def _make_model(self):
# prepare network
self.logger.info("Creating graph and optimizer...")
model = get_model('train', self.joint_num)
model = DataParallel(model).cuda()
optimizer = self.get_optimizer(model)
if cfg.continue_train:
start_epoch, model, optimizer = self.load_model(model, optimizer)
else:
start_epoch = 0
model.train()
self.start_epoch = start_epoch
self.model = model
self.optimizer = optimizer
class SSRunner(object):
def __init__(self, config):
self.config = config
# Data
self.dataset_ss_train, _, self.dataset_ss_val = DatasetUtil.get_dataset_by_type(
DatasetUtil.dataset_type_ss,
self.config.ss_size,
is_balance=self.config.is_balance_data,
data_root=self.config.data_root_path,
train_label_path=self.config.label_path,
max_size=self.config.max_size)
self.data_loader_ss_train = DataLoader(self.dataset_ss_train,
self.config.ss_batch_size,
True,
num_workers=16,
drop_last=True)
self.data_loader_ss_val = DataLoader(self.dataset_ss_val,
self.config.ss_batch_size,
False,
num_workers=16,
drop_last=True)
# Model
self.net = self.config.Net(num_classes=self.config.ss_num_classes,
output_stride=self.config.output_stride,
arch=self.config.arch)
if self.config.only_train_ss:
self.net = BalancedDataParallel(0, self.net, dim=0).cuda()
else:
self.net = DataParallel(self.net).cuda()
pass
cudnn.benchmark = True
# Optimize
self.optimizer = optim.SGD(params=[
{
'params': self.net.module.model.backbone.parameters(),
'lr': self.config.ss_lr
},
{
'params': self.net.module.model.classifier.parameters(),
'lr': self.config.ss_lr * 10
},
],
lr=self.config.ss_lr,
momentum=0.9,
weight_decay=1e-4)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.config.ss_milestones, gamma=0.1)
# Loss
self.ce_loss = nn.CrossEntropyLoss(ignore_index=255,
reduction='mean').cuda()
pass
def train_ss(self, start_epoch=0, model_file_name=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
# self.eval_ss(epoch=0)
best_iou = 0.0
for epoch in range(start_epoch, self.config.ss_epoch_num):
Tools.print()
Tools.print('Epoch:{:2d}, lr={:.6f} lr2={:.6f}'.format(
epoch, self.optimizer.param_groups[0]['lr'],
self.optimizer.param_groups[1]['lr']),
txt_path=self.config.ss_save_result_txt)
###########################################################################
# 1 训练模型
all_loss = 0.0
self.net.train()
if self.config.is_balance_data:
self.dataset_ss_train.reset()
pass
for i, (inputs,
labels) in tqdm(enumerate(self.data_loader_ss_train),
total=len(self.data_loader_ss_train)):
inputs, labels = inputs.float().cuda(), labels.long().cuda()
self.optimizer.zero_grad()
result = self.net(inputs)
loss = self.ce_loss(result, labels)
loss.backward()
self.optimizer.step()
all_loss += loss.item()
if (i + 1) % (len(self.data_loader_ss_train) // 10) == 0:
score = self.eval_ss(epoch=epoch)
mean_iou = score["Mean IoU"]
if mean_iou > best_iou:
best_iou = mean_iou
save_file_name = Tools.new_dir(
os.path.join(
self.config.ss_model_dir,
"ss_{}_{}_{}.pth".format(epoch, i, best_iou)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
pass
pass
self.scheduler.step()
###########################################################################
Tools.print("[E:{:3d}/{:3d}] ss loss:{:.4f}".format(
epoch, self.config.ss_epoch_num,
all_loss / len(self.data_loader_ss_train)),
txt_path=self.config.ss_save_result_txt)
###########################################################################
# 2 保存模型
if epoch % self.config.ss_save_epoch_freq == 0:
Tools.print()
save_file_name = Tools.new_dir(
os.path.join(self.config.ss_model_dir,
"ss_{}.pth".format(epoch)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
pass
###########################################################################
###########################################################################
# 3 评估模型
if epoch % self.config.ss_eval_epoch_freq == 0:
score = self.eval_ss(epoch=epoch)
pass
###########################################################################
pass
# Final Save
Tools.print()
save_file_name = Tools.new_dir(
os.path.join(self.config.ss_model_dir,
"ss_final_{}.pth".format(self.config.ss_epoch_num)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
self.eval_ss(epoch=self.config.ss_epoch_num)
pass
def eval_ss(self, epoch=0, model_file_name=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
self.net.eval()
metrics = StreamSegMetrics(self.config.ss_num_classes)
with torch.no_grad():
for i, (inputs,
labels) in tqdm(enumerate(self.data_loader_ss_val),
total=len(self.data_loader_ss_val)):
