def train(model, train_loader, optimizer, criterion, epoch, log_writer, args):
train_loss = lib.Metric('train_loss')
train_accuracy = lib.Metric('train_accuracy')
model.train()
N = len(train_loader)
start_time = time.time()
for batch_idx, (data, target) in enumerate(train_loader):
lr_cur = adjust_learning_rate(args, optimizer, epoch, batch_idx, N, type=args.lr_scheduler)
if args.cuda:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss.update(loss)
train_accuracy.update(accuracy(output, target))
if (batch_idx + 1) % 20 == 0:
memory = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
used_time = time.time() - start_time
eta = used_time / (batch_idx + 1) * (N - batch_idx)
eta = str(datetime.timedelta(seconds=int(eta)))
training_state = ' '.join(['Epoch: {}', '[{} / {}]', 'eta: {}', 'lr: {:.9f}', 'max_mem: {:.0f}',
'loss: {:.3f}', 'accuracy: {:.3f}'])
training_state = training_state.format(epoch + 1, batch_idx + 1, N, eta, lr_cur, memory,
train_loss.avg.item(), 100. * train_accuracy.avg.item())
print(training_state)
if log_writer:
log_writer.add_scalar('train/loss', train_loss.avg, epoch)
log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch)
def train(epoch, net, trainloader, optimizer, npc, criterion, rlb, lr):
train_loss = AverageMeter()
net.train()
adjust_learning_rate(optimizer, lr)
for (inputs, _, indexes) in trainloader:
optimizer.zero_grad()
inputs, indexes = inputs.to(cfg.device), indexes.to(cfg.device)
features = net(inputs)
outputs = npc(features, indexes)
loss = criterion(outputs, indexes, rlb)
loss.backward()
train_loss.update(loss.item(), inputs.size(0))
optimizer.step()
return train_loss.avg
def train(model, train_sampler, train_loader, optimizer, criterion, epoch,
log_writer, args, verbose):
train_loss = lib.Metric('train_loss')
train_accuracy = lib.Metric('train_accuracy')
model.train()
if args.distributed:
train_sampler.set_epoch(epoch)
N = len(train_loader)
start_time = time.time()
for batch_idx, (data, target) in enumerate(train_loader):
lr_cur = adjust_learning_rate(args,
optimizer,
epoch,
batch_idx,
N,
type=args.lr_scheduler)
if args.cuda:
data, target = data.cuda(args.gpu, non_blocking=True), target.cuda(
args.gpu, non_blocking=True)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
dist.all_reduce(loss)
pred = output.max(1, keepdim=True)[1]
acc = pred.eq(target.view_as(pred)).float().mean()
dist.all_reduce(acc)
train_loss.update(loss * 1.0 / args.ngpus_per_node)
train_accuracy.update(acc.cpu() * 1.0 / args.ngpus_per_node)
if (batch_idx + 1) % 20 == 0 and verbose:
memory = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
used_time = time.time() - start_time
eta = used_time / (batch_idx + 1) * (N - batch_idx)
eta = str(datetime.timedelta(seconds=int(eta)))
training_state = ' '.join([
'Epoch: {}', '[{} / {}]', 'eta: {}', 'lr: {:.9f}',
'max_mem: {:.0f}', 'loss: {:.3f}', 'accuracy: {:.3f}'
])
training_state = training_state.format(
epoch + 1, batch_idx + 1, N, eta, lr_cur, memory,
train_loss.avg.item(), 100. * train_accuracy.avg.item())
print(training_state)
if log_writer and verbose:
log_writer.add_scalar('train/loss', train_loss.avg, epoch)
log_writer.add_scalar('train/accuracy', train_accuracy.avg, epoch)
def main():
global args, best_prec1
args = parser.parse_args()
# init seed
my_whole_seed = 222
random.seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.manual_seed(my_whole_seed)
torch.cuda.manual_seed_all(my_whole_seed)
torch.cuda.manual_seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(my_whole_seed)
for kk_time in range(args.seedstart, args.seedstart + 1):
args.seed = kk_time
args.result = args.result + str(args.seed)
# create model
model = models.__dict__[args.arch](low_dim=args.low_dim,
multitask=args.multitask,
showfeature=args.showfeature,
domain=args.domain,
args=args)
model = torch.nn.DataParallel(model).cuda()
print('Number of learnable params',
get_learnable_para(model) / 1000000., " M")
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
aug = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
transforms.RandomGrayscale(p=0.2),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
# aug = transforms.Compose([transforms.RandomResizedCrop(224, scale=(0.08, 1.), ratio=(3 / 4, 4 / 3)),
# transforms.RandomHorizontalFlip(p=0.5),
# get_color_distortion(s=1),
# transforms.Lambda(lambda x: gaussian_blur(x)),
# transforms.ToTensor(),
# normalize])
aug_test = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor(), normalize])
# load dataset
# import datasets.fundus_amd_syn_crossvalidation as medicaldata
import datasets.fundus_amd_syn_crossvalidation_ind as medicaldata
train_dataset = medicaldata.traindataset(root=args.data,
transform=aug,
train=True,
args=args)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4,
drop_last=True if args.multiaug else False,
worker_init_fn=random.seed(my_whole_seed))
valid_dataset = medicaldata.traindataset(root=args.data,
transform=aug_test,
train=False,
args=args)
val_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4,
worker_init_fn=random.seed(my_whole_seed))
# define lemniscate and loss function (criterion)
ndata = train_dataset.__len__()
lemniscate = LinearAverage(args.low_dim, ndata, args.nce_t,
args.nce_m).cuda()
if args.multitaskposrot:
cls_criterion = nn.CrossEntropyLoss().cuda()
else:
cls_criterion = None
if args.multitaskposrot:
print("running multi task with miccai")
criterion = BatchCriterion(1, 0.1, args.batch_size, args).cuda()
elif args.synthesis:
print("running synthesis")
criterion = BatchCriterionFour(1, 0.1, args.batch_size,
args).cuda()
elif args.multiaug:
print("running cvpr")
criterion = BatchCriterion(1, 0.1, args.batch_size, args).cuda()
else:
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
lemniscate = checkpoint['lemniscate']
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.evaluate:
knn_num = 100
auc, acc, precision, recall, f1score = kNN(args, model, lemniscate,
train_loader,
val_loader, knn_num,
args.nce_t, 2)
f = open("savemodels/result.txt", "a+")
f.write("auc: %.4f\n" % (auc))
f.write("acc: %.4f\n" % (acc))
f.write("pre: %.4f\n" % (precision))
f.write("recall: %.4f\n" % (recall))
f.write("f1score: %.4f\n" % (f1score))
f.close()
return
# mkdir result folder and tensorboard
os.makedirs(args.result, exist_ok=True)
