def __init__(self):
# model
self.model = RCF()
self.model.cuda()
self.vgg_model_name = os.path.join(script_dir, "fast-rcnn-vgg16-pascal07-dagnn.mat")
self.weights_pretraind = os.path.join(script_dir,"RCFcheckpoint_epoch12.pth")
self.model.apply(weights_init)
load_vgg16pretrain(self.model, self.vgg_model_name)
self.checkpoint = torch.load(self.weights_pretraind)
self.model.load_state_dict(self.checkpoint['state_dict'])
self.model.eval()
def main():
args.cuda = True
# dataset
train_dataset = BSDSLoader(root=args.dataset, split="train")
test_dataset = BSDSLoader(root=args.dataset, split="test")
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=8,
drop_last=True,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=8,
drop_last=True,
shuffle=False)
with open('data/HED-BSDS/test.lst', 'r') as f:
test_list = f.readlines()
test_list = [split(i.rstrip())[1] for i in test_list]
assert len(test_list) == len(test_loader), "%d vs %d" % (len(test_list),
len(test_loader))
# model
model = HED()
model.cuda()
model.apply(weights_init)
load_vgg16pretrain(model)
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(args.resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
#tune lr
net_parameters_id = {}
net = model
for pname, p in net.named_parameters():
if pname in [
'conv1_1.weight', 'conv1_2.weight', 'conv2_1.weight',
'conv2_2.weight', 'conv3_1.weight', 'conv3_2.weight',
'conv3_3.weight', 'conv4_1.weight', 'conv4_2.weight',
'conv4_3.weight'
]:
#print(pname, 'lr:1 de:1')
if 'conv1-4.weight' not in net_parameters_id:
net_parameters_id['conv1-4.weight'] = []
net_parameters_id['conv1-4.weight'].append(p)
elif pname in [
'conv1_1.bias', 'conv1_2.bias', 'conv2_1.bias', 'conv2_2.bias',
'conv3_1.bias', 'conv3_2.bias', 'conv3_3.bias', 'conv4_1.bias',
'conv4_2.bias', 'conv4_3.bias'
]:
#print(pname, 'lr:2 de:0')
if 'conv1-4.bias' not in net_parameters_id:
net_parameters_id['conv1-4.bias'] = []
net_parameters_id['conv1-4.bias'].append(p)
elif pname in ['conv5_1.weight', 'conv5_2.weight', 'conv5_3.weight']:
#print(pname, 'lr:100 de:1')
if 'conv5.weight' not in net_parameters_id:
net_parameters_id['conv5.weight'] = []
net_parameters_id['conv5.weight'].append(p)
elif pname in ['conv5_1.bias', 'conv5_2.bias', 'conv5_3.bias']:
#print(pname, 'lr:200 de:0')
if 'conv5.bias' not in net_parameters_id:
net_parameters_id['conv5.bias'] = []
net_parameters_id['conv5.bias'].append(p)
elif pname in [
'score_dsn1.weight', 'score_dsn2.weight', 'score_dsn3.weight',
'score_dsn4.weight', 'score_dsn5.weight'
]:
#print(pname, 'lr:0.01 de:1')
if 'score_dsn_1-5.weight' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.weight'] = []
net_parameters_id['score_dsn_1-5.weight'].append(p)
elif pname in [
'score_dsn1.bias', 'score_dsn2.bias', 'score_dsn3.bias',
'score_dsn4.bias', 'score_dsn5.bias'
]:
#print(pname, 'lr:0.02 de:0')
if 'score_dsn_1-5.bias' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.bias'] = []
net_parameters_id['score_dsn_1-5.bias'].append(p)
elif pname in ['score_final.weight']:
#print(pname, 'lr:0.001 de:1')
if 'score_final.weight' not in net_parameters_id:
net_parameters_id['score_final.weight'] = []
net_parameters_id['score_final.weight'].append(p)
elif pname in ['score_final.bias']:
#print(pname, 'lr:0.002 de:0')
if 'score_final.bias' not in net_parameters_id:
net_parameters_id['score_final.bias'] = []
net_parameters_id['score_final.bias'].append(p)
optimizer = torch.optim.SGD([
{
'params': net_parameters_id['conv1-4.weight'],
'lr': args.lr * 1,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['conv1-4.bias'],
'lr': args.lr * 2,
'weight_decay': 0.
