改anchor参数的设置,教案中的anchor参数设置直接使用原作者在coco数据集上的设置,针对此模型是否要调整 (难度系数3)
4、调整优化器、学习率策略、正则化系数等是否能提升模型精度 (难度系数1)
总结
本章系统化的介绍了计算机视觉的各种网络结构和发展历程,并以图像分类和目标检测两个任务为例,展示了ResNet和YOLOv3等算法的实现。期望读者不仅掌握了计算机视觉模型搭建方法,也能够对提取视觉特征的方法有更深入的认知。
作业
修改模型参数,对比添加多尺度特征融合前后模型的效果。