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train.py
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train.py
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from __future__ import print_function
import sys
if len(sys.argv) != 4:
print('Usage:')
print('python train.py datacfg cfgfile weightfile')
exit()
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms
from torch.autograd import Variable
import dataset
import random
import math
from utils import *
from cfg import parse_cfg
from region_loss import RegionLoss
from darknet import Darknet
# Training settings
datacfg = sys.argv[1]
cfgfile = sys.argv[2]
weightfile = sys.argv[3]
data_options = read_data_cfg(datacfg)
net_options = parse_cfg(cfgfile)[0]
trainlist = data_options['train']
testlist = data_options['valid']
backupdir = data_options['backup']
nsamples = file_lines(trainlist)
batch_size = int(net_options['batch'])
max_batches = int(net_options['max_batches'])
learning_rate = float(net_options['learning_rate'])
momentum = float(net_options['momentum'])
#Train parameters
max_epochs = max_batches*batch_size/nsamples+1
use_cuda = True
seed = 22222
eps = 1e-5
epoch_step = 120 # epochs to change lr
lr_step = 0.1
num_workers = 8
save_interval = 15 # epoches
dot_interval = 70 # batches
# Test parameters
conf_thresh = 0.25
nms_thresh = 0.4
iou_thresh = 0.5
###############
torch.manual_seed(seed)
if use_cuda:
torch.cuda.manual_seed(seed)
model = Darknet(cfgfile)
region_loss = model.loss
model.load_weights(weightfile)
model.print_network()
init_epoch = model.seen / nsamples
kwargs = {'num_workers': num_workers, 'pin_memory': True} if use_cuda else {}
test_loader = torch.utils.data.DataLoader(
dataset.listDataset(testlist, shape=(model.width, model.height),
shuffle=False,
transform=transforms.Compose([
transforms.ToTensor(),
]), train=False),
batch_size=batch_size, shuffle=False, **kwargs)
if use_cuda:
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = learning_rate * (lr_step ** (epoch // epoch_step))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if epoch % epoch_step == 0:
logging('lr = %f' % (lr))
def train(epoch):
train_loader = torch.utils.data.DataLoader(
dataset.listDataset(trainlist, shape=(model.module.width, model.module.height),
shuffle=True,
transform=transforms.Compose([
transforms.ToTensor(),
]), train=True, seen=model.module.seen),
batch_size=batch_size, shuffle=False, **kwargs)
logging('epoch %d : processed %d samples' % (epoch, epoch * len(train_loader.dataset)))
model.train()
adjust_learning_rate(optimizer, epoch)
for batch_idx, (data, target) in enumerate(train_loader):
if (batch_idx+1) % dot_interval == 0:
sys.stdout.write('.')
if use_cuda:
data = data.cuda()
#target= target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = region_loss(output, target)
loss.backward()
optimizer.step()
print('')
if (epoch+1) % save_interval == 0:
logging('save weights to %s/%06d.weights' % (backupdir, epoch+1))
model.module.seen = (epoch + 1) * len(train_loader.dataset)
model.module.save_weights('%s/%06d.weights' % (backupdir, epoch+1))
def test(epoch):
def truths_length(truths):
for i in range(50):
if truths[i][1] == 0:
return i
model.eval()
num_classes = model.module.num_classes
anchors = model.module.anchors
num_anchors = model.module.num_anchors
total = 0.0
proposals = 0.0
correct = 0.0
for batch_idx, (data, target) in enumerate(test_loader):
if use_cuda:
data = data.cuda()
data = Variable(data, volatile=True)
output = model(data).data
all_boxes = get_region_boxes(output, conf_thresh, num_classes, anchors, num_anchors)
for i in range(output.size(0)):
boxes = all_boxes[i]
boxes = nms(boxes, nms_thresh)
truths = target[i].view(-1, 5)
num_gts = truths_length(truths)
total = total + num_gts
for i in range(len(boxes)):
if boxes[i][4] > conf_thresh:
proposals = proposals+1
for i in range(num_gts):
box_gt = [truths[i][1], truths[i][2], truths[i][3], truths[i][4], 1.0, 1.0, truths[i][0]]
best_iou = 0
for j in range(len(boxes)):
iou = bbox_iou(box_gt, boxes[j], x1y1x2y2=False)
best_iou = max(iou, best_iou)
if best_iou > iou_thresh and boxes[j][6] == box_gt[6]:
correct = correct+1
precision = 1.0*correct/(proposals+eps)
recall = 1.0*correct/(total+eps)
fscore = 2.0*precision*recall/(precision+recall+eps)
logging("precision: %f, recall: %f, fscore: %f" % (precision, recall, fscore))
evaluate = False
if evaluate:
print('evaluating ...')
test(0)
else:
for epoch in range(init_epoch, max_epochs):
train(epoch)
test(epoch)