writer = SummaryWriter("runs/" + str(args.result.split("/")[-1]))
writer.add_text('Text', str(args))
# copy code
import shutil, glob
source = glob.glob("*.py")
source += glob.glob("*/*.py")
os.makedirs(args.result + "/code_file", exist_ok=True)
for file in source:
name = file.split("/")[0]
if name == file:
shutil.copy(file, args.result + "/code_file/")
else:
os.makedirs(args.result + "/code_file/" + name, exist_ok=True)
shutil.copy(file, args.result + "/code_file/" + name)
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, args, [1000, 2000])
writer.add_scalar("lr", lr, epoch)
# # train for one epoch
loss = train(train_loader, model, lemniscate, criterion,
cls_criterion, optimizer, epoch, writer)
writer.add_scalar("train_loss", loss, epoch)
# save checkpoint
if epoch % 200 == 0 or (epoch in [1600, 1800, 2000]):
auc, acc, precision, recall, f1score = kNN(
args, model, lemniscate, train_loader, val_loader, 100,
args.nce_t, 2)
# save to txt
writer.add_scalar("test_auc", auc, epoch)
writer.add_scalar("test_acc", acc, epoch)
writer.add_scalar("test_precision", precision, epoch)
writer.add_scalar("test_recall", recall, epoch)
writer.add_scalar("test_f1score", f1score, epoch)
f = open(args.result + "/result.txt", "a+")
f.write("epoch " + str(epoch) + "\n")
f.write("auc: %.4f\n" % (auc))
f.write("acc: %.4f\n" % (acc))
f.write("pre: %.4f\n" % (precision))
f.write("recall: %.4f\n" % (recall))
f.write("f1score: %.4f\n" % (f1score))
f.close()
save_checkpoint(
{
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'lemniscate': lemniscate,
'optimizer': optimizer.state_dict(),
},
filename=args.result + "/fold" + str(args.seedstart) +
"-epoch-" + str(epoch) + ".pth.tar")
def main():
global args, best_prec1
args = parser.parse_args()
my_whole_seed = 222
random.seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.manual_seed(my_whole_seed)
torch.cuda.manual_seed_all(my_whole_seed)
torch.cuda.manual_seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(my_whole_seed)
for kk_time in range(args.seedstart, args.seedend):
args.seed = kk_time
args.result = args.result + str(args.seed)
# create model
from models.resnet_sup import resnet18, resnet50, resnet34
model = resnet18()
# pretrain_dict = torch.load("resnet18-5c106cde.pth")
# model_dict = model.state_dict()
# pretrained_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict}
# pretrained_dict.pop("fc.weight")
# pretrained_dict.pop("fc.bias")
# model_dict.update(pretrained_dict)
# model.load_state_dict(model_dict)
model = torch.nn.DataParallel(model).cuda()
model_weights = torch.load(
"exp/fundus_dr/DR_miccai_repeat0/fold0-epoch-800.pth.tar")
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in model_weights["state_dict"].items() if k in model_dict
}
pretrained_dict.pop("module.fc.weight")
pretrained_dict.pop("module.fc.bias")
model.load_state_dict(pretrained_dict, strict=False)
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
aug = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
# transforms.RandomGrayscale(p=0.2),
# transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
# aug = transforms.Compose([transforms.RandomResizedCrop(224, scale=(0.08, 1.), ratio=(3 / 4, 4 / 3)),
# transforms.RandomHorizontalFlip(p=0.5),
# get_color_distortion(s=1),
# transforms.Lambda(lambda x: gaussian_blur(x)),
# transforms.ToTensor(),
# normalize])
# aug = transforms.Compose([transforms.RandomRotation(60),
# transforms.RandomResizedCrop(224, scale=(0.6, 1.)),
# transforms.RandomGrayscale(p=0.2),
# transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# normalize])
aug_test = transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(), normalize])
# dataset
import datasets.fundus_amd_syn_crossvalidation as medicaldata
train_dataset = medicaldata.traindataset(root=args.data,
transform=aug,
train=True,
args=args)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=4,
drop_last=True if args.multiaug else False,
worker_init_fn=random.seed(my_whole_seed))
valid_dataset = medicaldata.traindataset(root=args.data,
transform=aug_test,
train=False,
args=args)
val_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4,
worker_init_fn=random.seed(my_whole_seed))
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in checkpoint["state_dict"].items()
if k in model_dict
}
pretrained_dict.pop("module.fc.weight")
pretrained_dict.pop("module.fc.bias")
# pretrained_dict = {k: v for k, v in checkpoint["net"].items() if k in model_dict}
# pretrained_dict.pop("module.conv1.weight")
# pretrained_dict.pop("module.conv1.bias")
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# mkdir result folder and tensorboard
os.makedirs(args.result, exist_ok=True)
writer = SummaryWriter("runs/" + str(args.result.split("/")[-1]))
writer.add_text('Text', str(args))
# copy code
import shutil, glob
source = glob.glob("*.py")
source += glob.glob("*/*.py")
os.makedirs(args.result + "/code_file", exist_ok=True)
for file in source:
name = file.split("/")[0]
if name == file:
shutil.copy(file, args.result + "/code_file/")
else:
os.makedirs(args.result + "/code_file/" + name, exist_ok=True)
shutil.copy(file, args.result + "/code_file/" + name)
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, args,
[500, 1000, 1500])
writer.add_scalar("lr", lr, epoch)
# # train for one epoch
loss = train(train_loader, model, criterion, optimizer)
writer.add_scalar("train_loss", loss, epoch)
gap_int = 200
if (epoch) % gap_int == 0:
loss_val, auc, acc, precision, recall, f1score = supervised_evaluation(
model, val_loader)
writer.add_scalar("test_auc", auc, epoch)
writer.add_scalar("test_acc", acc, epoch)
writer.add_scalar("test_precision", precision, epoch)
writer.add_scalar("test_recall", recall, epoch)
writer.add_scalar("test_f1score", f1score, epoch)
# save to txt
f = open(args.result + "/result.txt", "a+")
f.write("epoch " + str(epoch) + "\n")
f.write("auc: %.4f\n" % (auc))
f.write("acc: %.4f\n" % (acc))
f.write("pre: %.4f\n" % (precision))
f.write("recall: %.4f\n" % (recall))
f.write("f1score: %.4f\n" % (f1score))
f.close()
# save checkpoint
if epoch in [1000, 2000, 3000]:
save_checkpoint(
{
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
},
filename=args.result + "/epoch-" + str(epoch) + ".pth.tar")
