def main():
net = PSPNet(num_classes=voc.num_classes).cuda()
print('load model ' + args['snapshot'])
net.load_state_dict(torch.load(os.path.join(ckpt_path, args['exp_name'], args['snapshot'])))
net.eval()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
test_set = voc.VOC('test', transform=val_input_transform)
test_loader = DataLoader(test_set, batch_size=1, num_workers=8, shuffle=False)
check_mkdir(os.path.join(ckpt_path, args['exp_name'], 'test'))
for vi, data in enumerate(test_loader):
img_name, img = data
img_name = img_name[0]
img = Variable(img, volatile=True).cuda()
output = net(img)
prediction = output.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
prediction = voc.colorize_mask(prediction)
prediction.save(os.path.join(ckpt_path, args['exp_name'], 'test', img_name + '.png'))
print('%d / %d' % (vi + 1, len(test_loader)))
def main():
net = PSPNet(num_classes=voc.num_classes).cuda()
print('load model ' + args['snapshot'])
net.load_state_dict(torch.load(os.path.join(ckpt_path, args['exp_name'], args['snapshot'])))
net.eval()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
test_set = voc.VOC('test', transform=val_input_transform)
test_loader = DataLoader(test_set, batch_size=1, num_workers=8, shuffle=False)
check_mkdir(os.path.join(ckpt_path, args['exp_name'], 'test'))
for vi, data in enumerate(test_loader):
img_name, img = data
img_name = img_name[0]
img = Variable(img, volatile=True).cuda()
output = net(img)
prediction = output.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
prediction = voc.colorize_mask(prediction)
prediction.save(os.path.join(ckpt_path, args['exp_name'], 'test', img_name + '.png'))
print('%d / %d' % (vi + 1, len(test_loader)))
def main():
net = fcn8s.FCN8s(num_classes=voc.num_classes,pretrained=False).cuda()
#net = deeplab_resnet.Res_Deeplab().cuda()
#net = dl.Res_Deeplab().cuda()
#net.load_state_dict(torch.load(os.path.join(ckpt_path, args['exp_name'], args['snapshot'])))
net.load_state_dict(torch.load(os.path.join(root,model,pth)))
net.eval()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
target_transform = extended_transforms.MaskToTensor()
test_set = voc.VOC('eval', transform=val_input_transform,target_transform=target_transform)
test_loader = DataLoader(test_set, batch_size=1, num_workers=8, shuffle=False)
#check_mkdir(os.path.join(ckpt_path, args['exp_name'], 'test'))
predictions = []
masks = []
ious =np.array([])
for vi, data in enumerate(test_loader):
img_name, img, msk = data
img_name = img_name[0]
H,W = img.size()[2:]
L = min(H,W)
interp_before = nn.UpsamplingBilinear2d(size=(L,L))
interp_after = nn.UpsamplingBilinear2d(size=(H,W))
img = Variable(img, volatile=True).cuda()
msk = Variable(msk, volatile=True).cuda()
masks.append(msk.data.squeeze_(0).cpu().numpy())
#img = interp_before(img)
output = net(img)
#output = interp_after(output[3])
prediction = output.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
#prediction = output.data.max(1)[1].squeeze().cpu().numpy()
ious = np.append(ious,get_iou(prediction,masks[-1]))
predictions.append(prediction)
## prediction.save(os.path.join(ckpt_path, args['exp_name], 'test',
img_name + '.png')) prediction = voc.colorize_mask(prediction)
prediction.save(os.path.join(root,'segmented-images',model+'-'+img_name+'.png'))
#if vi == 10:
# break
print('%d / %d' % (vi + 1, len(test_loader)))
results = evaluate(predictions,masks,voc.num_classes)
print('mean iou = {}'.format(results[2]))
print(ious.mean())
def main():
backbone = ResNet()
backbone.load_state_dict(torch.load('./weight/resnet34-333f7ec4.pth'),
strict=False)
net = Decoder34(num_classes=13, backbone=backbone).cuda()
print('load model' + snapshot)
net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name,
snapshot)))
net.eval()
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
val_input_transform = standard_transforms.Compose([
standard_transforms.ToTensor(),
