def test(val_loader, model, epoch):
model.eval()
step = 0
batch_time = AverageMeter()
total_losses = AverageMeter()
binary_losses = AverageMeter()
instance_losses = AverageMeter()
mean_iou = AverageMeter()
end = time.time()
val_img_list = []
# val_img_md5 = open(os.path.join(im_path, "val_" + str(epoch + 1) + ".txt"), "w")
for batch_idx, batch in enumerate(iter(val_loader)):
step += 1
# image_data = Variable(input_data["input_tensor"]).to(DEVICE)
# instance_label = Variable(input_data["instance_label"]).to(DEVICE)
# binary_label = Variable(input_data["binary_label"]).to(DEVICE)
image_data = Variable(batch[0]).type(torch.FloatTensor).to(DEVICE)
binary_label = Variable(batch[1]).type(torch.LongTensor).to(DEVICE)
instance_label = Variable(batch[2]).type(torch.FloatTensor).to(DEVICE)
# output process
net_output = model(image_data)
net_output
total_loss, binary_loss, instance_loss, out, val_iou = compute_loss(
net_output, binary_label, instance_label)
total_losses.update(total_loss.item(), image_data.size()[0])
binary_losses.update(binary_loss.item(), image_data.size()[0])
instance_losses.update(instance_loss.item(), image_data.size()[0])
mean_iou.update(val_iou, image_data.size()[0])
# if step % 100 == 0:
# val_img_list.append(
# compose_img(image_data, out, binary_label, net_output["instance_seg_logits"], instance_label, 0))
# val_img_md5.write(input_data["img_name"][0] + "\n")
# lane_cluster_and_draw(image_data, net_output["binary_seg_pred"], net_output["instance_seg_logits"], input_data["o_size"], input_data["img_name"], json_path)
batch_time.update(time.time() - end)
end = time.time()
# print(
# "Epoch {ep} Validation Report | ETA: {et:.2f}|Total:{tot:.5f}|Binary:{bin:.5f}|Instance:{ins:.5f}|IoU:{iou:.5f}".format(
# ep=epoch + 1,
# et=batch_time.val,
# tot=total_losses.avg,
# bin=binary_losses.avg,
# ins=instance_losses.avg,
# iou=mean_iou.avg,
# ))
# sys.stdout.flush()
# val_img = np.concatenate(val_img_list, axis=1)
# cv2.imwrite(os.path.join(im_path, "val_" + str(epoch + 1) + ".png"), val_img)
# val_img_md5.close()
return mean_iou.avg
def train(train_loader, model, optimizer, epoch, w1, w2, w3, w4):
model.train()
batch_time = AverageMeter()
mean_iou = AverageMeter()
total_losses = AverageMeter()
binary_losses = AverageMeter()
instance_losses = AverageMeter()
end = time.time()
step = 0
t = tqdm(enumerate(iter(train_loader)),
leave=False,
total=len(train_loader))
for batch_idx, batch in t:
try:
step += 1
image_data = Variable(batch[0]).type(torch.FloatTensor).to(DEVICE)
binary_label = Variable(batch[1]).type(torch.LongTensor).to(DEVICE)
instance_label = Variable(batch[2]).type(
torch.FloatTensor).to(DEVICE)
#print("///////////////////////////////////////////////////")
#print(image_data.size())
#print(binary_label.size())
# # print(image_data.shape)
#print("///////////////////////////////////////////////////")
# forward pass
net_output = model(image_data)
# compute loss
total_loss, binary_loss, instance_loss, out, train_iou = compute_loss(
net_output, binary_label, instance_label, w1, w2, w3, w4)
# update loss in AverageMeter instance
total_losses.update(total_loss.item(), image_data.size()[0])
binary_losses.update(binary_loss.item(), image_data.size()[0])
instance_losses.update(instance_loss.item(), image_data.size()[0])
mean_iou.update(train_iou, image_data.size()[0])
# reset gradients
optimizer.zero_grad()
# backpropagate
total_loss.backward()
# update weights
optimizer.step()
# update batch time
batch_time.update(time.time() - end)
end = time.time()
if step % config.show_interval == 0:
print(
"Epoch {ep} Step {st} |({batch}/{size})| ETA: {et:.2f}|Total loss:{tot:.5f}|Binary loss:{bin:.5f}|Instance loss:{ins:.5f}|IoU:{iou:.5f}"
.format(
ep=epoch + 1,
st=step,
batch=batch_idx + 1,
size=len(train_loader),
et=batch_time.val,
tot=total_losses.avg,
bin=binary_losses.avg,
ins=instance_losses.avg,
iou=train_iou,
))
print("current learning rate is %s" %
(str(optimizer.state_dict()['param_groups'][0]['lr'])))
sys.stdout.flush()
train_img_list = []
for i in range(3):
train_img_list.append(
compose_img(image_data, out, binary_label,
net_output["instance_seg_logits"],
instance_label, i))
train_img = np.concatenate(train_img_list, axis=1)
cv2.imwrite(
os.path.join(
"./output", "train_" + str(epoch + 1) + "_step_" +
str(step) + ".png"), train_img)
except Exception as e:
print(e)
print('error')
return mean_iou.avg
def train(train_loader, model, optimizer, epoch, image_output_path):
batch_time = AverageMeter()
mean_iou = AverageMeter()
total_losses = AverageMeter()
binary_losses = AverageMeter()
instance_losses = AverageMeter()
end = time.time()
step = 0
t = tqdm(enumerate(iter(train_loader)),
leave=False,
total=len(train_loader))
for batch_idx, batch in t:
# MES changes to use less GPU memory
step += 1
image_data = Variable(batch[0]).type(torch.FloatTensor).to(DEVICE)
binary_label = Variable(batch[1]).type(torch.LongTensor).to(DEVICE)
instance_label = Variable(batch[2]).type(torch.FloatTensor).to(DEVICE)
# forward pass
net_output = model(image_data)
# compute loss
total_loss, binary_loss, instance_loss, out, train_iou = compute_loss(
net_output, binary_label, instance_label)
# update loss in AverageMeter instance
total_losses.update(total_loss.item(), image_data.size()[0])
binary_losses.update(binary_loss.item(), image_data.size()[0])
instance_losses.update(instance_loss.item(), image_data.size()[0])
mean_iou.update(train_iou, image_data.size()[0])
# reset gradients
optimizer.zero_grad()
# backpropagate
total_loss.backward()
# update weights
optimizer.step()
# update batch time
batch_time.update(time.time() - end)
end = time.time()
if step % 1 == 0:
print(
"Epoch {ep} Step {st} |({batch}/{size})| ETA: {et:.2f}|Total loss:{tot:.5f}|Binary loss:{bin:.5f}|Instance loss:{ins:.5f}|IoU:{iou:.5f}"
.format(
ep=epoch + 1,
st=step,
batch=batch_idx + 1,
size=len(train_loader),
et=batch_time.val,
tot=total_losses.avg,
bin=binary_losses.avg,
ins=instance_losses.avg,
iou=train_iou,
))
sys.stdout.flush()
train_img_list = []
for i in range(3):
train_img_list.append(
compose_img(image_data, out, binary_label,
net_output["instance_seg_logits"],
instance_label, i))
train_img = np.concatenate(train_img_list, axis=1)
# MES changes
print("train: about to imwrite images")
print("train_img: ", train_img)
print(" ")
cv2.imwrite(
os.path.join(
image_output_path,
"train_" + str(epoch + 1) + "_step_" + str(step) + ".png"),
train_img)
return mean_iou.avg