def train(self,
nets,
criterions,
optimizers,
train_loader,
test_loader,
logs=None,
**kwargs):
import time
import os
print("manual seed : %d" % self.args.manualSeed)
for epoch in range(self.args.trainer.start_epoch,
self.args.trainer.epochs + 1):
print("epoch %d" % epoch)
start_time = time.time()
for optimizer, model_args in zip(optimizers, self.args.models):
utils.adjust_learning_rate(optimizer, epoch,
model_args.optim.gammas,
model_args.optim.schedule,
model_args.optim.args.lr)
kwargs = {} if kwargs is None else kwargs
kwargs.update({
"_trainer": self,
"_train_loader": train_loader,
"_test_loader": test_loader,
"_nets": nets,
"_criterions": criterions,
"_optimizers": optimizers,
"_epoch": epoch,
"_logs": logs,
"_args": self.args
})
# train for one epoch
self.train_on_dataset(train_loader, nets, criterions, optimizers,
epoch, logs, **kwargs)
# evaluate on validation set
self.validate_on_dataset(test_loader, nets, criterions, epoch,
logs, **kwargs)
# print log
for i, log in enumerate(logs.net):
print(
" net{0} loss :train={1:.3f}, test={2:.3f} acc :train={3:.3f}, test ={4:.3f}"
.format(i, log["epoch_log"][epoch]["train_loss"],
log["epoch_log"][epoch]["test_loss"],
log["epoch_log"][epoch]["train_accuracy"],
log["epoch_log"][epoch]["test_accuracy"]))
if epoch % self.args.trainer.saving_interval == 0:
ckpt_dir = os.path.join(self.args.trainer.base_dir,
"checkpoint")
utils.save_checkpoint(nets, optimizers, epoch, ckpt_dir)
logs.save(self.args.trainer.base_dir + r"log/")
elapsed_time = time.time() - start_time
print(" elapsed_time:{0:.3f}[sec]".format(elapsed_time))
if "_callback" in kwargs:
kwargs["_callback"](**kwargs)
return
def train(data_dir, model_path=None, vis_port=None, init=None):
# loading meta data
# -----------------------------------------------------------------------------------------------------
configs = './net/config.json'
cfg = json.load(open(configs))
# create dataset
# -----------------------------------------------------------------------------------------------------
trainloader, validloader = build_data_loader(cfg)
anchors = None
# create summary writer
if not os.path.exists(config.log_dir):
os.mkdir(config.log_dir)
summary_writer = SummaryWriter(config.log_dir)
if vis_port:
vis = visual(port=vis_port)
# start training
# -----------------------------------------------------------------------------------------------------
model = SiameseAlexNet()
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
# load model weight
# -----------------------------------------------------------------------------------------------------
start_epoch = 1
if model_path and init:
print("init training with checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
if 'model' in checkpoint.keys():
model.load_state_dict(checkpoint['model'])
else:
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
del checkpoint
torch.cuda.empty_cache()
print("inited checkpoint")
elif model_path and not init:
print("loading checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
print("loaded checkpoint")
elif not model_path and config.pretrained_model:
print("init with pretrained checkpoint %s" % config.pretrained_model +
'\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(config.pretrained_model)
# change name and load parameters
checkpoint = {
k.replace('features.features', 'featureExtract'): v
for k, v in checkpoint.items()
}
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
# freeze layers
def freeze_layers(model):
print(
'------------------------------------------------------------------------------------------------'
)
for layer in model.featureExtract[:10]:
if isinstance(layer, nn.BatchNorm2d):
layer.eval()
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.Conv2d):
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.MaxPool2d):
continue
elif isinstance(layer, nn.ReLU):
continue
else:
raise KeyError('error in fixing former 3 layers')
print("fixed layers:")
print(model.featureExtract[:10])
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
for epoch in range(start_epoch, config.epoch + 1):
train_loss = []
model.train()
if config.fix_former_3_layers:
if torch.cuda.device_count() > 1:
freeze_layers(model.module)
else:
freeze_layers(model)
loss_temp_cls = 0
loss_temp_reg = 0
for i, data in enumerate(tqdm(trainloader)):
exemplar_imgs, instance_imgs, regression_target, conf_target, delta_weight, gt = data
# conf_target (8,1125) (8,225x5)
regression_target, conf_target = regression_target.cuda(
), conf_target.cuda()
pred_score, pred_regression = model(exemplar_imgs.cuda(),
instance_imgs.cuda())
pred_conf = pred_score.reshape(
-1, 2, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
pred_offset = pred_regression.reshape(
-1, 4, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
cls_loss = rpn_cross_entropy_balance(pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors,
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
loss = cls_loss + config.lamb * reg_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip)
optimizer.step()
step = (epoch - 1) * len(trainloader) + i
summary_writer.add_scalar('train/cls_loss', cls_loss.data, step)
summary_writer.add_scalar('train/reg_loss', reg_loss.data, step)
train_loss.append(loss.detach().cpu())
loss_temp_cls += cls_loss.detach().cpu().numpy()
loss_temp_reg += reg_loss.detach().cpu().numpy()
# if vis_port:
# vis.plot_error({'rpn_cls_loss': cls_loss.detach().cpu().numpy().ravel()[0],
# 'rpn_regress_loss': reg_loss.detach().cpu().numpy().ravel()[0]}, win=0)
if (i + 1) % config.show_interval == 0:
tqdm.write(
"[epoch %2d][iter %4d] cls_loss: %.4f, reg_loss: %.4f lr: %.2e"
% (epoch, i, loss_temp_cls / config.show_interval,
loss_temp_reg / config.show_interval,
optimizer.param_groups[0]['lr']))
loss_temp_cls = 0
loss_temp_reg = 0
train_loss = np.mean(train_loss)
valid_loss = []
model.eval()
for i, data in enumerate(tqdm(validloader)):
exemplar_imgs, instance_imgs, regression_target, conf_target, delta_weight, gt = data
regression_target, conf_target = regression_target.cuda(
), conf_target.cuda()
pred_score, pred_regression = model(exemplar_imgs.cuda(),
instance_imgs.cuda())
pred_conf = pred_score.reshape(
-1, 2, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
pred_offset = pred_regression.reshape(
