from icevision.models.mmdet.models.vfnet import backbones from icevision.models.mmdet.common.bbox.single_stage import * from icevision.models.interpretation import Interpretation, _move_to_device from icevision.models.mmdet.common.interpretation_utils import ( sum_losses_mmdet, loop_mmdet, ) _LOSSES_DICT = { "loss_cls": [], "loss_bbox": [], "loss_total": [], } interp = Interpretation( losses_dict=_LOSSES_DICT, valid_dl=valid_dl, infer_dl=infer_dl, predict_from_dl=predict_from_dl, ) interp._loop = loop_mmdet
loss = compute_loss(preds, y)[0] loss = { "loss_yolo": float(loss.cpu().numpy()), "loss_total": float(loss.cpu().numpy()), } for l in losses_stats.keys(): losses_stats[l].append(loss[l]) loss_comp = LossesRecordComponent() loss_comp.set_losses(loss) sample[0].add_component(loss_comp) sample[0].set_img(tensor_to_image(x[0])) samples_plus_losses.append(sample[0]) return samples_plus_losses, losses_stats _LOSSES_DICT = { "loss_yolo": [], "loss_total": [], } interp = Interpretation( losses_dict=_LOSSES_DICT, valid_dl=valid_dl, infer_dl=infer_dl, predict_from_dl=predict_from_dl, ) interp._loop = loop_yolo
"loss_unet": float(loss.cpu().numpy()), "loss_total": float(loss.cpu().numpy()), } for l in losses_stats.keys(): losses_stats[l].append(loss[l]) loss_comp = LossesRecordComponent() loss_comp.set_losses(loss) sample[0].add_component(loss_comp) sample[0].set_img(tensor_to_image(x[0])) sample[0].segmentation.set_mask_array( MaskArray(y[0].detach().cpu().numpy())) samples_plus_losses.append(sample[0]) return samples_plus_losses, losses_stats _LOSSES_DICT = { "loss_unet": [], "loss_total": [], } interp = Interpretation( losses_dict=_LOSSES_DICT, valid_dl=valid_dl, infer_dl=infer_dl, predict_from_dl=predict_from_dl, ) interp._loop = loop_unet