def validation_phase_mIoU(history, main_dict, model, val_set,
predict_name, epoch):
category2name = lambda x: "person" if x == 1 else "car"
iou_mean = {"person":0, "car":0}
iou_sum = {"person":0, "car":0}
n_objects = {"person":0, "car":0}
n_images = len(val_set)
for i in range(n_images):
batch = ms.get_batch(val_set, [i])
maskObjects = batch["maskObjects"].squeeze()
pred_dict = model.predict(batch, predict_method="BestDice")
for ann in pred_dict["annList"]:
point = ann["point"]
category_id = point["category_id"]
label = maskObjects[point["y"], point["x"]].item()
assert label != 0
A = maskObjects == label
B = au.ann2mask(ann)["mask"]
iou_sum[category2name(category_id)] += au.compute_iou(ms.t2n(A), ms.t2n(B))
n_objects[category2name(category_id)] += 1
# ms.images(batch["images"], A, denorm=1, win="GT")
# ms.images(batch["images"], B, denorm=1, win="Pred")
for name in ["person", "car"]:
iou_mean[name] = iou_sum[name] / max(1, n_objects[name])
print("{}/{} - {:.3f} {:.3f}".format(i, n_images, iou_mean["person"],
iou_mean["car"]))
# iou_mean = iou_sum / n_objects
val_dict = {}
val_dict["iou_mean"] = iou_mean
val_dict["predict_name"] = predict_name
val_dict["epoch"] = epoch
val_dict["time"] = datetime.datetime.now().strftime("%b %d, 20%y")
# Update history
history["val"] += [val_dict]
# Higher is better
if (history["best_model"] == {} or
history["best_model"]["iou_mean"]["person"] <= val_dict["iou_mean"]["person"]):
history["best_model"] = val_dict
ms.save_best_model(main_dict, model)
return history
def validation_phase_mAP(history, main_dict, model, val_set, predict_name, epoch):
val_dict, pred_annList = au.validate(model, val_set,
predict_method=predict_name,
n_val=len(val_set), return_annList=True)
val_dict["predict_name"] = predict_name
val_dict["epoch"] = epoch
val_dict["time"] = datetime.datetime.now().strftime("%b %d, 20%y")
# Update history
history["val"] += [val_dict]
# Higher is better
if (history["best_model"] == {} or
history["best_model"]["0.5"] <= val_dict["0.5"]):
history["best_model"] = val_dict
ms.save_best_model(main_dict, model)
ms.save_pkl(main_dict["path_best_annList"], pred_annList)
ms.copy_code_best(main_dict)
return history
def validation_phase_MAE(history, main_dict, model, val_set, metric_class, verbose, metric_name,
predict_name, epoch):
val_dict = ms.validate(dataset=val_set,
model=model,
verbose=verbose,
metric_class=metric_class,
batch_size=1,
epoch=epoch)
val_dict["predict_name"] = predict_name
val_dict["epoch"] = epoch
# Update history
history["val"] += [val_dict]
# Higher is better
if (history["best_model"] == {} or
history["best_model"][metric_name] >= val_dict[metric_name]):
history["best_model"] = val_dict
ms.save_best_model(main_dict, model)
return history