- 参考
https://github.com/bubbliiiing/yolo3-pytorch train.py流程：加载数据dataloader.py正向传播tiny.py反向传播loss.py- dataloader.py
import cv2
import numpy as np
from PIL import Image
from torch.utils.data.dataset import Dataset
class YoloDataset(Dataset):
def __init__(self, annotation_lines, input_shape, train):
super(YoloDataset, self).__init__()
self.annotation_lines = annotation_lines
self.input_shape = input_shape
self.length = len(self.annotation_lines)
self.train = train
def __len__(self):
return self.length
def __getitem__(self, index):
index = index % self.length
image, box = self.get_random_data(self.annotation_lines[index], self.input_shape[0:2], random = self.train)
image = np.transpose(np.array(image, dtype=np.float32)/255.0, (2, 0, 1))
box = np.array(box, dtype=np.float32)
if len(box) != 0:
box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1]
box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0]
box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
return image, box
def rand(self, a=0, b=1):
return np.random.rand()*(b-a) + a
def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.02, sat=1.5, val=1.5, random=True):
line = annotation_line.split()
label_line = line[0][:-4]+'.txt'
boxes = []
for lin in open(label_line):
t = lin.split()
boxes.append([t[1],t[2],t[3],t[4],t[0]])
box = np.array(boxes, dtype=np.float32)
image = Image.open(line[0])
iw, ih = image.size
h, w = input_shape
if len(box) > 0:
box[:, [0,2]] = box[:, [0,2]] * iw
box[:, [1,3]] = box[:, [1,3]] * ih
box[:, 0:2] = box[:, 0:2] - box[:, 2:4] / 2
box[:, 2:4] = box[:, 0:2] + box[:, 2:4]
if not random:
scale = min(w/iw, h/ih)
nw = int(iw*scale)
nh = int(ih*scale)
dx = (w-nw)//2
dy = (h-nh)//2
image = image.resize((nw,nh), Image.BICUBIC)
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image_data = np.array(new_image, np.float32)
if len(box)>0:
np.random.shuffle(box)
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)]
return image_data, box
new_ar = w/h * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
scale = self.rand(.25, 2)
if new_ar < 1:
nh = int(scale*h)
nw = int(nh*new_ar)
else:
nw = int(scale*w)
nh = int(nw/new_ar)
image = image.resize((nw,nh), Image.BICUBIC)
dx = int(self.rand(0, w-nw))
dy = int(self.rand(0, h-nh))
new_image = Image.new('RGB', (w,h), (128,128,128))
new_image.paste(image, (dx, dy))
image = new_image
flip = self.rand()<.5
if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
hue = self.rand(-hue, hue)
sat = self.rand(1, sat) if self.rand()<.5 else 1/self.rand(1, sat)
val = self.rand(1, val) if self.rand()<.5 else 1/self.rand(1, val)
x = cv2.cvtColor(np.array(image,np.float32)/255, cv2.COLOR_RGB2HSV)
x[..., 0] += hue*360
x[..., 0][x[..., 0]>1] -= 1
x[..., 0][x[..., 0]<0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x[:,:, 0]>360, 0] = 360
x[:, :, 1:][x[:, :, 1:]>1] = 1
x[x<0] = 0
image_data = cv2.cvtColor(x, cv2.COLOR_HSV2RGB)*255
if len(box)>0:
np.random.shuffle(box)
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
if flip: box[:, [0,2]] = w - box[:, [2,0]]