},
{
'params': net_parameters_id['conv5.weight'],
'lr': args.lr * 100,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['conv5.bias'],
'lr': args.lr * 200,
'weight_decay': 0.
},
{
'params': net_parameters_id['score_dsn_1-5.weight'],
'lr': args.lr * 0.01,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['score_dsn_1-5.bias'],
'lr': args.lr * 0.02,
'weight_decay': 0.
},
{
'params': net_parameters_id['score_final.weight'],
'lr': args.lr * 0.001,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['score_final.bias'],
'lr': args.lr * 0.002,
'weight_decay': 0.
},
],
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.stepsize,
gamma=args.gamma)
# log
log = Logger(join(TMP_DIR, '%s-%d-log.txt' % ('Adam', args.lr)))
sys.stdout = log
train_loss = []
train_loss_detail = []
for epoch in range(args.start_epoch, args.maxepoch):
#if epoch == 0:
# print("Performing initial testing...")
# test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'initial-testing-record'))
tr_avg_loss, tr_detail_loss = train(
train_loader,
model,
optimizer,
epoch,
save_dir=join(TMP_DIR, 'epoch-%d-training-record' % epoch))
test(model,
test_loader,
epoch=epoch,
test_list=test_list,
save_dir=join(TMP_DIR, 'epoch-%d-testing-record' % epoch))
log.flush() # write log
# Save checkpoint
save_file = os.path.join(TMP_DIR,
'checkpoint_epoch{}.pth'.format(epoch))
save_checkpoint(
{
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
},
filename=save_file)
scheduler.step() # will adjust learning rate
# save train/val loss/accuracy, save every epoch in case of early stop
train_loss.append(tr_avg_loss)
train_loss_detail += tr_detail_loss
def main():
args.cuda = True
# dataset
train_dataset = BSDS_RCFLoader(root=args.dataset, split="train")
test_dataset = BSDS_RCFLoader(root=args.dataset + "/HED-BSDS",
split="test")
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=8,
drop_last=True,
shuffle=True)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=8,
drop_last=True,
shuffle=False)
# model
model = RCF()
model.cuda()
model.apply(weights_init)
load_vgg16pretrain(model)
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(args.resume))
else:
raise Exception()
else:
raise Exception()
#tune lr
net_parameters_id = {}
net = model
for pname, p in net.named_parameters():
if pname in [
'conv1_1.weight', 'conv1_2.weight', 'conv2_1.weight',
'conv2_2.weight', 'conv3_1.weight', 'conv3_2.weight',
'conv3_3.weight', 'conv4_1.weight', 'conv4_2.weight',
'conv4_3.weight'
]:
print(pname, 'lr:1 de:1')
if 'conv1-4.weight' not in net_parameters_id:
net_parameters_id['conv1-4.weight'] = []
net_parameters_id['conv1-4.weight'].append(p)
elif pname in [
'conv1_1.bias', 'conv1_2.bias', 'conv2_1.bias', 'conv2_2.bias',
'conv3_1.bias', 'conv3_2.bias', 'conv3_3.bias', 'conv4_1.bias',
'conv4_2.bias', 'conv4_3.bias'
]:
print(pname, 'lr:2 de:0')
if 'conv1-4.bias' not in net_parameters_id:
net_parameters_id['conv1-4.bias'] = []
net_parameters_id['conv1-4.bias'].append(p)
elif pname in ['conv5_1.weight', 'conv5_2.weight', 'conv5_3.weight']:
print(pname, 'lr:100 de:1')
if 'conv5.weight' not in net_parameters_id:
net_parameters_id['conv5.weight'] = []
net_parameters_id['conv5.weight'].append(p)