inputs = inputs.float().cuda()
labels = labels.long().cuda()
outputs = self.net(inputs)
preds = outputs.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
pass
pass
score = metrics.get_results()
Tools.print("{} {}".format(epoch, metrics.to_str(score)),
txt_path=self.config.ss_save_result_txt)
return score
def inference_ss(self,
model_file_name=None,
data_loader=None,
save_path=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
final_save_path = Tools.new_dir("{}_final".format(save_path))
self.net.eval()
metrics = StreamSegMetrics(self.config.ss_num_classes)
with torch.no_grad():
for i, (inputs, labels,
image_info_list) in tqdm(enumerate(data_loader),
total=len(data_loader)):
assert len(image_info_list) == 1
# 标签
max_size = 1000
size = Image.open(image_info_list[0]).size
basename = os.path.basename(image_info_list[0])
final_name = os.path.join(final_save_path,
basename.replace(".JPEG", ".png"))
if os.path.exists(final_name):
continue
if size[0] < max_size and size[1] < max_size:
targets = F.interpolate(torch.unsqueeze(
labels[0].float().cuda(), dim=0),
size=(size[1], size[0]),
mode="nearest").detach().cpu()
else:
targets = F.interpolate(torch.unsqueeze(labels[0].float(),
dim=0),
size=(size[1], size[0]),
mode="nearest")
targets = targets[0].long().numpy()
# 预测
outputs = 0
for input_index, input_one in enumerate(inputs):
output_one = self.net(input_one.float().cuda())
if size[0] < max_size and size[1] < max_size:
outputs += F.interpolate(
output_one,
size=(size[1], size[0]),
mode="bilinear",
align_corners=False).detach().cpu()
else:
outputs += F.interpolate(output_one.detach().cpu(),
size=(size[1], size[0]),
mode="bilinear",
align_corners=False)
pass
pass
outputs = outputs / len(inputs)
preds = outputs.max(dim=1)[1].numpy()
# 计算
metrics.update(targets, preds)
if save_path:
Image.open(image_info_list[0]).save(
os.path.join(save_path, basename))
DataUtil.gray_to_color(
np.asarray(targets[0], dtype=np.uint8)).save(
os.path.join(save_path,
basename.replace(".JPEG", "_l.png")))
DataUtil.gray_to_color(np.asarray(
preds[0], dtype=np.uint8)).save(
os.path.join(save_path,
basename.replace(".JPEG", ".png")))
Image.fromarray(np.asarray(
preds[0], dtype=np.uint8)).save(final_name)
pass
pass
pass
score = metrics.get_results()
Tools.print("{}".format(metrics.to_str(score)),
txt_path=self.config.ss_save_result_txt)
return score
def load_model(self, model_file_name):
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
checkpoint = torch.load(model_file_name)
if len(os.environ["CUDA_VISIBLE_DEVICES"].split(",")) == 1:
# checkpoint = {key.replace("module.", ""): checkpoint[key] for key in checkpoint}
pass
self.net.load_state_dict(checkpoint, strict=True)
Tools.print("Restore from {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
pass
def stat(self):
stat(self.net, (3, self.config.ss_size, self.config.ss_size))
pass
pass
pre_run = np.random.randint((eo + 1 - mile_stone) *
track_step + 1)
second = int(pre_run)
third = second + track_step
print_flag = i < epoch_outer or i % 10 == 1
trk_state = model.module.init_state(batch_size=batch_size)
data = to_device(data, device=device)
model.eval()
for j in range(second):
frame_data = get_frame_data(data, j)
with torch.no_grad():
out, trk_state = model(frame_data, state=trk_state)
trk_state = model.module.map_gt_ids(out,
frame_data,
state=trk_state)
model.train()
opt.zero_grad()
losses = []
for j in range(second, third):
frame_data = get_frame_data(data, j)
# print(model._cs_pos[0].weight.mean(), 'before')
output, trk_state = model(frame_data, state=trk_state)
# input()
trk_state = model.module.map_gt_ids(output,
frame_data,
state=trk_state)
loss_j = model.module.loss(output,
frame_data,
verbose=print_flag)
losses.append(loss_j)
# print(model._cs_pos[0].weight.mean(), 'after')
def train(args, pt_dir, chkpt_path, trainloader, devloader, writer, logger, hp,
hp_str):
model = get_SLOCountNet(hp).cuda()
print("FOV: {}", model.get_fov(hp.features.n_fft))
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("N_parameters : {}".format(params))
model = DataParallel(model)
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=hp.train.adam)
else:
raise Exception("%s optimizer not supported" % hp.train.optimizer)
epoch = 0
best_loss = np.inf
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
epoch = checkpoint['step']
# will use new given hparams.
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams is different from checkpoint.")