box[:, 0:2][box[:, 0:2]<0] = 0
box[:, 2][box[:, 2]>w] = w
box[:, 3][box[:, 3]>h] = h
box_w = box[:, 2] - box[:, 0]
box_h = box[:, 3] - box[:, 1]
box = box[np.logical_and(box_w>1, box_h>1)]
return image_data, box
def yolo_dataset_collate(batch):
images = []
bboxes = []
for img, box in batch:
images.append(img)
bboxes.append(box)
images = np.array(images)
return images, bboxes
import torch, math, sys
import numpy as np
import torch.nn as nn
def iou(a,b):
A=len(a)
B=len(b)
area1=a[:,2]*a[:,3]
area1=area1.unsqueeze(1).expand(A,B)
area2=b[:,2]*b[:,3]
area2=area2.unsqueeze(0).expand(A,B)
aa=torch.zeros_like(a)
aa[:,0:2]=a[:,0:2]-a[:,2:4]/2
aa[:,2:4]=aa[:,0:2]+a[:,2:4]
aa=aa.unsqueeze(1).expand(A,B,4)
bb=torch.zeros_like(b)
bb[:,0:2]=b[:,0:2]-b[:,2:4]/2
bb[:,2:4]=bb[:,0:2]+b[:,2:4]
bb=bb.unsqueeze(0).expand(A,B,4)
lt=torch.max(aa[:,:,0:2], bb[:,:,0:2])
rb=torch.min(aa[:,:,2:4], bb[:,:,2:4])
inter=torch.clamp((rb-lt), min=0)
inter=inter[:,:,0]*inter[:,:,1]
return inter/(area1+area2-inter)
def clip(a):
mi=1e-7
ma=1-mi
b=(a>=mi).float()*a+(a<mi).float()*mi
b=(b<=ma).float()*b+(b>ma).float()*ma
return b
def BCELoss(pred, target):
p=clip(pred)
return -target*torch.log(p)-(1-target)*torch.log(1-p)
def MSELoss(pred, target):
return torch.pow((pred-target), 2)
class Loss(nn.Module):
def __init__(self, input_size, anchors, classes, anchors_mask=[[0,1,2], [3,4,5]]):
super().__init__()
self.input_size = input_size
self.anchors = anchors
self.bbox_attrs = 5 + classes
self.anchors_mask = anchors_mask
self.ignore_threshold = 0.5
'''
l: 第l组anchors_mask
out： b*255*h*w， 网络输出之一
targets: b*N*5，比例形式的gt
'''
def forward(self, l, out, target):
b = out.size(0)
in_h = out.size(2)
in_w = out.size(3)
s = self.input_size[0] // in_w
scaled_anchors = [(aw/s, ah/s) for aw,ah in self.anchors]
y_true, no_obj, scale = self.get_target(l, target, scaled_anchors, in_h, in_w)
scale=2-scale
out = out.view(b, 3, self.bbox_attrs, in_h, in_w).permute(0,1,3,4,2)
x = torch.sigmoid(out[...,0])
y = torch.sigmoid(out[...,1])
w = out[...,2]
h = out[...,3]
c = torch.sigmoid(out[...,4])
cl=torch.sigmoid(out[...,5:])
no_obj = self.get_ignore(l,x,y,h,w,target, scaled_anchors, in_h, in_w, no_obj)
if x.is_cuda:
y_true = y_true.cuda()
no_obj = no_obj.cuda()
scale = scale.cuda()
xloss=torch.sum(BCELoss(x, y_true[...,0])*y_true[...,4]*scale)
yloss=torch.sum(BCELoss(y, y_true[...,1])*y_true[...,4]*scale)
wloss=torch.sum(MSELoss(w, y_true[...,2])*y_true[...,4]*scale*0.5)
hloss=torch.sum(MSELoss(h, y_true[...,3])*y_true[...,4]*scale*0.5)
closs=torch.sum(BCELoss(c, y_true[...,4])*y_true[...,4] + BCELoss(c, y_true[...,4])*no_obj)
clsloss=torch.sum(BCELoss(cl[y_true[...,4]==1], y_true[...,5:][y_true[...,4]==1]))
loss = xloss + yloss + wloss + hloss + closs + clsloss
num=torch.sum(y_true[...,4])
num=torch.max(num, torch.ones_like(num))
return loss, num
'''
l: 第l组anchors_mask
targets: b*N*5，比例形式的gt
anchors: 9*2，已经放缩过的
in_h： 特征图高度
in_w： 特征图宽度
每个batch：
N*4的gt和9*4的anchor求iou
每个gt的最大IOU对应的anchor：
如果不在当前mask： continue
否则：gt中心点坐标和anchor序号确定位置，赋值