elif pname in ['conv5_1.bias', 'conv5_2.bias', 'conv5_3.bias']:
print(pname, 'lr:200 de:0')
if 'conv5.bias' not in net_parameters_id:
net_parameters_id['conv5.bias'] = []
net_parameters_id['conv5.bias'].append(p)
elif pname in [
'conv1_1_down.weight', 'conv1_2_down.weight',
'conv2_1_down.weight', 'conv2_2_down.weight',
'conv3_1_down.weight', 'conv3_2_down.weight',
'conv3_3_down.weight', 'conv4_1_down.weight',
'conv4_2_down.weight', 'conv4_3_down.weight',
'conv5_1_down.weight', 'conv5_2_down.weight',
'conv5_3_down.weight'
]:
print(pname, 'lr:0.1 de:1')
if 'conv_down_1-5.weight' not in net_parameters_id:
net_parameters_id['conv_down_1-5.weight'] = []
net_parameters_id['conv_down_1-5.weight'].append(p)
elif pname in [
'conv1_1_down.bias', 'conv1_2_down.bias', 'conv2_1_down.bias',
'conv2_2_down.bias', 'conv3_1_down.bias', 'conv3_2_down.bias',
'conv3_3_down.bias', 'conv4_1_down.bias', 'conv4_2_down.bias',
'conv4_3_down.bias', 'conv5_1_down.bias', 'conv5_2_down.bias',
'conv5_3_down.bias'
]:
print(pname, 'lr:0.2 de:0')
if 'conv_down_1-5.bias' not in net_parameters_id:
net_parameters_id['conv_down_1-5.bias'] = []
net_parameters_id['conv_down_1-5.bias'].append(p)
elif pname in [
'score_dsn1.weight', 'score_dsn2.weight', 'score_dsn3.weight',
'score_dsn4.weight', 'score_dsn5.weight'
]:
print(pname, 'lr:0.01 de:1')
if 'score_dsn_1-5.weight' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.weight'] = []
net_parameters_id['score_dsn_1-5.weight'].append(p)
elif pname in [
'score_dsn1.bias', 'score_dsn2.bias', 'score_dsn3.bias',
'score_dsn4.bias', 'score_dsn5.bias'
]:
print(pname, 'lr:0.02 de:0')
if 'score_dsn_1-5.bias' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.bias'] = []
net_parameters_id['score_dsn_1-5.bias'].append(p)
elif pname in ['score_final.weight']:
print(pname, 'lr:0.001 de:1')
if 'score_final.weight' not in net_parameters_id:
net_parameters_id['score_final.weight'] = []
net_parameters_id['score_final.weight'].append(p)
elif pname in ['score_final.bias']:
print(pname, 'lr:0.002 de:0')
if 'score_final.bias' not in net_parameters_id:
net_parameters_id['score_final.bias'] = []
net_parameters_id['score_final.bias'].append(p)
optimizer = torch.optim.SGD([
{
'params': net_parameters_id['conv1-4.weight'],
'lr': args.lr * 1,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['conv1-4.bias'],
'lr': args.lr * 2,
'weight_decay': 0.
},
{
'params': net_parameters_id['conv5.weight'],
'lr': args.lr * 100,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['conv5.bias'],
'lr': args.lr * 200,
'weight_decay': 0.
},
{
'params': net_parameters_id['conv_down_1-5.weight'],
'lr': args.lr * 0.1,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['conv_down_1-5.bias'],
'lr': args.lr * 0.2,
'weight_decay': 0.
},
{
'params': net_parameters_id['score_dsn_1-5.weight'],
'lr': args.lr * 0.01,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['score_dsn_1-5.bias'],
'lr': args.lr * 0.02,
'weight_decay': 0.
},
{
'params': net_parameters_id['score_final.weight'],
'lr': args.lr * 0.001,
'weight_decay': args.weight_decay
},
{
'params': net_parameters_id['score_final.bias'],
'lr': args.lr * 0.002,
'weight_decay': 0.