standard_transforms.Normalize(*mean_std)
])
# test_set = voc.VOC('test', transform=val_input_transform)
# test_loader = DataLoader(test_set, batch_size=1,
# num_workers=8, shuffle=False)
test_set = wp.Wp('test',
transform=input_transform,
target_transform=target_transform)
test_loader = DataLoader(train_set,
batch_size=1,
num_workers=4,
shuffle=False)
check_mkdir(os.path.join(ckpt_path, args['exp_name'], 'test'))
for vi, data in enumerate(test_loader):
img_name, img = data
img_name = img_name[0]
img = Variable(img, volatile=True).cuda()
output = net(img)
prediction = output.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy()
prediction = voc.colorize_mask(prediction)
prediction.save(
os.path.join(ckpt_path, args['exp_name'], 'test',
img_name + '.png'))
print('%d / %d' % (vi + 1, len(test_loader)))
def validate(val_loader, net, criterion, optimizer, epoch, train_args,
visualize):
# the following code is written assuming that batch size is 1
net.eval()
val_loss = AverageMeter()
gts_all = np.zeros(
(len(val_loader), args['shorter_size'], 2 * args['shorter_size']),
dtype=float)
predictions_all = np.zeros(
(len(val_loader), args['shorter_size'], 2 * args['shorter_size']),
dtype=int)
for vi, data in enumerate(val_loader):
input, gt, slices_info = data
assert len(input.size()) == 5 and len(gt.size()) == 4 and len(
slices_info.size()) == 3
input.transpose_(0, 1)
gt.transpose_(0, 1)
slices_info.squeeze_(0)
assert input.size()[3:] == gt.size()[2:]
count = torch.zeros(args['shorter_size'],
2 * args['shorter_size']).cuda()
output = torch.zeros(voc.num_classes, args['shorter_size'],
2 * args['shorter_size']).cuda()
slice_batch_pixel_size = input.size(1) * input.size(3) * input.size(4)
for input_slice, gt_slice, info in zip(input, gt, slices_info):
input_slice = Variable(input_slice).cuda()
gt_slice = Variable(gt_slice).cuda()
output_slice = net(input_slice)
assert output_slice.size()[2:] == gt_slice.size()[1:]
assert output_slice.size()[1] == voc.num_classes
# print('size: ', output[:, info[0]: info[1], info[2]: info[3]].size(), output_slice[0, :, :info[4], :info[5]].data.size())
output[:, info[0]:info[1], info[2]:info[3]] += output_slice[
0, :, :info[4], :info[5]].data
gts_all[vi, info[0]:info[1], info[2]:info[3]] += gt_slice[
0, :info[4], :info[5]].data.cpu().numpy()
count[info[0]:info[1], info[2]:info[3]] += 1
val_loss.update(
criterion(output_slice, gt_slice).item(),
slice_batch_pixel_size)
output /= count
# print(count.cpu().numpy())
gts_all[vi, :, :] /= count.cpu().numpy().astype(float)
predictions_all[vi, :, :] = output.max(0)[1].squeeze_(
0).cpu().numpy().astype(int)
print('validating: %d / %d' % (vi + 1, len(val_loader)))
acc, acc_cls, mean_iu, fwavacc = evaluate(predictions_all, gts_all,
voc.num_classes)
train_args['best_record']['val_loss'] = val_loss.avg
train_args['best_record']['epoch'] = epoch
train_args['best_record']['acc'] = acc
train_args['best_record']['acc_cls'] = acc_cls
train_args['best_record']['mean_iu'] = mean_iu
train_args['best_record']['fwavacc'] = fwavacc
snapshot_name = 'epoch_%d_loss_%.5f_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc,
optimizer.param_groups[1]['lr'])
torch.save(net.state_dict(),
os.path.join(ckpt_path, exp_name, snapshot_name + '.pth'))
torch.save(
optimizer.state_dict(),
os.path.join(ckpt_path, exp_name, 'opt_' + snapshot_name + '.pth'))
print("saving model: ",
os.path.join(ckpt_path, exp_name, snapshot_name + '.pth'))
if train_args['val_save_to_img_file']:
to_save_dir = os.path.join(ckpt_path, exp_name, str(epoch))
check_mkdir(to_save_dir)
val_visual = []
for idx, data in enumerate(zip(gts_all, predictions_all)):
gt_pil = voc.colorize_mask(data[0])
predictions_pil = voc.colorize_mask(data[1])
if train_args['val_save_to_img_file']:
predictions_pil.save(
os.path.join(to_save_dir, '%d_prediction.png' % idx))
gt_pil.save(os.path.join(to_save_dir, '%d_gt.png' % idx))
val_visual.extend([