'''
def get_target(self, l, targets, anchors, in_h, in_w):
b = len(targets)
c = len(self.anchors_mask[l])
y_true = torch.zeros(b,c,in_h, in_w,self.bbox_attrs,requires_grad = False)
no_obj = torch.ones(b,c,in_h, in_w,requires_grad = False)
scale = torch.zeros(b,c,in_h, in_w,requires_grad = False)
for bi in range(b):
if(len(targets[bi]) == 0): continue
batch_target = torch.zeros(len(targets[bi]), 4)
batch_target[:,2] = targets[bi][:,2] * in_w
batch_target[:,3] = targets[bi][:,3] * in_h
anchor4 = torch.zeros(len(anchors), 4)
anchor4[:,2:] = torch.FloatTensor(anchors)
ious = iou(batch_target, anchor4)
bests = torch.argmax(ious, dim=1)
batch_target[:,0] = targets[bi][:,0] * in_w
batch_target[:,1] = targets[bi][:,1] * in_h
for it, best in enumerate(bests):
if best not in self.anchors_mask[l]:
continue
c = self.anchors_mask[l].index(best)
i = torch.floor(batch_target[it,0]).long()
j = torch.floor(batch_target[it,1]).long()
no_obj[bi,c,j,i] = 0
y_true[bi,c,j,i,0] = batch_target[it,0] - i.float()
y_true[bi,c,j,i,1] = batch_target[it,1] - j.float()
y_true[bi,c,j,i,2] = math.log(batch_target[it,2]/anchors[best][0])
y_true[bi,c,j,i,3] = math.log(batch_target[it,3]/anchors[best][1])
y_true[bi,c,j,i,4] = 1
clss=targets[bi][it][4].long()
y_true[bi,c,j,i,5+clss] = 1
scale[bi,c,j,i] = batch_target[it,2]*batch_target[it,3]/in_h/in_w
return y_true, no_obj, scale
'''
l: 第l组anchors_mask
x, y, h, w: b*3*h*w，网络输出，其中x,y已经过sigmoid
targets: b*N*5，比例形式的gt
anchors: 9*2，已经放缩过的
in_h： 特征图高度
in_w： 特征图宽度
no_obj： b*3*h*w，标记负样本
将anchors_mask对应的anchors分布到特征图每个网格上，形状是b*3*h*w*2
将x, y, h, w结合上面的anchors转化并concat成b*3*h*w*4的预测值
每个batch：
计算与gt的iou
取每个预测框的最大iou值
最大IOU超过阈值的是忽略样本，即no_obj对应的值设为0
'''
def get_ignore(self, l, x, y, h, w, targets, anchors, in_h, in_w, no_obj):
ft = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
b = len(targets)
anchor_l = np.array(anchors)[self.anchors_mask[l]]
anchor_w = ft(anchor_l[:,0:1])
anchor_h = ft(anchor_l[:,1:])
anchor_w = anchor_w.repeat(1,in_h*in_w).repeat(b,1).view(b,3,in_h,in_w)
anchor_h = anchor_h.repeat(1,in_h*in_w).repeat(b,1).view(b,3,in_h,in_w)
tw = (torch.exp(w.data)*anchor_w).unsqueeze(-1)
th = (torch.exp(h.data)*anchor_h).unsqueeze(-1)
grid_y, grid_x = torch.meshgrid(torch.arange(in_w), torch.arange(in_h))
grid_x = grid_x.repeat(b,3,1,1).type(ft)
grid_y = grid_y.repeat(b,3,1,1).type(ft)
tx = (x.data + grid_x).unsqueeze(-1)
ty = (y.data + grid_y).unsqueeze(-1)
pred = torch.cat([tx, ty, tw, th], -1)
for bi in range(b):
if(len(targets[bi]) == 0): continue
pre = pred[bi].view(-1,4)
gt = torch.zeros_like(targets[bi])
gt[:,[0,2]] = targets[bi][:,[0,2]] * in_w
gt[:,[1,3]] = targets[bi][:,[1,3]] * in_h
gt = gt[:,:4]
ious=iou(gt, pre)
maxx, _ = torch.max(ious, dim=0)
maxx = maxx.view(3,in_h,in_w)
no_obj[bi][maxx > self.ignore_threshold] = 0
return no_obj
from tiny import Tiny
from loss import Loss
from torch.utils.data import DataLoader
from PIL import Image, ImageDraw
import numpy as np
import torch, sys, cv2
import torch.optim as optim
