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train_sun.py
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import argparse
from torch.utils.data import DataLoader
# from dataset.CamVid import CamVid
from dataset.SUN import SUN
import os
from model.build_BiSeNet import BiSeNet
import torch
from tensorboardX import SummaryWriter
import tqdm
import numpy as np
from utils import poly_lr_scheduler
from utils import reverse_one_hot, get_label_info, colour_code_segmentation, compute_global_accuracy
from mk_random_val import mk_random_val
np.set_printoptions(threshold=np.nan, linewidth=10000)
def val(args, model, csv_path):
print('start val!')
mk_random_val(100)
label_info = get_label_info(csv_path)
val_img_path = os.path.join(args.data, 'val/image')
val_depth_path = os.path.join(args.data, 'val/depth')
val_label_path = os.path.join(args.data, 'val/label')
dataset_val = SUN(val_img_path, val_depth_path, val_label_path, csv_path, scale=(args.crop_height, args.crop_width), mode='val')
dataloader_val = DataLoader(
dataset_val,
# this has to be 1
batch_size=1,
shuffle=True,
num_workers=args.num_workers
)
with torch.no_grad():
model.eval()
precision_record = []
for i, (data, label) in enumerate(dataloader_val):
if torch.cuda.is_available() and args.use_gpu:
data = data.cuda()
label = label.cuda()
# get RGB predict image
predict = model(data).squeeze()
predict = reverse_one_hot(predict)
predict = colour_code_segmentation(np.array(predict), label_info)
# get RGB label image
label = label.squeeze()
label = reverse_one_hot(label)
label = colour_code_segmentation(np.array(label), label_info)
# compute per pixel accuracy
precision = compute_global_accuracy(predict, label)
precision_record.append(precision)
dice = np.mean(precision_record)
print('precision per pixel for validation: %.3f' % dice)
return dice
def train(args, model, optimizer, dataloader_train, csv_path):
writer = SummaryWriter()
step = 0
for epoch in range(args.epoch_start_i, args.num_epochs):
lr = poly_lr_scheduler(optimizer, args.learning_rate, iter=epoch, max_iter=args.num_epochs)
model.train()
tq = tqdm.tqdm(total=len(dataloader_train) * args.batch_size)
tq.set_description('epoch %d, lr %f' % (epoch, lr))
loss_record = []
for i, (data, label) in enumerate(dataloader_train):
if torch.cuda.is_available() and args.use_gpu:
data = data.cuda()
label = label.cuda()
# p = label
# for i in range(args.batch_size):
# predict = np.array(reverse_one_hot(p[i]))
# print(predict)
# print('label')
output = model(data)
# p = output
# for i in range(args.batch_size):
# predict = np.array(reverse_one_hot(p[i]))
# print(predict)
# print('output')
loss = torch.nn.BCEWithLogitsLoss()(output, label)
tq.update(args.batch_size)
tq.set_postfix(loss='%.6f' % loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
writer.add_scalar('loss_step', loss, step)
loss_record.append(loss.item())
tq.close()
loss_train_mean = np.mean(loss_record)
writer.add_scalar('loss_epoch_train', float(loss_train_mean), epoch)
print('loss for train : %f' % loss_train_mean)
if epoch % args.checkpoint_step == 0:
if not os.path.isdir(args.save_model_path):
os.mkdir(args.save_model_path)
torch.save(model.module.state_dict(), os.path.join(args.save_model_path, 'epoch_{}.pth'.format(epoch)))
if epoch % args.validation_step == 0:
dice = val(args, model, csv_path)
writer.add_scalar('precision_val', dice, epoch)
def main(params):
# basic parameters
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='/home/disk2/xs/sun', help='path of training data')
parser.add_argument('--num_epochs', type=int, default=300, help='Number of epochs to train for')
parser.add_argument('--epoch_start_i', type=int, default=0, help='Start counting epochs from this number')
parser.add_argument('--checkpoint_step', type=int, default=10, help='How often to save checkpoints (epochs)')
parser.add_argument('--validation_step', type=int, default=1, help='How often to perform validation (epochs)')
parser.add_argument('--dataset', type=str, default='SUN', help='Dataset you are using.')
parser.add_argument('--crop_height', type=int, default=480, help='Height of cropped/resized input image to network')
parser.add_argument('--crop_width', type=int, default=640, help='Width of cropped/resized input image to network')
parser.add_argument('--batch_size', type=int, default=5, help='Number of images in each batch')
parser.add_argument('--context_path', type=str, default="resnet101", help='The context path model you are using.')
parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate used for train')
parser.add_argument('--num_workers', type=int, default=4, help='num of workers')
parser.add_argument('--num_classes', type=int, default=38, help='num of object classes (with void)')
parser.add_argument('--cuda', type=str, default='2', help='GPU ids used for training')
parser.add_argument('--use_gpu', type=bool, default=True, help='whether to user gpu for training')
parser.add_argument('--pretrained_model_path', type=str, default=None, help='path to pretrained model')
parser.add_argument('--save_model_path', type=str, default='./checkpoints', help='path to save model')
parser.add_argument('--csv_path', type=str, default='/home/disk2/xs/sun/seg37_class_dict.csv', help='Path to label info csv file')
args = parser.parse_args(params)
# create dataset and dataloader
train_img_path = os.path.join(args.data, 'train/image')
train_depth_path = os.path.join(args.data, 'train/depth')
train_label_path = os.path.join(args.data, 'train/label')
csv_path = os.path.join(args.data, 'seg37_class_dict.csv')
dataset_train = SUN(train_img_path, train_depth_path, train_label_path, csv_path, scale=(args.crop_height, args.crop_width), mode='train')
dataloader_train = DataLoader(
dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers
)
# build model
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda
model = BiSeNet(args.num_classes, args.context_path)
if torch.cuda.is_available() and args.use_gpu:
model = torch.nn.DataParallel(model).cuda()
# build optimizer
optimizer = torch.optim.RMSprop(model.parameters(), args.learning_rate)
# load pretrained model if exists
if args.pretrained_model_path is not None:
print('load model from %s ...' % args.pretrained_model_path)
model.module.load_state_dict(torch.load(args.pretrained_model_path))
print('Done!')
# train
train(args, model, optimizer, dataloader_train, csv_path)
# val(args, model, dataloader_val, csv_path)
if __name__ == '__main__':
params = [
'--epoch_start_i', '111',
'--cuda', '0',
'--batch_size', '5',
'--context_path', 'Xception',
'--pretrained_model_path', './checkpoints/epoch_110.pth'
]
main(params)