visualize(gt_pil.convert('RGB')),
visualize(predictions_pil.convert('RGB'))
])
val_visual = torch.stack(val_visual, 0)
val_visual = vutils.make_grid(val_visual, nrow=2, padding=5)
writer.add_image(snapshot_name, val_visual)
print(
'-----------------------------------------------------------------------------------------------------------'
)
print(
'[epoch %d], [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]'
% (epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc))
print(
'best record: [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f], [epoch %d]'
% (train_args['best_record']['val_loss'],
train_args['best_record']['acc'],
train_args['best_record']['acc_cls'],
train_args['best_record']['mean_iu'],
train_args['best_record']['fwavacc'],
train_args['best_record']['epoch']))
print(
'-----------------------------------------------------------------------------------------------------------'
)
writer.add_scalar('val_loss', val_loss.avg, epoch)
writer.add_scalar('acc', acc, epoch)
writer.add_scalar('acc_cls', acc_cls, epoch)
writer.add_scalar('mean_iu', mean_iu, epoch)
writer.add_scalar('fwavacc', fwavacc, epoch)
net.train()
return val_loss.avg
def validate(val_loader, net, criterion, optimizer, epoch, train_args, visualize):
# the following code is written assuming that batch size is 1
net.eval()
val_loss = AverageMeter()
gts_all = np.zeros((len(val_loader), args['shorter_size'], 2 * args['shorter_size']), dtype=int)
predictions_all = np.zeros((len(val_loader), args['shorter_size'], 2 * args['shorter_size']), dtype=int)
for vi, data in enumerate(val_loader):
input, gt, slices_info = data
assert len(input.size()) == 5 and len(gt.size()) == 4 and len(slices_info.size()) == 3
input.transpose_(0, 1)
gt.transpose_(0, 1)
slices_info.squeeze_(0)
assert input.size()[3:] == gt.size()[2:]
count = torch.zeros(args['shorter_size'], 2 * args['shorter_size']).cuda()
output = torch.zeros(voc.num_classes, args['shorter_size'], 2 * args['shorter_size']).cuda()
slice_batch_pixel_size = input.size(1) * input.size(3) * input.size(4)
for input_slice, gt_slice, info in zip(input, gt, slices_info):
input_slice = Variable(input_slice).cuda()
gt_slice = Variable(gt_slice).cuda()
output_slice = net(input_slice)
assert output_slice.size()[2:] == gt_slice.size()[1:]
assert output_slice.size()[1] == voc.num_classes
output[:, info[0]: info[1], info[2]: info[3]] += output_slice[0, :, :info[4], :info[5]].data
gts_all[vi, info[0]: info[1], info[2]: info[3]] += gt_slice[0, :info[4], :info[5]].data.cpu().numpy()
count[info[0]: info[1], info[2]: info[3]] += 1
val_loss.update(criterion(output_slice, gt_slice).data[0], slice_batch_pixel_size)
output /= count
gts_all[vi, :, :] /= count.cpu().numpy().astype(int)
predictions_all[vi, :, :] = output.max(0)[1].squeeze_(0).cpu().numpy()
print('validating: %d / %d' % (vi + 1, len(val_loader)))
acc, acc_cls, mean_iu, fwavacc = evaluate(predictions_all, gts_all, voc.num_classes)
train_args['best_record']['val_loss'] = val_loss.avg
train_args['best_record']['epoch'] = epoch
train_args['best_record']['acc'] = acc
train_args['best_record']['acc_cls'] = acc_cls
train_args['best_record']['mean_iu'] = mean_iu
train_args['best_record']['fwavacc'] = fwavacc
snapshot_name = 'epoch_%d_loss_%.5f_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc, optimizer.param_groups[1]['lr'])
torch.save(net.state_dict(), os.path.join(ckpt_path, exp_name, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(), os.path.join(ckpt_path, exp_name, 'opt_' + snapshot_name + '.pth'))
if train_args['val_save_to_img_file']:
to_save_dir = os.path.join(ckpt_path, exp_name, str(epoch))
check_mkdir(to_save_dir)
val_visual = []
for idx, data in enumerate(zip(gts_all, predictions_all)):
gt_pil = voc.colorize_mask(data[0])
predictions_pil = voc.colorize_mask(data[1])
if train_args['val_save_to_img_file']:
predictions_pil.save(os.path.join(to_save_dir, '%d_prediction.png' % idx))