from dataloader import YoloDataset, yolo_dataset_collate
def show_batch(image, label):
for i in range(len(image)):
im = np.transpose(image[i]*255.0,(1,2,0)).astype('uint8')[:,:,[2,1,0]]
ih, iw = np.shape(im)[0:2]
cv2.imshow("im", im)
cv2.waitKey(0)
batch_size = 2
data_txt='/home/lwd/data/all.txt'
with open(data_txt) as f:
train_lines = f.readlines()
train_dataset=YoloDataset(train_lines, (416, 416), True)
train_data = DataLoader(train_dataset, shuffle = True, batch_size = batch_size, pin_memory=True, drop_last=True, collate_fn=yolo_dataset_collate)
test_txt='/home/lwd/data/test.txt'
with open(test_txt) as f:
test_lines = f.readlines()
test_dataset=YoloDataset(test_lines, (416, 416), False)
test_data = DataLoader(test_dataset, shuffle = False, batch_size = batch_size, pin_memory=True, drop_last=True, collate_fn=yolo_dataset_collate)
train_step = len(train_lines) // batch_size
val_step = len(test_lines) // batch_size
model_path=''
net=Tiny()
net.init()
net.load_darknet('/home/lwd/code/darknet/yolov3-tiny.conv.15')
net = net.cuda()
if len(model_path) > 1:
paras=torch.load(model_path, map_location='cuda')
net.load_state_dict(paras)
anchors = [[44, 43], [87, 39], [64,102], [20, 18], [43, 21], [28, 34]]
los = Loss((416, 416), anchors, 80)
lr = 1e-4
optimizer = optim.Adam(net.parameters(), lr, weight_decay = 5e-4)
i = 1
lr_cnt = 0
vl_last = 9
for param in net.backbone.parameters():
param.requires_grad = False
while True:
net.train()
if i == 400:
for param in net.backbone.parameters():
param.requires_grad = True
train_loss = 0
for bi, (batch_image, batch_label) in enumerate(train_data):
loss = 0
number = 0
batch_image = torch.from_numpy(batch_image).type(torch.FloatTensor).cuda()
batch_label = [torch.from_numpy(ann).type(torch.FloatTensor).cuda() for ann in batch_label]
optimizer.zero_grad()
outputs = net(batch_image)
for oi, output in enumerate(outputs):
loss_item, num_pos = los(oi, output, batch_label)
loss += loss_item
number += num_pos
loss_value = loss / number
loss_value.backward()
optimizer.step()
train_loss += loss_value.item()
net.eval()
val_loss = 0
with torch.no_grad():
for bi, (batch_image, batch_label) in enumerate(test_data):
loss = 0
number = 0
batch_image = torch.from_numpy(batch_image).type(torch.FloatTensor).cuda()
batch_label = [torch.from_numpy(ann).type(torch.FloatTensor).cuda() for ann in batch_label]
optimizer.zero_grad()
outputs = net(batch_image)
for oi, output in enumerate(outputs):
loss_item, num_pos = los(oi, output, batch_label)
loss += loss_item
number += num_pos
loss_value = loss / number
val_loss += loss_value.item()
vl=val_loss / val_step
print('epoch: ', i, ' ------ train_loss:', train_loss / train_step, ' val_loss:', val_loss / val_step)
print(optimizer.param_groups[0]['lr'])
if vl < vl_last:
torch.save(net.state_dict(), 'result/model/'+str(i)+':'+str(vl)[:5]+'.pth')
vl_last = vl
if i > 999:
break
i += 1
- 备注
- 使用darknet的预训练模型训练
- 学习率固定1e-4
- 前400次训练不更新预训练权重
- 在验证loss=5.5左右得到可用的模型
- 试了从头训练，loss在9附近降不下去，可能是训练集太小
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