-1, 4, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
cls_loss = rpn_cross_entropy_balance(pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors,
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
loss = cls_loss + config.lamb * reg_loss
valid_loss.append(loss.detach().cpu())
valid_loss = np.mean(valid_loss)
print("EPOCH %d valid_loss: %.4f, train_loss: %.4f" %
(epoch, valid_loss, train_loss))
summary_writer.add_scalar('valid/loss', valid_loss,
(epoch + 1) * len(trainloader))
adjust_learning_rate(
optimizer, config.gamma
) # adjust before save, and it will be epoch+1's lr when next load
if epoch % config.save_interval == 0:
if not os.path.exists('./data/models/'):
os.makedirs("./data/models/")
save_name = "./data/models/siamrpn_{}.pth".format(epoch)
new_state_dict = model.state_dict()
if torch.cuda.device_count() > 1:
new_state_dict = OrderedDict()
for k, v in model.state_dict().items():
namekey = k[7:] # remove `module.`
new_state_dict[namekey] = v
torch.save(
{
'epoch': epoch,
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
}, save_name)
print('save model: {}'.format(save_name))
def train(data_dir, model_path=None, vis_port=None, init=None):
# loading meta data
# -----------------------------------------------------------------------------------------------------
meta_data_path = os.path.join(data_dir, "meta_data.pkl")
meta_data = pickle.load(open(meta_data_path, 'rb'))
all_videos = [x[0] for x in meta_data]
# split train/valid dataset
# -----------------------------------------------------------------------------------------------------
train_videos, valid_videos = train_test_split(all_videos,
test_size=1 -
config.train_ratio,
random_state=config.seed)
# define transforms
train_z_transforms = transforms.Compose([ToTensor()])
train_x_transforms = transforms.Compose([ToTensor()])
valid_z_transforms = transforms.Compose([ToTensor()])
valid_x_transforms = transforms.Compose([ToTensor()])
# open lmdb
db = lmdb.open(data_dir + '.lmdb', readonly=True, map_size=int(200e9))
# create dataset
# -----------------------------------------------------------------------------------------------------
train_dataset = ImagnetVIDDataset(db, train_videos, data_dir,
train_z_transforms, train_x_transforms)
anchors = train_dataset.anchors
# dic_num = {}
# ind_random = list(range(len(train_dataset)))
# import random
# random.shuffle(ind_random)
# for i in tqdm(ind_random):
# exemplar_img, instance_img, regression_target, conf_target = train_dataset[i+1000]
valid_dataset = ImagnetVIDDataset(db,
valid_videos,
data_dir,
valid_z_transforms,
valid_x_transforms,
training=False)
# create dataloader
trainloader = DataLoader(
train_dataset,
batch_size=config.train_batch_size * torch.cuda.device_count(),
shuffle=True,
pin_memory=True,
num_workers=config.train_num_workers * torch.cuda.device_count(),
drop_last=True)
validloader = DataLoader(
valid_dataset,
batch_size=config.valid_batch_size * torch.cuda.device_count(),
shuffle=False,
pin_memory=True,
num_workers=config.valid_num_workers * torch.cuda.device_count(),
drop_last=True)
# create summary writer
if not os.path.exists(config.log_dir):
os.mkdir(config.log_dir)
summary_writer = SummaryWriter(config.log_dir)
if vis_port:
vis = visual(port=vis_port)
# start training
# -----------------------------------------------------------------------------------------------------
model = SiameseAlexNet()
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
# load model weight
# -----------------------------------------------------------------------------------------------------
start_epoch = 1
if model_path and init:
print("init training with checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
if 'model' in checkpoint.keys():
model.load_state_dict(checkpoint['model'])
else:
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
del checkpoint
torch.cuda.empty_cache()
print("inited checkpoint")
elif model_path and not init:
print("loading checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
print("loaded checkpoint")
elif not model_path and config.pretrained_model:
print("init with pretrained checkpoint %s" % config.pretrained_model +
'\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(config.pretrained_model)
# change name and load parameters
checkpoint = {
k.replace('features.features', 'featureExtract'): v
for k, v in checkpoint.items()
}
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
# freeze layers
def freeze_layers(model):
print(
'------------------------------------------------------------------------------------------------'
)
for layer in model.featureExtract[:10]:
if isinstance(layer, nn.BatchNorm2d):
layer.eval()
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.Conv2d):
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.MaxPool2d):
continue
elif isinstance(layer, nn.ReLU):
continue
else:
raise KeyError('error in fixing former 3 layers')
print("fixed layers:")
print(model.featureExtract[:10])
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
for epoch in range(start_epoch, config.epoch + 1):
train_loss = []
model.train()
if config.fix_former_3_layers:
if torch.cuda.device_count() > 1:
freeze_layers(model.module)
else:
freeze_layers(model)
loss_temp_cls = 0
loss_temp_reg = 0
for i, data in enumerate(tqdm(trainloader)):
exemplar_imgs, instance_imgs, regression_target, conf_target = data
# conf_target (8,1125) (8,225x5)
regression_target, conf_target = regression_target.cuda(
), conf_target.cuda()
pred_score, pred_regression = model(exemplar_imgs.cuda(),
instance_imgs.cuda())
pred_conf = pred_score.reshape(
-1, 2, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
pred_offset = pred_regression.reshape(
-1, 4, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
cls_loss = rpn_cross_entropy_balance(pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors,
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
loss = cls_loss + config.lamb * reg_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip)
optimizer.step()
step = (epoch - 1) * len(trainloader) + i
summary_writer.add_scalar('train/cls_loss', cls_loss.data, step)
summary_writer.add_scalar('train/reg_loss', reg_loss.data, step)
train_loss.append(loss.detach().cpu())