gt_pil.save(os.path.join(to_save_dir, '%d_gt.png' % idx))
val_visual.extend([visualize(gt_pil.convert('RGB')),
visualize(predictions_pil.convert('RGB'))])
val_visual = torch.stack(val_visual, 0)
val_visual = vutils.make_grid(val_visual, nrow=2, padding=5)
writer.add_image(snapshot_name, val_visual)
print('-----------------------------------------------------------------------------------------------------------')
print('[epoch %d], [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc))
print('best record: [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f], [epoch %d]' % (
train_args['best_record']['val_loss'], train_args['best_record']['acc'], train_args['best_record']['acc_cls'],
train_args['best_record']['mean_iu'], train_args['best_record']['fwavacc'], train_args['best_record']['epoch']))
print('-----------------------------------------------------------------------------------------------------------')
writer.add_scalar('val_loss', val_loss.avg, epoch)
writer.add_scalar('acc', acc, epoch)
writer.add_scalar('acc_cls', acc_cls, epoch)
writer.add_scalar('mean_iu', mean_iu, epoch)
writer.add_scalar('fwavacc', fwavacc, epoch)
net.train()
return val_loss.avg
def validate(val_loader, net, criterion, optimizer, epoch, train_args, restore,
visualize):
net.eval()
val_loss = AverageMeter()
inputs_all, gts_all, predictions_all = [], [], []
for vi, data in enumerate(val_loader):
inputs, gts = data
N = inputs.size(0)
inputs = Variable(inputs, volatile=True).cuda()
gts = Variable(gts, volatile=True).cuda()
outputs = net(inputs)
predictions = outputs.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy()
val_loss.update(criterion(outputs, gts).data[0] / N, N)
if random.random() > train_args['val_img_sample_rate']:
inputs_all.append(None)
else:
inputs_all.append(inputs.data.squeeze_(0).cpu())
gts_all.append(gts.data.squeeze_(0).cpu().numpy())
predictions_all.append(predictions)
acc, acc_cls, mean_iu, fwavacc = evaluate(predictions_all, gts_all,
voc.num_classes)
if mean_iu > train_args['best_record']['mean_iu']:
train_args['best_record']['val_loss'] = val_loss.avg
train_args['best_record']['epoch'] = epoch
train_args['best_record']['acc'] = acc
train_args['best_record']['acc_cls'] = acc_cls
train_args['best_record']['mean_iu'] = mean_iu
train_args['best_record']['fwavacc'] = fwavacc
snapshot_name = 'epoch_%d_loss_%.5f_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc,
optimizer.param_groups[1]['lr'])
# torch.save(net.state_dict(), os.path.join(ckpt_path, exp_name, snapshot_name + '.pth'))
# torch.save(optimizer.state_dict(), os.path.join(ckpt_path, exp_name, 'opt_' + snapshot_name + '.pth'))
if train_args['val_save_to_img_file']:
to_save_dir = os.path.join(ckpt_path, exp_name, str(epoch))
check_mkdir(to_save_dir)
val_visual = []
for idx, data in enumerate(zip(inputs_all, gts_all, predictions_all)):
if data[0] is None:
continue
input_pil = restore(data[0])
gt_pil = voc.colorize_mask(data[1])
predictions_pil = voc.colorize_mask(data[2])
if train_args['val_save_to_img_file']:
input_pil.save(os.path.join(to_save_dir, '%d_input.png' % idx))
predictions_pil.save(
os.path.join(to_save_dir, '%d_prediction.png' % idx))
gt_pil.save(os.path.join(to_save_dir, '%d_gt.png' % idx))
val_visual.extend([
visualize(input_pil.convert('RGB')),
visualize(gt_pil.convert('RGB')),
visualize(predictions_pil.convert('RGB'))
])
val_visual = torch.stack(val_visual, 0)
val_visual = vutils.make_grid(val_visual, nrow=3, padding=5)
writer.add_image(snapshot_name, val_visual)
print(
'--------------------------------------------------------------------')
print(
'[epoch %d], [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]'
% (epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc))
print(
'best record: [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f], [epoch %d]'
% (train_args['best_record']['val_loss'],
