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train_defense.py
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train_defense.py
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from __future__ import print_function
import sys
if len(sys.argv) != 6:
print('Usage:')
print('python train.py datacfg cfgfile weightfile backupdir trainlist')
exit()
import time
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
import os
from utils import *
from cfg import parse_cfg
from region_loss import RegionLoss
from darknet_defense import Darknet
# from models.tiny_yolo import TinyYoloNet
# 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]
backupdir = sys.argv[4]
trainlist = sys.argv[5]
nsamples = file_lines(trainlist)
ngpus = 1
num_workers = 10
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'])
decay = float(net_options['decay'])
steps = [float(step) for step in net_options['steps'].split(',')]
scales = [float(scale) for scale in net_options['scales'].split(',')]
# Train parameters
max_epochs = max_batches * batch_size // nsamples + 1
use_cuda = True
seed = int(time.time())
eps = 1e-5
save_interval = 2000 # iterations
dot_interval = 70 # batches
# Test parameters
conf_thresh = 0.25
nms_thresh = 0.4
iou_thresh = 0.5
if not os.path.exists(backupdir):
os.makedirs(backupdir)
###############
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()
model.seen = 0
region_loss.seen = model.seen
processed_batches = model.seen / batch_size
init_width = model.width
init_height = model.height
init_epoch = model.seen // nsamples
kwargs = {'num_workers': num_workers, 'pin_memory': True} if use_cuda else {}
if use_cuda:
if ngpus > 1 and False:
model = torch.nn.DataParallel(model).cuda()
else:
model = model.cuda()
params_dict = dict(model.named_parameters())
params = []
for key, value in params_dict.items():
if key.find('.bn') >= 0 or key.find('.bias') >= 0:
params += [{'params': [value], 'weight_decay': 0.0}]
else:
params += [{'params': [value], 'weight_decay': decay * batch_size}]
optimizer = optim.SGD(model.parameters(), lr=learning_rate / batch_size,
momentum=momentum, dampening=0, weight_decay=decay * batch_size)
def adjust_learning_rate(optimizer, batch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = learning_rate
for i in range(len(steps)):
scale = scales[i] if i < len(scales) else 1
if batch >= steps[i]:
lr = lr * scale
if batch == steps[i]:
break
else:
break
for param_group in optimizer.param_groups:
param_group['lr'] = lr / batch_size
return lr
checkpoint_start_time = time.time()
def train(epoch):
global processed_batches, checkpoint_start_time
t0 = time.time()
cur_model = model
train_loader = torch.utils.data.DataLoader(
dataset.listDataset(trainlist, shape=(init_width, init_height),
shuffle=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
train=True,
seen=0,
batch_size=batch_size,
num_workers=num_workers),
batch_size=batch_size, shuffle=False, **kwargs)
lr = adjust_learning_rate(optimizer, processed_batches)
model.train()
features = []
def hook_feature(module, input, output):
features.append(output)
h1 = model._modules.get('models')[16].register_forward_hook(
hook_feature) # layer chosen for calculating defense loss
t1 = time.time()
avg_time = torch.zeros(9)
count = 0
logging('epoch %d, processed %d samples, lr %f' %
(epoch, epoch * len(train_loader.dataset), lr))
for batch_idx, (data, target, path) in enumerate(train_loader):
features = []
t2 = time.time()
adjust_learning_rate(optimizer, processed_batches)
processed_batches = processed_batches + 1
if use_cuda:
data = data.cuda()
t3 = time.time()
data, target = Variable(data, requires_grad=True), Variable(target.float())
t4 = time.time()
optimizer.zero_grad()
t5 = time.time()
output = model(data)
t6 = time.time()
region_loss.seen = region_loss.seen + data.data.size(0)
loss = region_loss(output, target, features[0])
t7 = time.time()
loss.backward()
t8 = time.time()
optimizer.step()
t9 = time.time()
if False and batch_idx > 1:
avg_time[0] = avg_time[0] + (t2 - t1)
avg_time[1] = avg_time[1] + (t3 - t2)
avg_time[2] = avg_time[2] + (t4 - t3)
avg_time[3] = avg_time[3] + (t5 - t4)
avg_time[4] = avg_time[4] + (t6 - t5)
avg_time[5] = avg_time[5] + (t7 - t6)
avg_time[6] = avg_time[6] + (t8 - t7)
avg_time[7] = avg_time[7] + (t9 - t8)
avg_time[8] = avg_time[8] + (t9 - t1)
print('-------------------------------')
print(' load data : %f' % (avg_time[0] / (batch_idx)))
print(' cpu to cuda : %f' % (avg_time[1] / (batch_idx)))
print('cuda to variable : %f' % (avg_time[2] / (batch_idx)))
print(' zero_grad : %f' % (avg_time[3] / (batch_idx)))
print(' forward feature : %f' % (avg_time[4] / (batch_idx)))
print(' forward loss : %f' % (avg_time[5] / (batch_idx)))
print(' backward : %f' % (avg_time[6] / (batch_idx)))
print(' step : %f' % (avg_time[7] / (batch_idx)))
print(' total : %f' % (avg_time[8] / (batch_idx)))
t1 = time.time()
del loss, output, data
torch.cuda.empty_cache()
print('')
t1 = time.time()
logging('training with %f samples/s' %
(len(train_loader.dataset) / (t1 - t0)))
checkpoint_stop_time = time.time()
training_time = checkpoint_stop_time - checkpoint_start_time
if (processed_batches + 1) % save_interval == 0:
logging('save weights to %s/%06d.weights' %
(backupdir, processed_batches + 1))
cur_model.save_weights(
'%s/%06d.weights' % (backupdir, processed_batches + 1))
h1.remove()
for epoch in range(init_epoch, 1000):
train(epoch)