},
],
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.stepsize,
gamma=args.gamma)
# log
log = Logger(join(TMP_DIR, '%s-%d-log.txt' % ('sgd', args.lr)))
sys.stdout = log
for epoch in range(args.start_epoch, args.maxepoch):
tr_avg_loss, tr_detail_loss = train(
train_loader,
model,
optimizer,
epoch,
save_dir=join(TMP_DIR, 'epoch-%d-training-record' % epoch))
# with torch.no_grad():
# # test(model, test_loader, epoch=epoch,
# # save_dir = join(TMP_DIR, 'epoch-%d-testing-record-view' % epoch))
# # multiscale_test(model, test_loader, epoch=epoch,
# # save_dir = join(TMP_DIR, 'epoch-%d-testing-record' % epoch))
log.flush() # write log
# Save checkpoint
save_file = os.path.join(TMP_DIR, 'checkpoint.pth')
save_checkpoint(
{
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
},
filename=save_file)
scheduler.step() # will adjust learning rate
def main():
args.cuda = True
# dataset
train_dataset = BSDS_RCFLoader(root=args.dataset, split="train")
test_dataset = BSDS_RCFLoader(root=args.dataset, split="test")
train_loader = DataLoader(
train_dataset, batch_size=args.batch_size,
num_workers=8, drop_last=True,shuffle=True)
test_loader = DataLoader(
test_dataset, batch_size=args.batch_size,
num_workers=8, drop_last=True,shuffle=False)
with open('data/HED-BSDS_PASCAL/test.lst', 'r') as f:
# with open('data/HED-BSDS/test.lst', 'r') as f:
test_list = f.readlines()
test_list = [split(i.rstrip())[1] for i in test_list]
assert len(test_list) == len(test_loader), "%d vs %d" % (len(test_list), len(test_loader))
# model
model = RCF()
model.cuda()
model.apply(weights_init)
load_vgg16pretrain(model,vgg_model_name)
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'"
.format(args.resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
#tune lr
net_parameters_id = {}
net = model
for pname, p in net.named_parameters():
if pname in ['conv1_1.weight','conv1_2.weight',
'conv2_1.weight','conv2_2.weight',
'conv3_1.weight','conv3_2.weight','conv3_3.weight',
'conv4_1.weight','conv4_2.weight','conv4_3.weight']:
print(pname, 'lr:1 de:1')
if 'conv1-4.weight' not in net_parameters_id:
net_parameters_id['conv1-4.weight'] = []
net_parameters_id['conv1-4.weight'].append(p)
elif pname in ['conv1_1.bias','conv1_2.bias',
'conv2_1.bias','conv2_2.bias',
'conv3_1.bias','conv3_2.bias','conv3_3.bias',
'conv4_1.bias','conv4_2.bias','conv4_3.bias']:
print(pname, 'lr:2 de:0')
if 'conv1-4.bias' not in net_parameters_id:
net_parameters_id['conv1-4.bias'] = []
net_parameters_id['conv1-4.bias'].append(p)
elif pname in ['conv5_1.weight','conv5_2.weight','conv5_3.weight']:
print(pname, 'lr:100 de:1')
if 'conv5.weight' not in net_parameters_id:
net_parameters_id['conv5.weight'] = []
net_parameters_id['conv5.weight'].append(p)
elif pname in ['conv5_1.bias','conv5_2.bias','conv5_3.bias'] :
print(pname, 'lr:200 de:0')
if 'conv5.bias' not in net_parameters_id:
net_parameters_id['conv5.bias'] = []
net_parameters_id['conv5.bias'].append(p)
elif pname in ['conv1_1_down.weight','conv1_2_down.weight',
'conv2_1_down.weight','conv2_2_down.weight',
'conv3_1_down.weight','conv3_2_down.weight','conv3_3_down.weight',
'conv4_1_down.weight','conv4_2_down.weight','conv4_3_down.weight',
'conv5_1_down.weight','conv5_2_down.weight','conv5_3_down.weight']:
print(pname, 'lr:0.1 de:1')
if 'conv_down_1-5.weight' not in net_parameters_id:
net_parameters_id['conv_down_1-5.weight'] = []
net_parameters_id['conv_down_1-5.weight'].append(p)
elif pname in ['conv1_1_down.bias','conv1_2_down.bias',
'conv2_1_down.bias','conv2_2_down.bias',
'conv3_1_down.bias','conv3_2_down.bias','conv3_3_down.bias',
'conv4_1_down.bias','conv4_2_down.bias','conv4_3_down.bias',
'conv5_1_down.bias','conv5_2_down.bias','conv5_3_down.bias']:
print(pname, 'lr:0.2 de:0')
if 'conv_down_1-5.bias' not in net_parameters_id:
net_parameters_id['conv_down_1-5.bias'] = []
net_parameters_id['conv_down_1-5.bias'].append(p)
elif pname in ['score_dsn1.weight','score_dsn2.weight','score_dsn3.weight',
'score_dsn4.weight','score_dsn5.weight']:
print(pname, 'lr:0.01 de:1')
if 'score_dsn_1-5.weight' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.weight'] = []
net_parameters_id['score_dsn_1-5.weight'].append(p)
elif pname in ['score_dsn1.bias','score_dsn2.bias','score_dsn3.bias',
'score_dsn4.bias','score_dsn5.bias']:
print(pname, 'lr:0.02 de:0')
if 'score_dsn_1-5.bias' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.bias'] = []