loss_temp_cls += cls_loss.detach().cpu().numpy()
loss_temp_reg += reg_loss.detach().cpu().numpy()
# if vis_port:
# vis.plot_error({'rpn_cls_loss': cls_loss.detach().cpu().numpy().ravel()[0],
# 'rpn_regress_loss': reg_loss.detach().cpu().numpy().ravel()[0]}, win=0)
if (i + 1) % config.show_interval == 0:
tqdm.write(
"[epoch %2d][iter %4d] cls_loss: %.4f, reg_loss: %.4f lr: %.2e"
% (epoch, i, loss_temp_cls / config.show_interval,
loss_temp_reg / config.show_interval,
optimizer.param_groups[0]['lr']))
loss_temp_cls = 0
loss_temp_reg = 0
if vis_port:
anchors_show = train_dataset.anchors
exem_img = exemplar_imgs[0].cpu().numpy().transpose(
1, 2, 0)
inst_img = instance_imgs[0].cpu().numpy().transpose(
1, 2, 0)
# show detected box with max score
topk = config.show_topK
vis.plot_img(exem_img.transpose(2, 0, 1),
win=1,
name='exemple')
cls_pred = conf_target[0]
gt_box = get_topk_box(cls_pred, regression_target[0],
anchors_show)[0]
# show gt_box
img_box = add_box_img(inst_img, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=2,
name='instance')
# show anchor with max score
cls_pred = F.softmax(pred_conf, dim=2)[0, :, 1]
scores, index = torch.topk(cls_pred, k=topk)
img_box = add_box_img(inst_img, anchors_show[index.cpu()])
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=3,
name='anchor_max_score')
cls_pred = F.softmax(pred_conf, dim=2)[0, :, 1]
topk_box = get_topk_box(cls_pred,
pred_offset[0],
anchors_show,
topk=topk)
img_box = add_box_img(inst_img, topk_box)
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=4,
name='box_max_score')
# show anchor and detected box with max iou
iou = compute_iou(anchors_show, gt_box).flatten()
index = np.argsort(iou)[-topk:]
img_box = add_box_img(inst_img, anchors_show[index])
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=5,
name='anchor_max_iou')
# detected box
regress_offset = pred_offset[0].cpu().detach().numpy()
topk_offset = regress_offset[index, :]
anchors_det = anchors_show[index, :]
pred_box = box_transform_inv(anchors_det, topk_offset)
img_box = add_box_img(inst_img, pred_box)
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=6,
name='box_max_iou')
train_loss = np.mean(train_loss)
valid_loss = []
model.eval()
for i, data in enumerate(tqdm(validloader)):
exemplar_imgs, instance_imgs, regression_target, conf_target = data
regression_target, conf_target = regression_target.cuda(
), conf_target.cuda()
pred_score, pred_regression = model(exemplar_imgs.cuda(),
instance_imgs.cuda())
pred_conf = pred_score.reshape(
-1, 2, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
pred_offset = pred_regression.reshape(
-1, 4, config.anchor_num * config.score_size *
config.score_size).permute(0, 2, 1)
cls_loss = rpn_cross_entropy_balance(pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors,
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
loss = cls_loss + config.lamb * reg_loss
valid_loss.append(loss.detach().cpu())
valid_loss = np.mean(valid_loss)
print("EPOCH %d valid_loss: %.4f, train_loss: %.4f" %
(epoch, valid_loss, train_loss))
summary_writer.add_scalar('valid/loss', valid_loss,
(epoch + 1) * len(trainloader))
adjust_learning_rate(
optimizer, config.gamma
) # adjust before save, and it will be epoch+1's lr when next load
if epoch % config.save_interval == 0:
if not os.path.exists('./data/models/'):
os.makedirs("./data/models/")
save_name = "./data/models/siamrpn_{}.pth".format(epoch)
new_state_dict = model.state_dict()
if torch.cuda.device_count() > 1:
new_state_dict = OrderedDict()
for k, v in model.state_dict().items():
namekey = k[7:] # remove `module.`
new_state_dict[namekey] = v
torch.save(
{
'epoch': epoch,
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
}, save_name)
print('save model: {}'.format(save_name))
def train(round, epoch, net, trainloader, optimizer, npc, criterion,
ANs_discovery, lr, writer):
# tracking variables
train_loss = AverageMeter()
data_time = AverageMeter()
batch_time = AverageMeter()
# switch the model to train mode
net.train()
# adjust learning rate
adjust_learning_rate(optimizer, lr)
end = time.time()
start_time = datetime.now()
optimizer.zero_grad()
for batch_idx, (inputs, _, indexes) in enumerate(trainloader):
data_time.update(time.time() - end)
inputs, indexes = inputs.to(cfg.device), indexes.to(cfg.device)
features = net(inputs)
outputs = npc(features, indexes)
loss = criterion(outputs, indexes, ANs_discovery) / cfg.iter_size
loss.backward()
train_loss.update(loss.item() * cfg.iter_size, inputs.size(0))
if batch_idx % cfg.iter_size == 0:
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % cfg.display_freq != 0:
continue
writer.add_scalar('Train/Learning_Rate', lr,
epoch * len(trainloader) + batch_idx)
writer.add_scalar('Train/Loss', train_loss.val,
epoch * len(trainloader) + batch_idx)
elapsed_time, estimated_time = time_progress(batch_idx + 1,
len(trainloader),
batch_time.sum)
logger.info(
'Round: {round} Epoch: {epoch}/{tot_epochs} '
'Progress: {elps_iters}/{tot_iters} ({elps_time}/{est_time}) '
'Data: {data_time.sum:.3f} LR: {learning_rate:.5f} '
'Loss: {train_loss.val:.4f} ({train_loss.avg:.4f})'.format(
round=round,
epoch=epoch,
tot_epochs=cfg.max_epoch,
elps_iters=batch_idx,
tot_iters=len(trainloader),
elps_time=elapsed_time,
est_time=estimated_time,
data_time=data_time,
learning_rate=lr,
train_loss=train_loss))
def main():
global args, best_prec1
args = parser.parse_args()
my_whole_seed = 111
random.seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.manual_seed(my_whole_seed)
torch.cuda.manual_seed_all(my_whole_seed)
torch.cuda.manual_seed(my_whole_seed)
np.random.seed(my_whole_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(my_whole_seed)
for kk_time in range(args.seedstart, args.seedend):
args.seed = kk_time
args.result = args.result + str(args.seed)
# create model
model = models.__dict__[args.arch](low_dim=args.low_dim,
multitask=args.multitask,
showfeature=args.showfeature,
args=args)
#
# from models.Gresnet import ResNet18
# model = ResNet18(low_dim=args.low_dim, multitask=args.multitask)
model = torch.nn.DataParallel(model).cuda()
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
aug = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
transforms.RandomGrayscale(p=0.2),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
