Datasets:
jblitzar
/
github-python-corpus

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batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
torch.cuda.empty_cache()
model.eval()
end = time.time()
for i, data in enumerate(val_loader):
features, pointclouds, edges_self, edges_forward, edges_propagate, target, norms = data
features, pointclouds, edges_self, edges_forward, edges_propagate, target, norms = \
to_device(features), to_device(pointclouds), \
to_device(edges_self), to_device(edges_forward), \
to_device(edges_propagate), to_device(target), to_device(norms)
data_time.update(time.time() - end)
with torch.no_grad():
pred = model(
features,
pointclouds,
edges_self,
edges_forward,
edges_propagate,
norms)
pred = pred.contiguous().view(-1, args.num_classes)
target = target.view(-1, 1)[:, 0]
loss = criterion(pred, target)
output = pred.max(1)[1]
n = output.size(0)
loss *= n
count = target.new_tensor([n], dtype=torch.long)
if args.DDP:
dist.all_reduce(loss)
dist.all_reduce(count)
n = count.item()
loss /= n
intersection, union, target = intersectionAndUnionGPU(
output, target, args.num_classes, args.ignore_label)
if args.DDP:
dist.all_reduce(intersection)
dist.all_reduce(union)
dist.all_reduce(target)
intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()
intersection_meter.update(intersection)
union_meter.update(union)
target_meter.update(target)
accuracy = sum(intersection_meter.val) / \
(sum(target_meter.val) + 1e-10)
loss_meter.update(loss.item(), n)
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0 and main_process():
logger.info(
'Test: [{}/{}] '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) '
'Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f}) '
'Accuracy {accuracy:.4f}.'.format(
i + 1,
len(val_loader),
data_time=data_time,
batch_time=batch_time,
loss_meter=loss_meter,
accuracy=accuracy))
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
# print('iou_class : ', iou_class)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
if main_process():
logger.info(
'Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc))
for i in range(args.num_classes):
logger.info('Class_{} Result: iou/accuracy {:.4f}/{:.4f}.'.format(i,
iou_class[i], accuracy_class[i]))
logger.info('<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<')
return loss_meter.avg, mIoU, mAcc, allAcc
if __name__ == '__main__':
import gc
gc.collect()
main()
# <FILESEP>
'''
Metrics for underwater image quality evaluation.
Author: Xuelei Chen