net_parameters_id['score_dsn_1-5.bias'].append(p)
elif pname in ['score_final.weight']:
print(pname, 'lr:0.001 de:1')
if 'score_final.weight' not in net_parameters_id:
net_parameters_id['score_final.weight'] = []
net_parameters_id['score_final.weight'].append(p)
elif pname in ['score_final.bias']:
print(pname, 'lr:0.002 de:0')
if 'score_final.bias' not in net_parameters_id:
net_parameters_id['score_final.bias'] = []
net_parameters_id['score_final.bias'].append(p)
optimizer = torch.optim.SGD([
{'params': net_parameters_id['conv1-4.weight'] , 'lr': args.lr*1 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['conv1-4.bias'] , 'lr': args.lr*2 , 'weight_decay': 0.},
{'params': net_parameters_id['conv5.weight'] , 'lr': args.lr*100 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['conv5.bias'] , 'lr': args.lr*200 , 'weight_decay': 0.},
{'params': net_parameters_id['conv_down_1-5.weight'], 'lr': args.lr*0.1 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['conv_down_1-5.bias'] , 'lr': args.lr*0.2 , 'weight_decay': 0.},
{'params': net_parameters_id['score_dsn_1-5.weight'], 'lr': args.lr*0.01 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['score_dsn_1-5.bias'] , 'lr': args.lr*0.02 , 'weight_decay': 0.},
{'params': net_parameters_id['score_final.weight'] , 'lr': args.lr*0.001, 'weight_decay': args.weight_decay},
{'params': net_parameters_id['score_final.bias'] , 'lr': args.lr*0.002, 'weight_decay': 0.},
], lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
# optimizer = torch.optim.Adam([
# {'params': net_parameters_id['conv1-4.weight'] , 'lr': args.lr*1 , 'weight_decay': args.weight_decay},
# {'params': net_parameters_id['conv1-4.bias'] , 'lr': args.lr*2 , 'weight_decay': 0.},
# {'params': net_parameters_id['conv5.weight'] , 'lr': args.lr*100 , 'weight_decay': args.weight_decay},
# {'params': net_parameters_id['conv5.bias'] , 'lr': args.lr*200 , 'weight_decay': 0.},
# {'params': net_parameters_id['conv_down_1-5.weight'], 'lr': args.lr*0.1 , 'weight_decay': args.weight_decay},
# {'params': net_parameters_id['conv_down_1-5.bias'] , 'lr': args.lr*0.2 , 'weight_decay': 0.},
# {'params': net_parameters_id['score_dsn_1-5.weight'], 'lr': args.lr*0.01 , 'weight_decay': args.weight_decay},
# {'params': net_parameters_id['score_dsn_1-5.bias'] , 'lr': args.lr*0.02 , 'weight_decay': 0.},
# {'params': net_parameters_id['score_final.weight'] , 'lr': args.lr*0.001, 'weight_decay': args.weight_decay},
# {'params': net_parameters_id['score_final.bias'] , 'lr': args.lr*0.002, 'weight_decay': 0.},
# ], lr=args.lr, betas=(0.9, 0.99), weight_decay=args.weight_decay)
# scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
# log
log = Logger(join(TMP_DIR, '%s-%d-log.txt' %('sgd',args.lr)))
sys.stdout = log
train_loss = []
train_loss_detail = []
print("The start is epoch", args.start_epoch)
print("The max is epoch",args.maxepoch)
# for epoch in range(args.start_epoch, args.maxepoch):
# if epoch == 0:
# print("Performing initial testing...")
# multiscale_test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'initial-testing-record'))
#
# tr_avg_loss, tr_detail_loss = train(
# train_loader, model, optimizer, epoch,
# save_dir = join(TMP_DIR, 'epoch-%d-training-record' % epoch))
#
# test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'epoch-%d-testing-record-view' % epoch))
#
# multiscale_test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'epoch-%d-testing-record' % epoch))
#
# log.flush() # write log
# # Save checkpoint
# save_file = os.path.join(TMP_DIR, 'checkpoint_epoch{}.pth'.format(epoch))
# save_checkpoint({
# 'epoch': epoch,
# 'state_dict': model.state_dict(),
# 'optimizer': optimizer.state_dict()
# }, filename=save_file)
# scheduler.step() # will adjust learning rate
## save train/val loss/accuracy, save every epoch in case of early stop
# train_loss.append(tr_avg_loss)
# train_loss_detail += tr_detail_loss
#Testing the pretraind model over the test images
checkpoint=torch.load("RCFcheckpoint_epoch12.pth")
model.load_state_dict(checkpoint['state_dict'])
epoch =0
# print("Performing initial testing...")
# multiscale_test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'initial-testing-record'))
# test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR, 'epoch-%d-testing-record-view' % epoch))
#
#########################
#Test for our dataset!