# aug = transforms.Compose([transforms.RandomResizedCrop(224, scale=(0.08, 1.), ratio=(3 / 4, 4 / 3)),
# transforms.RandomHorizontalFlip(p=0.5),
# get_color_distortion(s=1),
# transforms.Lambda(lambda x: gaussian_blur(x)),
# transforms.ToTensor(),
# normalize])
# aug = transforms.Compose([transforms.RandomRotation(60),
# transforms.RandomResizedCrop(224, scale=(0.6, 1.)),
# transforms.RandomGrayscale(p=0.2),
# transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# normalize])
aug_test = transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(), normalize])
# dataset
import datasets.fundus_kaggle_dr as medicaldata
train_dataset = medicaldata.traindataset(root=args.data,
transform=aug,
train=True,
args=args)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=8,
drop_last=True if args.multiaug else False,
worker_init_fn=random.seed(my_whole_seed))
valid_dataset = medicaldata.traindataset(root=args.data,
transform=aug_test,
train=False,
test_type="amd",
args=args)
val_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=8,
worker_init_fn=random.seed(my_whole_seed))
valid_dataset_gon = medicaldata.traindataset(root=args.data,
transform=aug_test,
train=False,
test_type="gon",
args=args)
val_loader_gon = torch.utils.data.DataLoader(
valid_dataset_gon,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=8,
worker_init_fn=random.seed(my_whole_seed))
valid_dataset_pm = medicaldata.traindataset(root=args.data,
transform=aug_test,
train=False,
test_type="pm",
args=args)
val_loader_pm = torch.utils.data.DataLoader(
valid_dataset_pm,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=8,
worker_init_fn=random.seed(my_whole_seed))
# define lemniscate and loss function (criterion)
ndata = train_dataset.__len__()
lemniscate = LinearAverage(args.low_dim, ndata, args.nce_t,
args.nce_m).cuda()
local_lemniscate = None
if args.multitaskposrot:
print("running multi task with positive")
criterion = BatchCriterionRot(1, 0.1, args.batch_size, args).cuda()
elif args.domain:
print("running domain with four types--unify ")
from lib.BatchAverageFour import BatchCriterionFour
# criterion = BatchCriterionTriple(1, 0.1, args.batch_size, args).cuda()
criterion = BatchCriterionFour(1, 0.1, args.batch_size,
args).cuda()
elif args.multiaug:
print("running multi task")
criterion = BatchCriterion(1, 0.1, args.batch_size, args).cuda()
else:
criterion = nn.CrossEntropyLoss().cuda()
if args.multitask:
cls_criterion = nn.CrossEntropyLoss().cuda()
else:
cls_criterion = None
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
lemniscate = checkpoint['lemniscate']
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.evaluate:
knn_num = 100
auc, acc, precision, recall, f1score = kNN(args, model, lemniscate,
train_loader,
val_loader, knn_num,
args.nce_t, 2)
return
# mkdir result folder and tensorboard
os.makedirs(args.result, exist_ok=True)
writer = SummaryWriter("runs/" + str(args.result.split("/")[-1]))
writer.add_text('Text', str(args))
# copy code
import shutil, glob
source = glob.glob("*.py")
source += glob.glob("*/*.py")
os.makedirs(args.result + "/code_file", exist_ok=True)
for file in source:
name = file.split("/")[0]
if name == file:
shutil.copy(file, args.result + "/code_file/")
else:
os.makedirs(args.result + "/code_file/" + name, exist_ok=True)
shutil.copy(file, args.result + "/code_file/" + name)
for epoch in range(args.start_epoch, args.epochs):
lr = adjust_learning_rate(optimizer, epoch, args, [100, 200])
writer.add_scalar("lr", lr, epoch)
# # train for one epoch
loss = train(train_loader, model, lemniscate, local_lemniscate,
criterion, cls_criterion, optimizer, epoch, writer)
writer.add_scalar("train_loss", loss, epoch)
# gap_int = 10
# if (epoch) % gap_int == 0:
# knn_num = 100
# auc, acc, precision, recall, f1score = kNN(args, model, lemniscate, train_loader, val_loader, knn_num, args.nce_t, 2)
# writer.add_scalar("test_auc", auc, epoch)
# writer.add_scalar("test_acc", acc, epoch)
# writer.add_scalar("test_precision", precision, epoch)
# writer.add_scalar("test_recall", recall, epoch)
# writer.add_scalar("test_f1score", f1score, epoch)
#
# auc, acc, precision, recall, f1score = kNN(args, model, lemniscate, train_loader, val_loader_gon,
# knn_num, args.nce_t, 2)
# writer.add_scalar("gon/test_auc", auc, epoch)
# writer.add_scalar("gon/test_acc", acc, epoch)
# writer.add_scalar("gon/test_precision", precision, epoch)
# writer.add_scalar("gon/test_recall", recall, epoch)
# writer.add_scalar("gon/test_f1score", f1score, epoch)
# auc, acc, precision, recall, f1score = kNN(args, model, lemniscate, train_loader, val_loader_pm,
# knn_num, args.nce_t, 2)
# writer.add_scalar("pm/test_auc", auc, epoch)
# writer.add_scalar("pm/test_acc", acc, epoch)
# writer.add_scalar("pm/test_precision", precision, epoch)
# writer.add_scalar("pm/test_recall", recall, epoch)
# writer.add_scalar("pm/test_f1score", f1score, epoch)
# save checkpoint
save_checkpoint(
{
'epoch': epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'lemniscate': lemniscate,
'optimizer': optimizer.state_dict(),
},
filename=args.result + "/fold" + str(args.seedstart) +
"-epoch-" + str(epoch) + ".pth.tar")
def train(data_dir, model_path=None, vis_port=None, init=None):
# loading meta data
# -----------------------------------------------------------------------------------------------------
meta_data_path = os.path.join(data_dir, "meta_data.pkl")
meta_data = pickle.load(
open(meta_data_path, 'rb')
) # meta_data[0] = ('ILSVRC2015_train_00001000', {0: ['000000', '000001', '000002',...]}),
all_videos = [x[0] for x in meta_data]
# split train/valid dataset
# -----------------------------------------------------------------------------------------------------
train_videos, valid_videos = train_test_split(all_videos,
test_size=1 -
config.train_ratio,
random_state=config.seed)
print("after split:train_videos {0},valid_videos {1}".format(
len(train_videos), len(valid_videos)))
# define transforms
train_z_transforms = transforms.Compose([ToTensor()])
train_x_transforms = transforms.Compose([ToTensor()])
valid_z_transforms = transforms.Compose([ToTensor()])
valid_x_transforms = transforms.Compose([ToTensor()])
# open lmdb
# db = lmdb.open(data_dir + '_lmdb', readonly=True, map_size=int(1024*1024*1024)) # 200e9,单位Byte
db_path = data_dir + '_Lmdb'
