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train.py
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#!/usr/bin/env python
#SBATCH --job-name=fusenet
#SBATCH --nodes=1
#SBATCH --cpus=4
#SBATCH --gres=gpu:1
#SBATCH --time="UNLIMITED"
import time
from options.train_options import TrainOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
# from util.visualize_mask import *
from util.util import confusion_matrix, getScores
import numpy as np
import random
import torch
import cv2
if __name__ == '__main__':
train_opt = TrainOptions().parse()
np.random.seed(train_opt.seed)
random.seed(train_opt.seed)
torch.manual_seed(train_opt.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.cuda.manual_seed(train_opt.seed)
train_data_loader = CreateDataLoader(train_opt)
train_dataset = train_data_loader.load_data()
train_dataset_size = len(train_data_loader)
print('#training images = %d' % train_dataset_size)
test_opt = TrainOptions().parse()
test_opt.phase = 'val'
test_opt.batch_size = 1
test_opt.num_threads = 1
test_opt.serial_batches = True
test_opt.no_flip = True
test_opt.display_id = -1
test_data_loader = CreateDataLoader(test_opt)
test_dataset = test_data_loader.load_data()
test_dataset_size = len(test_data_loader)
print('#test images = %d' % test_dataset_size)
model = create_model(train_opt, train_dataset.dataset)
model.setup(train_opt)
visualizer = Visualizer(train_opt)
total_steps = 0
for epoch in range(train_opt.epoch_count, train_opt.niter + 1):
model.train()
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
for i, data in enumerate(train_dataset):
iter_start_time = time.time()
if total_steps % train_opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_steps += train_opt.batch_size
epoch_iter += train_opt.batch_size
model.set_input(data)
model.optimize_parameters()
if total_steps % train_opt.display_freq == 0:
save_result = total_steps % train_opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_steps % train_opt.print_freq == 0:
losses = model.get_current_losses()
t = (time.time() - iter_start_time) / train_opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data)
if train_opt.display_id > 0:
visualizer.plot_current_losses(epoch,
float(epoch_iter) / train_dataset_size, train_opt, losses)
iter_data_time = time.time()
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, train_opt.niter, time.time() - epoch_start_time))
model.update_learning_rate()
if epoch > 100 and epoch % train_opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
model.save_networks('latest')
model.save_networks(epoch)
model.eval()
test_loss_iter = []
gts = None
preds = None
epoch_iter = 0
conf_mat = np.zeros((test_dataset.dataset.num_labels, test_dataset.dataset.num_labels), dtype=np.float)
with torch.no_grad():
for i, data in enumerate(test_dataset):
model.set_input(data)
model.forward()
model.get_loss()
epoch_iter += test_opt.batch_size
gt = model.mask.cpu().int().numpy()
_, pred = torch.max(model.output.data.cpu(), 1)
pred = pred.float().detach().int().numpy()
if test_dataset.dataset.name() == 'Scannetv2':
gt = data["mask_fullsize"].cpu().int().numpy()[0]
pred = cv2.resize(pred[0], (gt.shape[1], gt.shape[0]), interpolation=cv2.INTER_NEAREST)
conf_mat += confusion_matrix(gt, pred, test_dataset.dataset.num_labels, ignore_label=test_dataset.dataset.ignore_label)
# visualizer.display_current_results(model.get_current_visuals(), epoch, False)
losses = model.get_current_losses()
test_loss_iter.append(model.loss_segmentation)
print('test epoch {0:}, iters: {1:}/{2:} '.format(epoch, epoch_iter, len(test_dataset) * test_opt.batch_size), end='\r')
avg_test_loss = np.mean(test_loss_iter)
glob,mean,iou = getScores(conf_mat)
visualizer.print_current_scores(epoch, avg_test_loss, glob, mean, iou)
visualizer.save_confusion_matrix(conf_mat, epoch)