########################
vid_name = 'StudySpot'
# # Read the video from specified path
# cam = cv2.VideoCapture('data/Book_Occ3.MOV')
# try:
# # creating a folder named data
# if not os.path.exists('data/'+vid_name+'/test'):
# os.makedirs('data/'+vid_name+'/test')
# # if not created then raise error
# except OSError:
# print('Error: Creating directory of data')
# # frame
# currentframe = 0
# fil = open('data/'+vid_name+'/test.lst', "a+")
# width_p = 481
# height_p = 321
#
# while (True):
# # reading from frame
# ret, frame = cam.read()
# if ret:
# # if video is still left continue creating images
# name = 'data/' +vid_name+'/test/'+ str(currentframe) + '.jpg'
# print('Creating...' + name)
# # writing the extracted images
# frame = cv2.resize(frame, (width_p, height_p))
# cv2.imwrite(name, frame)
# fil.write('test/'+ str(currentframe) + '.jpg\n')
# # increasing counter so that it will
# # show how many frames are created
# currentframe += 1
#
# else:
# break
#
# # Release all space and windows once done
# cam.release()
# fil.close()
# cv2.destroyAllWindows()
test_dataset = BSDS_RCFLoader(root='data/'+vid_name, split="test")
print(test_dataset.filelist)
test_loader = DataLoader(
test_dataset, batch_size=args.batch_size,
num_workers=8, drop_last=True, shuffle=False)
with open('data/'+vid_name+'/test.lst', 'r') as f:
test_list = f.readlines()
test_list = [split(i.rstrip())[1] for i in test_list]
assert len(test_list) == len(test_loader), "%d vs %d" % (len(test_list), len(test_loader))
epoch = 0
print("Performing testing...")
# multiscale_test(model, test_loader, epoch=epoch, test_list=test_list,
# save_dir = join(TMP_DIR,vid_name,'initial-testing-record'))
test(model, test_loader, epoch=epoch, test_list=test_list,
save_dir = join(TMP_DIR,vid_name, 'epoch-%d-testing-record-view' % epoch))
def main():
args.cuda = True
# dataset
train_dataset = BSDSLoader(root=args.dataset, split="train")
test_dataset = BSDSLoader(root=args.dataset, split="test")
train_loader = DataLoader(
train_dataset, batch_size=args.batch_size,
num_workers=4, drop_last=True,shuffle=True)
test_loader = DataLoader(
test_dataset, batch_size=args.batch_size,
num_workers=4, drop_last=True,shuffle=False)
with open(join(args.dataset, 'test.lst'), 'r') as f:
test_list = f.readlines()
test_list = [split(i.rstrip())[1] for i in test_list]
assert len(test_list) == len(test_loader), "%d vs %d" % (len(test_list), len(test_loader))
# default hyperparameters
if args.use_cfg:
if args.pretrained and not args.small:
args.stepsize = 2
args.lr = 0.001 if args.harmonic else 0.0002
elif args.small:
args.stepsize = 6
args.lr = 0.005 if args.harmonic else 0.001
else:
args.stepsize = 4
args.lr = 0.0005 if args.harmonic else 0.0002
args.maxepoch = args.stepsize + 1
# model
model = HEDSmall(harmonic=args.harmonic) if args.small else HED(harmonic=args.harmonic)
model.cuda()
model.apply(weights_init)
if args.pretrained and not args.small:
if args.harmonic:
load_harm_vgg16pretrain(model)
else:
load_vgg16pretrain(model)
#tune lr
net_parameters_id = {}
if args.pretrained and not args.small:
for pname, p in model.named_parameters():
if pname in ['conv1_1.weight','conv1_2.weight',
'conv2_1.weight','conv2_2.weight',
'conv3_1.weight','conv3_2.weight','conv3_3.weight',
'conv4_1.weight','conv4_2.weight','conv4_3.weight',
'conv5_1.weight','conv5_2.weight','conv5_3.weight']:
print(pname, 'lr:1 de:1')
if 'conv1-5.weight' not in net_parameters_id:
net_parameters_id['conv1-5.weight'] = []
net_parameters_id['conv1-5.weight'].append(p)
elif pname in ['conv1_1.bias','conv1_2.bias',