else:
logger.info("Starting new training run")
try:
for epoch in range(epoch, hp.train.n_epochs):
vad_scores = Binarymetrics.BinaryMeter() # activity scores
vod_scores = Binarymetrics.BinaryMeter() # overlap scores
count_scores = Binarymetrics.MultiMeter() # Countnet scores
model.train()
tot_loss = 0
with tqdm(trainloader) as t:
t.set_description("Epoch: {}".format(epoch))
for count, batch in enumerate(trainloader):
features, labels = batch
features = features.cuda()
labels = labels.cuda()
preds = model(features)
loss = criterion(preds, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# compute proper metrics for VAD
loss = loss.item()
if loss > 1e8 or math.isnan(loss): # check if exploded
logger.error("Loss exploded to %.02f at step %d!" %
(loss, epoch))
raise Exception("Loss exploded")
VADpreds = torch.sum(torch.exp(preds[:, 1:5, :]),
dim=1).unsqueeze(1)
VADlabels = torch.sum(labels[:, 1:5, :],
dim=1).unsqueeze(1)
vad_scores.update(VADpreds, VADlabels)
VODpreds = torch.sum(torch.exp(preds[:, 2:5, :]),
dim=1).unsqueeze(1)
VODlabels = torch.sum(labels[:, 2:5, :],
dim=1).unsqueeze(1)
vod_scores.update(VODpreds, VODlabels)
count_scores.update(
torch.argmax(torch.exp(preds), 1).unsqueeze(1),
torch.argmax(labels, 1).unsqueeze(1))
tot_loss += loss
vad_fa = vad_scores.get_fa().item()
vad_miss = vad_scores.get_miss().item()
vad_precision = vad_scores.get_precision().item()
vad_recall = vad_scores.get_recall().item()
vad_matt = vad_scores.get_matt().item()
vad_f1 = vad_scores.get_f1().item()
vad_tp = vad_scores.tp.item()
vad_tn = vad_scores.tn.item()
vad_fp = vad_scores.fp.item()
vad_fn = vad_scores.fn.item()
vod_fa = vod_scores.get_fa().item()
vod_miss = vod_scores.get_miss().item()
vod_precision = vod_scores.get_precision().item()
vod_recall = vod_scores.get_recall().item()
vod_matt = vod_scores.get_matt().item()
vod_f1 = vod_scores.get_f1().item()
vod_tp = vod_scores.tp.item()
vod_tn = vod_scores.tn.item()
vod_fp = vod_scores.fp.item()
vod_fn = vod_scores.fn.item()
count_fa = count_scores.get_accuracy().item()
count_miss = count_scores.get_miss().item()
count_precision = count_scores.get_precision().item()
count_recall = count_scores.get_recall().item()
count_matt = count_scores.get_matt().item()
count_f1 = count_scores.get_f1().item()
count_tp = count_scores.get_tp().item()
count_tn = count_scores.get_tn().item()
count_fp = count_scores.get_fp().item()
count_fn = count_scores.get_fn().item()
t.set_postfix(loss=tot_loss / (count + 1),
vad_miss=vad_miss,
vad_fa=vad_fa,
vad_prec=vad_precision,
vad_recall=vad_recall,
vad_matt=vad_matt,
vad_f1=vad_f1,
vod_miss=vod_miss,
vod_fa=vod_fa,
vod_prec=vod_precision,
vod_recall=vod_recall,
vod_matt=vod_matt,
vod_f1=vod_f1,
count_miss=count_miss,
count_fa=count_fa,
count_prec=count_precision,
count_recall=count_recall,
count_matt=count_matt,
count_f1=count_f1)
t.update()
writer.log_metrics("train_vad", loss, vad_fa, vad_miss, vad_recall,
vad_precision, vad_f1, vad_matt, vad_tp, vad_tn,
vad_fp, vad_fn, epoch)
writer.log_metrics("train_vod", loss, vod_fa, vod_miss, vod_recall,
vod_precision, vod_f1, vod_matt, vod_tp, vod_tn,
vod_fp, vod_fn, epoch)
writer.log_metrics("train_count", loss, count_fa, count_miss,
count_recall, count_precision, count_f1,
count_matt, count_tp, count_tn, count_fp,
count_fn, epoch)
# end epoch save model and validate it
val_loss = validate(hp, model, devloader, writer, epoch)
if hp.train.save_best == 0:
save_path = os.path.join(pt_dir, 'chkpt_%d.pt' % epoch)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': epoch,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
else:
if val_loss < best_loss: # save only when best
best_loss = val_loss
save_path = os.path.join(pt_dir, 'chkpt_%d.pt' % epoch)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': epoch,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
return best_loss
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
return data, label
device = "cuda"
data = torch.load("corpus.pt")
ds = DS(data['train']['src'], data['train']['label'], 1000)
train_data_loader = DataLoader(ds, batch_size=1000)
device_ids = [0, 7]
m = model(data['dict']['vocab_size'], data['dict']['label_size'], 1000)
m = m.to(device)
optimizer = torch.optim.Adam(m.parameters())
criterion = torch.nn.CrossEntropyLoss()
m = DataParallel(m, device_ids=device_ids)
if __name__ == "__main__":
m.train()
for _ in range(100):
for data, label in train_data_loader:
data = data.to(device)
label = label.to(device)
optimizer.zero_grad()
target = m(data)
loss = criterion(target, label)
loss.backward()
optimizer.step()
