for x, y in test_dloader:
# implement testing pipeline here
x = x.to(device)
y = y.to(device)
logit = model(x)
loss_xy, loss_wh, loss_obj, loss_noobj, loss_class = compute_loss(
logit, y)
loss = (lambda_coord * (loss_xy + loss_wh) + loss_obj +
lambda_noobj * loss_noobj + loss_class) / batch_size
valid_loss += loss
valid_loss /= len(test_dloader)
ckpt = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}
torch.save(ckpt, ckpt_path)
VOC_CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
'tvmonitor')
test_image_dir = 'test_images'
image_path_list = [
os.path.join(test_image_dir, path) for path in os.listdir(test_image_dir)
]
targets[:, 2:] = utils.xywh2xyxy(targets[:, 2:])
targets[:, 2:] *= args.img_size
with torch.no_grad():
outputs, _ = model(imgs)
outputs = utils.non_max_suppression(
outputs,
conf_thresh=args.conf_thresh,
nms_thresh=args.nms_thresh)
sample_metrics += utils.get_batch_statistics(
outputs, targets, iou_thresh=args.map_thresh)
if len(sample_metrics) == 0:
print('---- mAP is NULL')
else:
# Concatenate sample statistics
true_positives, pred_scores, pred_labels = [
np.concatenate(x, 0) for x in list(zip(*sample_metrics))
]
precision, recall, AP, f1, ap_class = utils.ap_per_class(
true_positives, pred_scores, pred_labels, labels)
print('---- mAP %.3f' % (AP.mean()))
if epoch % args.checkpoint_interval == 0 and epoch > 20:
torch.save(
model.state_dict(),
os.path.join(args.output_path,
'yolov3_tiny_ckpt_%d.pth' % epoch))
scheduler.step()