PyTorch YOLO2
PyTorch 튜토리얼 1~10 PyTorch MNIST Example similar to TensorFlow Tutorial
1. 개요
1.1 기본 import 패키지
import torch # arrays on GPU
import torch.autograd as autograd #build a computational graph
import torch.nn as nn ## neural net library
import torch.nn.functional as F ## most non-linearities are here
import torch.optim as optim # optimization package
1.2 파일 입력 : DataLoader()
train_loader = torch.utils.data.DataLoader()
2. Modeling
A. 정의된 모델 불러 오기
cuda = torch.cuda.is_available()
start_epoch = 0
start_iteration = 0
resume = False
model = torchfcn.models.FCN8s(n_class=21) #import torchfcn 하였기 떄문에 가능
# 이전에 저장되었던 부분 부터 하기
if resume:
checkpoint = torch.load(resume)
model.load_state_dict(checkpoint['model_state_dict'])
start_epoch = checkpoint['epoch']
start_iteration = checkpoint['iteration']
else:
fcn16s = torchfcn.models.FCN16s()
fcn16s.load_state_dict(torch.load(torchfcn.models.FCN16s.download()))
model.copy_params_from_fcn16s(fcn16s) #파라미터 복사
if cuda:
model = model.cuda()
B. 직접 정의하기
class MnistModel(nn.Module):
def __init__(self):
super(MnistModel, self).__init__()
# input is 28x28
# padding=2 for same padding
self.conv1 = nn.Conv2d(1, 32, 5, padding=2)
# feature map size is 14*14 by pooling
# padding=2 for same padding
self.conv2 = nn.Conv2d(32, 64, 5, padding=2)
# feature map size is 7*7 by pooling
self.fc1 = nn.Linear(64*7*7, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), 2)
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, 64*7*7) # reshape Variable
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
model = MnistModel()
[Tip] 모델내 파라미터(Weight, biase)확인
for p in model.parameters():
print(p.size())
params = list(model.parameters())
print(len(params))
print(params[0].size()) # conv1's .weight
출처: http://bob3rdnewbie.tistory.com/316 [Newbie Hacker]
3. Training : model.train()
3.1 학습 알고리즘 정의
optimizer = optim.Adam(model.parameters(), lr=0.0001)
3.2 Trainer 정의
trainer = torchfcn.Trainer(
cuda=cuda,
model=model,
optimizer=optim,
train_loader=train_loader,
val_loader=val_loader,
out='./log/',
#max_iter=cfg['max_iteration'],
max_iter=100000,
#interval_validate=cfg.get('interval_validate', len(train_loader)),
interval_validate=4000,
)
trainer.epoch = start_epoch
trainer.iteration = start_iteration
최종 학습 수행 명령어 : trainer.train()
[참고] Loss 함수 정의 : nn.MSELoss()
output = net(input)
target = Variable(torch.arange(1, 11)) # a dummy target, for example
criterion = nn.MSELoss()
loss = criterion(output, target)
print(loss)
[참고] 역전파 : loss.backward()
loss.backward()를 호출하고 backward() 호출 이전과 이후의 바이어스 그라디언트를 살펴볼 것이다.
net.zero_grad() # zeroes the gradient buffers of all parameters
print(net.conv1.bias.grad)
loss.backward()
print(net.conv1.bias.grad)
[전체 코드]
model.train()
train_loss = []
train_accu = []
i = 0
for epoch in range(15):
for data, target in train_loader:
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward() # calc gradients
train_loss.append(loss.data[0])
optimizer.step() # update gradients
prediction = output.data.max(1)[1] # first column has actual prob.
accuracy = prediction.eq(target.data).sum()/batch_size*100
train_accu.append(accuracy)
if i % 1000 == 0:
print('Train Step: {}\tLoss: {:.3f}\tAccuracy: {:.3f}'.format(i, loss.data[0], accuracy))
i += 1
model.train()
4. Testing : model.eval()
model.eval()
[전체 코드]
model.eval()
correct = 0
for data, target in test_loader:
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
prediction = output.data.max(1)[1]
correct += prediction.eq(target.data).sum()
print('Test set: Accuracy: {:.2f}%'.format(100. * correct / len(test_loader.dataset)))
5. Fine tuning
| 제목 | YOLO2 |
|---|---|
| 코드 | marvis |
| 참고 |
PyTorch YOLO2
1. 개요
2. 설치
# pytorch 설치
conda create -n py2torch python=2.7 ipykernel
source activate py2torch
conda install pytorch torchvision cuda80 -c soumith
#YOLO설치
git clone [email protected]:marvis/pytorch-yolo2.git
wget http://pjreddie.com/media/files/yolo.weights
python detect.py cfg/yolo.cfg yolo.weights data/dog.jpg
3. Training
Training YOLO on VOC
Get The Pascal VOC Data
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
Generate Labels for VOC
wget http://pjreddie.com/media/files/voc_label.py
python voc_label.py
cat 2007_train.txt 2007_val.txt 2012_*.txt > voc_train.txt
Modify Cfg for Pascal Data
Change the cfg/voc.data config file
train = train.txt
valid = 2007_test.txt
names = data/voc.names
backup = backup
Download Pretrained Convolutional Weights
Download weights from the convolutional layers
wget http://pjreddie.com/media/files/darknet19_448.conv.23
or run the following command:
python partial.py cfg/darknet19_448.cfg darknet19_448.weights darknet19_448.conv.23 23
Train The Model
python train.py cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23
Evaluate The Model
python valid.py cfg/voc.data cfg/yolo-voc.cfg yolo-voc.weights
python scripts/voc_eval.py results/comp4_det_test_
mAP test on released models
yolo-voc.weights 544 0.7682 (paper: 78.6)
yolo-voc.weights 416 0.7513 (paper: 76.8)
tiny-yolo-voc.weights 416 0.5410 (paper: 57.1)
4. Testing
5. Fine tuning
변경 사항
# detect.py
PATH_TO_TEST_IMAGES_DIR = '/home/hjlim99/test_images/'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'frame{}.jpg'.format(i)) for i in range(1, 11561) ]
def detect(cfgfile, weightfile, imgfile):
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
if m.num_classes == 20:
namesfile = 'data/voc.names'
elif m.num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 1
if use_cuda:
m.cuda()
for image_path in TEST_IMAGE_PATHS:
img = Image.open(image_path).convert('RGB')
sized = img.resize((m.width, m.height))
#img = Image.open(imgfile).convert('RGB')
#sized = img.resize((m.width, m.height))
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('%s: Predicted in %f seconds.' % (imgfile, (finish-start)))
class_names = load_class_names(namesfile)
#plot_boxes(img, boxes, 'predictions.jpg', class_names)
filename = image_path.replace('/home/hjlim99/test_images/', '')
plot_boxes(img, boxes, './result/{}'.format(filename), class_names)
#'./save/{}.png'.format(image_path)
cfgfile = "cfg/yolo.cfg"
weightfile = "weight/yolo.weights"
imgfile = "data/dog.jpg"
detect(cfgfile, weightfile, imgfile)