# create dataset
# -----------------------------------------------------------------------------------------------------
train_dataset = ImagnetVIDDataset(db_path, train_videos, data_dir,
train_z_transforms, train_x_transforms)
# test __getitem__
# train_dataset.__getitem__(1)
# exit(0)
anchors = train_dataset.anchors # (1805,4) = (19*19*5,4)
# dic_num = {}
# ind_random = list(range(len(train_dataset)))
# import random
# random.shuffle(ind_random)
# for i in tqdm(ind_random):
# exemplar_img, instance_img, regression_target, conf_target = train_dataset[i+1000]
valid_dataset = ImagnetVIDDataset(db_path,
valid_videos,
data_dir,
valid_z_transforms,
valid_x_transforms,
training=False)
# create dataloader
trainloader = DataLoader(train_dataset,
batch_size=config.train_batch_size,
shuffle=True,
pin_memory=True,
num_workers=config.train_num_workers,
drop_last=True)
validloader = DataLoader(valid_dataset,
batch_size=config.valid_batch_size,
shuffle=False,
pin_memory=True,
num_workers=config.valid_num_workers,
drop_last=True)
# create summary writer
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
summary_writer = SummaryWriter(config.log_dir)
if vis_port:
vis = visual(port=vis_port)
# start training
# -----------------------------------------------------------------------------------------------------
# model = SiameseAlexNet()
model = SiamFPN50()
model.init_weights() # 权重初始化
if config.CUDA:
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
# load model weight
# -----------------------------------------------------------------------------------------------------
start_epoch = 1
if model_path and init:
print("init training with checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
if 'model' in checkpoint.keys():
model.load_state_dict(checkpoint['model'])
else:
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
del checkpoint
torch.cuda.empty_cache()
print("inited checkpoint")
elif model_path and not init:
print("loading checkpoint %s" % model_path + '\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(model_path)
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
print("loaded checkpoint")
elif not model_path and config.pretrained_model:
print("init with pretrained checkpoint %s" % config.pretrained_model +
'\n')
print(
'------------------------------------------------------------------------------------------------ \n'
)
checkpoint = torch.load(config.pretrained_model)
# change name and load parameters
checkpoint = {
k.replace('features.features', 'featureExtract'): v
for k, v in checkpoint.items()
}
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
# layers
def freeze_layers(model):
print(
'------------------------------------------------------------------------------------------------'
)
for layer in model.featureExtract[:10]:
if isinstance(layer, nn.BatchNorm2d):
layer.eval()
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.Conv2d):
for k, v in layer.named_parameters():
v.requires_grad = False
elif isinstance(layer, nn.MaxPool2d):
continue
elif isinstance(layer, nn.ReLU):
continue
else:
raise KeyError('error in fixing former 3 layers')
# print("fixed layers:")
# print(model.featureExtract[:10])
'''
fixed layers:
Sequential(
(0): Conv2d(3, 96, kernel_size=(11, 11), stride=(2, 2))
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): ReLU(inplace)
(4): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1))
(5): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(7): ReLU(inplace)
(8): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1))
(9): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
'''
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model) # 前提是model已经.cuda()了
# if isinstance(model,torch.nn.DataParallel): # 多GPU训练, AttributeError: ‘DataParallel’ object has no attribute ‘xxxx’
# model = model.module
for epoch in range(start_epoch, config.EPOCH + 1):
train_loss = []
model.train()
if config.fix_former_3_layers: # 暂时去掉 # 固定前三层卷积的v.requires_grad = False
if torch.cuda.device_count() > 1:
freeze_layers(model.module)
else:
freeze_layers(model)
loss_temp_cls = 0
loss_temp_reg = 0
loss_temp = 0
# for i, data in enumerate(tqdm(trainloader)): # can't pickle Transaction objects
for k, data in enumerate(tqdm(trainloader)): # 这里有问题,loader没有遍历完就跳走了
# print("done")
# return
# (8,3,127,127)\(8,3,271,271)\(8,1805,4)\(8,1805)
# 8为batch_size,1445 = 19 * 19 * 5,5 = anchors_num
# exemplar_imgs, instance_imgs, regression_target, conf_target = data
exemplar_imgs, instance_imgs, regression_targets, conf_targets = data
# conf_target (8,1125) (8,225x5)
if config.CUDA:
# 这里有问题,regression_targets是list,不能直接使用.cuda(),后面考虑将其压缩成(N,4)的形式
# regression_targets, conf_targets = torch.tensor(regression_targets).cuda(), torch.tensor(conf_targets).cuda()
exemplar_imgs, instance_imgs = exemplar_imgs.cuda(
), instance_imgs.cuda()
# # 基于一层的损失计算
# # (8,10,19,19)\(8,20,19,19)
# pred_score, pred_regression = model(exemplar_imgs, instance_imgs)
# # (8,1805,2)
# pred_conf = pred_score.reshape(-1, 2, config.anchor_num * config.score_size * config.score_size).permute(0,2,1)
# # (8,1805,4)
# pred_offset = pred_regression.reshape(-1, 4,config.anchor_num * config.score_size * config.score_size).permute(0,2,1)
# cls_loss = rpn_cross_entropy_balance(pred_conf, conf_target, config.num_pos, config.num_neg, anchors,
# ohem_pos=config.ohem_pos, ohem_neg=config.ohem_neg)
# reg_loss = rpn_smoothL1(pred_offset, regression_target, conf_target, config.num_pos, ohem=config.ohem_reg)
# loss = cls_loss + config.lamb * reg_loss
# 基于金字塔模型的损失计算
# try:
# output = model(input)
# except RuntimeError as exception:
# if "out of memory" in str(exception):
# print("WARNING: out of memory")
# if hasattr(torch.cuda, 'empty_cache'):
# torch.cuda.empty_cache()
# else:
# raise exception
pred_scores, pred_regressions = model(exemplar_imgs, instance_imgs)
# FEATURE_MAP_SIZE、FPN_ANCHOR_NUM
'''
when batch_size = 2, anchor_num = 3
torch.Size([N, 6, 37, 37])
torch.Size([N, 6, 19, 19])
torch.Size([N, 6, 10, 10])
torch.Size([N, 6, 6, 6])
torch.Size([N, 12, 37, 37])
torch.Size([N, 12, 19, 19])
torch.Size([N, 12, 10, 10])
torch.Size([N, 12, 6, 6])
'''
loss = 0
cls_loss_sum = 0
reg_loss_sum = 0
for i in range(len(pred_scores)):