train_args['best_record']['acc'],
train_args['best_record']['acc_cls'],
train_args['best_record']['mean_iu'],
train_args['best_record']['fwavacc'],
train_args['best_record']['epoch']))
print(
'--------------------------------------------------------------------')
writer.add_scalar('val_loss', val_loss.avg, epoch)
writer.add_scalar('acc', acc, epoch)
writer.add_scalar('acc_cls', acc_cls, epoch)
writer.add_scalar('mean_iu', mean_iu, epoch)
writer.add_scalar('fwavacc', fwavacc, epoch)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'], epoch)
net.train()
return val_loss.avg
def validate(val_loader, net, criterion, optimizer, epoch, train_args, restore, visualize):
net.eval()
val_loss = AverageMeter()
inputs_all, gts_all, predictions_all = [], [], []
for vi, data in enumerate(val_loader):
inputs, gts = data
N = inputs.size(0)
inputs = Variable(inputs, volatile=True).cuda()
gts = Variable(gts, volatile=True).cuda()
outputs = net(inputs)
predictions = outputs.data.max(1)[1].squeeze_(1).squeeze_(0).cpu().numpy()
val_loss.update(criterion(outputs, gts).data[0] / N, N)
if random.random() > train_args['val_img_sample_rate']:
inputs_all.append(None)
else:
inputs_all.append(inputs.data.squeeze_(0).cpu())
gts_all.append(gts.data.squeeze_(0).cpu().numpy())
predictions_all.append(predictions)
acc, acc_cls, mean_iu, fwavacc = evaluate(predictions_all, gts_all, voc.num_classes)
if mean_iu > train_args['best_record']['mean_iu']:
train_args['best_record']['val_loss'] = val_loss.avg
train_args['best_record']['epoch'] = epoch
train_args['best_record']['acc'] = acc
train_args['best_record']['acc_cls'] = acc_cls
train_args['best_record']['mean_iu'] = mean_iu
train_args['best_record']['fwavacc'] = fwavacc
snapshot_name = 'epoch_%d_loss_%.5f_acc_%.5f_acc-cls_%.5f_mean-iu_%.5f_fwavacc_%.5f_lr_%.10f' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc, optimizer.param_groups[1]['lr']
)
torch.save(net.state_dict(), os.path.join(ckpt_path, exp_name, snapshot_name + '.pth'))
torch.save(optimizer.state_dict(), os.path.join(ckpt_path, exp_name, 'opt_' + snapshot_name + '.pth'))
if train_args['val_save_to_img_file']:
to_save_dir = os.path.join(ckpt_path, exp_name, str(epoch))
check_mkdir(to_save_dir)
val_visual = []
for idx, data in enumerate(zip(inputs_all, gts_all, predictions_all)):
if data[0] is None:
continue
input_pil = restore(data[0])
gt_pil = voc.colorize_mask(data[1])
predictions_pil = voc.colorize_mask(data[2])
if train_args['val_save_to_img_file']:
input_pil.save(os.path.join(to_save_dir, '%d_input.png' % idx))
predictions_pil.save(os.path.join(to_save_dir, '%d_prediction.png' % idx))
gt_pil.save(os.path.join(to_save_dir, '%d_gt.png' % idx))
val_visual.extend([visualize(input_pil.convert('RGB')), visualize(gt_pil.convert('RGB')),
visualize(predictions_pil.convert('RGB'))])
val_visual = torch.stack(val_visual, 0)
val_visual = vutils.make_grid(val_visual, nrow=3, padding=5)
writer.add_image(snapshot_name, val_visual)
print('--------------------------------------------------------------------')
print('[epoch %d], [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f]' % (
epoch, val_loss.avg, acc, acc_cls, mean_iu, fwavacc))
print('best record: [val loss %.5f], [acc %.5f], [acc_cls %.5f], [mean_iu %.5f], [fwavacc %.5f], [epoch %d]' % (
train_args['best_record']['val_loss'], train_args['best_record']['acc'], train_args['best_record']['acc_cls'],
train_args['best_record']['mean_iu'], train_args['best_record']['fwavacc'], train_args['best_record']['epoch']))
print('--------------------------------------------------------------------')
writer.add_scalar('val_loss', val_loss.avg, epoch)
writer.add_scalar('acc', acc, epoch)
writer.add_scalar('acc_cls', acc_cls, epoch)
writer.add_scalar('mean_iu', mean_iu, epoch)
writer.add_scalar('fwavacc', fwavacc, epoch)
writer.add_scalar('lr', optimizer.param_groups[1]['lr'], epoch)
net.train()
return val_loss.avg