def validation(model, val_loader, epoch, writer):
# set evaluate mode
model.eval()
total_correct, total_label = 0, 0
hist = np.zeros((args.num_classes, args.num_classes))
# Iterate over data.
bar = Bar('Processing {}'.format('val'), max=len(val_loader))
bar.check_tty = False
for idx, batch in enumerate(val_loader):
image, target, _ = batch
image, target = image.cuda(), target.cuda()
with torch.no_grad():
h, w = target.size(1), target.size(2)
outputs = model(image)
outputs = gather(outputs, 0, dim=0)
preds = F.interpolate(input=outputs[0], size=(h, w), mode='bilinear', align_corners=True)
if idx % 50 == 0:
img_vis = inv_preprocess(image, num_images=args.save_num)
label_vis = decode_predictions(target.int(), num_images=args.save_num, num_classes=args.num_classes)
pred_vis = decode_predictions(torch.argmax(preds, dim=1), num_images=args.save_num,
num_classes=args.num_classes)
# visual grids
img_grid = torchvision.utils.make_grid(torch.from_numpy(img_vis.transpose(0, 3, 1, 2)))
label_grid = torchvision.utils.make_grid(torch.from_numpy(label_vis.transpose(0, 3, 1, 2)))
pred_grid = torchvision.utils.make_grid(torch.from_numpy(pred_vis.transpose(0, 3, 1, 2)))
writer.add_image('val_images', img_grid, epoch * len(val_loader) + idx + 1)
writer.add_image('val_labels', label_grid, epoch * len(val_loader) + idx + 1)
writer.add_image('val_preds', pred_grid, epoch * len(val_loader) + idx + 1)
# pixelAcc
correct, labeled = batch_pix_accuracy(preds.data, target)
# mIoU
hist += fast_hist(preds, target, args.num_classes)
total_correct += correct
total_label += labeled
pixAcc = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = round(np.nanmean(per_class_iu(hist)) * 100, 2)
# plot progress
bar.suffix = '{} / {} | pixAcc: {pixAcc:.4f}, mIoU: {IoU:.4f}'.format(idx + 1, len(val_loader),
pixAcc=pixAcc, IoU=IoU)
bar.next()
mIoU = round(np.nanmean(per_class_iu(hist)) * 100, 2)
writer.add_scalar('val_pixAcc', pixAcc, epoch)
writer.add_scalar('val_mIoU', mIoU, epoch)
bar.finish()
return pixAcc, mIoU
def validation(model, val_loader, epoch, writer):
# set evaluate mode
model.eval()
total_correct, total_label = 0, 0
total_correct_hb, total_label_hb = 0, 0
total_correct_fb, total_label_fb = 0, 0
hist = np.zeros((args.num_classes, args.num_classes))
hist_hb = np.zeros((args.hbody_cls, args.hbody_cls))
hist_fb = np.zeros((args.fbody_cls, args.fbody_cls))
# Iterate over data.
from tqdm import tqdm
tbar = tqdm(val_loader)
for idx, batch in enumerate(tbar):
image, target, hlabel, flabel, _ = batch
image, target, hlabel, flabel = image.cuda(), target.cuda(), hlabel.cuda(), flabel.cuda()
with torch.no_grad():
h, w = target.size(1), target.size(2)
outputs = model(image)
outputs = gather(outputs, 0, dim=0)
preds = F.interpolate(input=outputs[0], size=(h, w), mode='bilinear', align_corners=True)
preds_hb = F.interpolate(input=outputs[1], size=(h, w), mode='bilinear', align_corners=True)
preds_fb = F.interpolate(input=outputs[2], size=(h, w), mode='bilinear', align_corners=True)
if idx % 50 == 0:
img_vis = inv_preprocess(image, num_images=args.save_num)
label_vis = decode_predictions(target.int(), num_images=args.save_num, num_classes=args.num_classes)
pred_vis = decode_predictions(torch.argmax(preds, dim=1), num_images=args.save_num,
num_classes=args.num_classes)
# visual grids
img_grid = torchvision.utils.make_grid(torch.from_numpy(img_vis.transpose(0, 3, 1, 2)))
label_grid = torchvision.utils.make_grid(torch.from_numpy(label_vis.transpose(0, 3, 1, 2)))
pred_grid = torchvision.utils.make_grid(torch.from_numpy(pred_vis.transpose(0, 3, 1, 2)))
writer.add_image('val_images', img_grid, epoch * len(val_loader) + idx + 1)
writer.add_image('val_labels', label_grid, epoch * len(val_loader) + idx + 1)
writer.add_image('val_preds', pred_grid, epoch * len(val_loader) + idx + 1)
# pixelAcc
correct, labeled = batch_pix_accuracy(preds.data, target)
correct_hb, labeled_hb = batch_pix_accuracy(preds_hb.data, hlabel)
correct_fb, labeled_fb = batch_pix_accuracy(preds_fb.data, flabel)
# mIoU
hist += fast_hist(preds, target, args.num_classes)
hist_hb += fast_hist(preds_hb, hlabel, args.hbody_cls)
hist_fb += fast_hist(preds_fb, flabel, args.fbody_cls)
total_correct += correct
total_correct_hb += correct_hb
total_correct_fb += correct_fb
total_label += labeled
total_label_hb += labeled_hb
total_label_fb += labeled_fb
pixAcc = 1.0 * total_correct / (np.spacing(1) + total_label)
IoU = round(np.nanmean(per_class_iu(hist)) * 100, 2)
pixAcc_hb = 1.0 * total_correct_hb / (np.spacing(1) + total_label_hb)
IoU_hb = round(np.nanmean(per_class_iu(hist_hb)) * 100, 2)
pixAcc_fb = 1.0 * total_correct_fb / (np.spacing(1) + total_label_fb)
IoU_fb = round(np.nanmean(per_class_iu(hist_fb)) * 100, 2)
# plot progress
tbar.set_description('{} / {} | {pixAcc:.4f}, {IoU:.4f} |' \
'{pixAcc_hb:.4f}, {IoU_hb:.4f} |' \
'{pixAcc_fb:.4f}, {IoU_fb:.4f}'.format(idx + 1, len(val_loader), pixAcc=pixAcc, IoU=IoU,pixAcc_hb=pixAcc_hb, IoU_hb=IoU_hb,pixAcc_fb=pixAcc_fb, IoU_fb=IoU_fb))
print('\n per class iou part: {}'.format(per_class_iu(hist)*100))
print('per class iou hb: {}'.format(per_class_iu(hist_hb)*100))
print('per class iou fb: {}'.format(per_class_iu(hist_fb)*100))
mIoU = round(np.nanmean(per_class_iu(hist)) * 100, 2)
mIoU_hb = round(np.nanmean(per_class_iu(hist_hb)) * 100, 2)
mIoU_fb = round(np.nanmean(per_class_iu(hist_fb)) * 100, 2)
writer.add_scalar('val_pixAcc', pixAcc, epoch)
writer.add_scalar('val_mIoU', mIoU, epoch)
writer.add_scalar('val_pixAcc_hb', pixAcc_hb, epoch)
writer.add_scalar('val_mIoU_hb', mIoU_hb, epoch)
writer.add_scalar('val_pixAcc_fb', pixAcc_fb, epoch)
writer.add_scalar('val_mIoU_fb', mIoU_fb, epoch)
tbar.close()
return pixAcc, mIoU