if i != 1:
continue # 这里先只考虑一层（19*19）的损失,其余的暂时不考虑
pred_score = pred_scores[i]
pred_regression = pred_regressions[i]
anchors_num = config.FPN_ANCHOR_NUM * config.FEATURE_MAP_SIZE[
i] * config.FEATURE_MAP_SIZE[i]
pred_conf = pred_score.reshape(-1, 2,
anchors_num).permute(0, 2, 1)
pred_offset = pred_regression.reshape(-1, 4,
anchors_num).permute(
0, 2, 1)
conf_target = conf_targets[i]
regression_target = regression_targets[i].type(
torch.FloatTensor) # pred_offset是float类型
if config.CUDA:
conf_target = conf_target.cuda()
regression_target = regression_target.cuda()
# 二分类损失计算(交叉熵)
cls_loss = rpn_cross_entropy_balance(pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors[i],
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
# 回归损失计算(Smooth L1) # 这里应该有问题,回归损失的值为0
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
_loss = cls_loss + reg_loss * config.lamb_reg # config.lamb_cls
loss += _loss # 这里四层的loss先直接加起来,后面考虑加权处理
# 用于tensorboard展示cls_loss\reg_loss 原样输出
cls_loss_sum = cls_loss_sum + cls_loss
reg_loss_sum = reg_loss_sum + reg_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip)
optimizer.step()
step = (epoch - 1) * len(trainloader) + k
summary_writer.add_scalar('train/cls_loss', cls_loss_sum.data,
step)
summary_writer.add_scalar('train/reg_loss', reg_loss_sum.data,
step)
loss = loss.detach().cpu()
train_loss.append(loss)
loss_temp_cls += cls_loss_sum.detach().cpu().numpy()
loss_temp_reg += reg_loss_sum.detach().cpu().numpy()
loss_temp += loss.numpy()
# if vis_port:
# vis.plot_error({'rpn_cls_loss': cls_loss.detach().cpu().numpy().ravel()[0],
# 'rpn_regress_loss': reg_loss.detach().cpu().numpy().ravel()[0]}, win=0)
# print("Epoch {0} batch {1} training_loss:{2}".format(epoch, k+1, loss))
if (k + 1) % config.show_interval == 0:
tqdm.write(
"[epoch %2d][iter %4d] loss: %.4f, cls_loss: %.4f, reg_loss: %.4f lr: %.2e"
% (epoch, k + 1, loss_temp / config.show_interval,
loss_temp_cls / config.show_interval, loss_temp_reg /
config.show_interval, optimizer.param_groups[0]['lr']))
loss_temp_cls = 0
loss_temp_reg = 0
loss_temp = 0
# 视觉展示
if vis_port:
anchors_show = train_dataset.anchors
exem_img = exemplar_imgs[0].cpu().numpy().transpose(
1, 2, 0)
inst_img = instance_imgs[0].cpu().numpy().transpose(
1, 2, 0)
# show detected box with max score
topk = config.show_topK
vis.plot_img(exem_img.transpose(2, 0, 1),
win=1,
name='exemple')
cls_pred = conf_target[0]
gt_box = get_topk_box(cls_pred, regression_target[0],
anchors_show)[0]
# show gt_box
img_box = add_box_img(inst_img, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=2,
name='instance')
# show anchor with max score
cls_pred = F.softmax(pred_conf, dim=2)[0, :, 1]
scores, index = torch.topk(cls_pred, k=topk)
img_box = add_box_img(inst_img, anchors_show[index.cpu()])
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=3,
name='anchor_max_score')
cls_pred = F.softmax(pred_conf, dim=2)[0, :, 1]
topk_box = get_topk_box(cls_pred,
pred_offset[0],
anchors_show,
topk=topk)
img_box = add_box_img(inst_img, topk_box)
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=4,
name='box_max_score')
# show anchor and detected box with max iou
iou = compute_iou(anchors_show, gt_box).flatten()
index = np.argsort(iou)[-topk:]
img_box = add_box_img(inst_img, anchors_show[index])
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=5,
name='anchor_max_iou')
# detected box
regress_offset = pred_offset[0].cpu().detach().numpy()
topk_offset = regress_offset[index, :]
anchors_det = anchors_show[index, :]
pred_box = box_transform_inv(anchors_det, topk_offset)
img_box = add_box_img(inst_img, pred_box)
img_box = add_box_img(img_box, gt_box, color=(255, 0, 0))
vis.plot_img(img_box.transpose(2, 0, 1),
win=6,
name='box_max_iou')
train_loss = np.mean(train_loss)
# print("done")
# exit(0)
# 验证
valid_loss = []
# 不计算梯度,节约显存(验证阶段等价于测试,仅计算结果)
with torch.no_grad():
model.eval()
# for i, data in enumerate(tqdm(validloader)):
for i, data in enumerate(tqdm(validloader)):
exemplar_imgs, instance_imgs, regression_targets, conf_targets = data
if config.CUDA:
exemplar_imgs, instance_imgs = exemplar_imgs.cuda(
), instance_imgs.cuda()
pred_scores, pred_regressions = model(exemplar_imgs,
instance_imgs)
loss = 0
for i in range(len(pred_scores)):
if i != 1:
continue # 这里先只考虑一层（19*19）的损失,其余的暂时不考虑
pred_score = pred_scores[i]
pred_regression = pred_regressions[i]
anchors_num = config.FPN_ANCHOR_NUM * config.FEATURE_MAP_SIZE[
i] * config.FEATURE_MAP_SIZE[i]
pred_conf = pred_score.reshape(-1, 2, anchors_num).permute(
0, 2, 1)
pred_offset = pred_regression.reshape(-1, 4,
anchors_num).permute(
0, 2, 1)
conf_target = conf_targets[i]
regression_target = regression_targets[i].type(
torch.FloatTensor) # pred_offset是float类型
if config.CUDA:
conf_target = conf_target.cuda()
regression_target = regression_target.cuda()
# 二分类损失计算(交叉熵)
cls_loss = rpn_cross_entropy_balance(
pred_conf,
conf_target,
config.num_pos,
config.num_neg,
anchors[i],
ohem_pos=config.ohem_pos,
ohem_neg=config.ohem_neg)
# 回归损失计算(Smooth L1) # 这里应该有问题,回归损失的值为0
reg_loss = rpn_smoothL1(pred_offset,
regression_target,
conf_target,
config.num_pos,
ohem=config.ohem_reg)
_loss = cls_loss * config.lamb_cls + reg_loss * config.lamb_reg
loss += _loss # 这里四层的loss先直接加起来,后面考虑加权处理
valid_loss.append(loss.detach().cpu())
valid_loss = np.mean(valid_loss)
print("EPOCH %d valid_loss: %.4f, train_loss: %.4f" %
(epoch, valid_loss, train_loss))
summary_writer.add_scalar('valid/loss', valid_loss,
(epoch + 1) * len(trainloader))
# 调整学习率
adjust_learning_rate(
optimizer, config.gamma
) # adjust before save, and it will be epoch+1's lr when next load
# 保存训练好的模型
if epoch % config.save_interval == 0:
if not os.path.exists('./data/models/'):
os.makedirs("./data/models/")
save_name = "./data/models/otb_siamfpn_{}_trainloss_{:.4f}_validloss_{:.4f}.pth".format(
epoch, train_loss, valid_loss)
new_state_dict = model.state_dict()
if torch.cuda.device_count() > 1:
new_state_dict = OrderedDict()
for k, v in model.state_dict().items():
namekey = k[7:] # remove `module.`
new_state_dict[namekey] = v
torch.save(
{
'epoch': epoch,
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
}, save_name)
print('save model: {}'.format(save_name))
# 清空缓存
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