'conv2_1.bias','conv2_2.bias',
'conv3_1.bias','conv3_2.bias','conv3_3.bias',
'conv4_1.bias','conv4_2.bias','conv4_3.bias',
'conv5_1.bias','conv5_2.bias','conv5_3.bias']:
print(pname, 'lr:2 de:0')
if 'conv1-5.bias' not in net_parameters_id:
net_parameters_id['conv1-5.bias'] = []
net_parameters_id['conv1-5.bias'].append(p)
elif pname in ['score_dsn1.weight','score_dsn2.weight','score_dsn3.weight',
'score_dsn4.weight','score_dsn5.weight']:
print(pname, 'lr:0.01 de:1')
if 'score_dsn_1-5.weight' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.weight'] = []
net_parameters_id['score_dsn_1-5.weight'].append(p)
elif pname in ['score_dsn1.bias','score_dsn2.bias','score_dsn3.bias',
'score_dsn4.bias','score_dsn5.bias']:
print(pname, 'lr:0.02 de:0')
if 'score_dsn_1-5.bias' not in net_parameters_id:
net_parameters_id['score_dsn_1-5.bias'] = []
net_parameters_id['score_dsn_1-5.bias'].append(p)
elif pname in ['score_final.weight']:
print(pname, 'lr:0.001 de:1')
if 'score_final.weight' not in net_parameters_id:
net_parameters_id['score_final.weight'] = []
net_parameters_id['score_final.weight'].append(p)
elif pname in ['score_final.bias']:
print(pname, 'lr:0.002 de:0')
if 'score_final.bias' not in net_parameters_id:
net_parameters_id['score_final.bias'] = []
net_parameters_id['score_final.bias'].append(p)
param_groups = [
{'params': net_parameters_id['conv1-5.weight'] , 'lr': args.lr*1 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['conv1-5.bias'] , 'lr': args.lr*2 , 'weight_decay': 0.},
{'params': net_parameters_id['score_dsn_1-5.weight'], 'lr': args.lr*0.01 , 'weight_decay': args.weight_decay},
{'params': net_parameters_id['score_dsn_1-5.bias'] , 'lr': args.lr*0.02 , 'weight_decay': 0.},
{'params': net_parameters_id['score_final.weight'] , 'lr': args.lr*0.001, 'weight_decay': args.weight_decay},
{'params': net_parameters_id['score_final.bias'] , 'lr': args.lr*0.002, 'weight_decay': 0.}
]
else:
net_parameters_id = {'weights': [], 'biases': []}
for pname, p in model.named_parameters():
if 'weight' in pname:
net_parameters_id['weights'].append(p)
elif 'bias' in pname:
net_parameters_id['biases'].append(p)
param_groups = [
{'params': net_parameters_id['weights'], 'weight_decay': args.weight_decay},
{'params': net_parameters_id['biases'], 'weight_decay': 0.}
]
optimizer = torch.optim.Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay)
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'"
.format(args.resume))
optimizer.load_state_dict(checkpoint['optimizer'])
args.start_epoch = checkpoint['epoch']
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# log
log = Logger(join(OUT_DIR, 'log.txt'))
sys.stdout = log
train_loss = []
train_loss_detail = []
for epoch in range(args.start_epoch, args.maxepoch):
if epoch == 0:
print("Performing initial testing...")
test(model, test_loader, epoch=epoch, test_list=test_list,
save_dir = join(OUT_DIR, 'initial-testing-record'))
tr_avg_loss, tr_detail_loss = train(
train_loader, model, optimizer, epoch,
save_dir = join(OUT_DIR, 'epoch-%d-training-record' % epoch))
test(model, test_loader, epoch=epoch, test_list=test_list,
save_dir = join(OUT_DIR, 'epoch-%d-testing-record' % epoch))
log.flush() # write log
# Save checkpoint
save_file = os.path.join(OUT_DIR, 'checkpoint_epoch{}.pth'.format(epoch))
save_checkpoint({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}, filename=save_file)
scheduler.step() # will adjust learning rate
# save train/val loss/accuracy, save every epoch in case of early stop
train_loss.append(tr_avg_loss)
train_loss_detail += tr_detail_loss