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from time import time
import numpy as np
import torch
from fbrs.inference import utils
from fbrs.inference.clicker import Clicker
try:
get_ipython()
from tqdm import tqdm_notebook as tqdm
except NameError:
from tqdm import tqdm
def evaluate_dataset(dataset, predictor, oracle_eval=False, **kwargs):
all_ious = []
start_time = time()
for index in tqdm(range(len(dataset)), leave=False):
sample = dataset.get_sample(index)
item = dataset[index]
if oracle_eval:
gt_mask = torch.tensor(sample['instances_mask'], dtype=torch.float32)
gt_mask = gt_mask.unsqueeze(0).unsqueeze(0)
predictor.opt_functor.mask_loss.set_gt_mask(gt_mask)
_, sample_ious, _ = evaluate_sample(item['images'], sample['instances_mask'], predictor, **kwargs)
all_ious.append(sample_ious)
end_time = time()
elapsed_time = end_time - start_time
return all_ious, elapsed_time
def evaluate_sample(image_nd, instances_mask, predictor, max_iou_thr,
pred_thr=0.49, max_clicks=20):
clicker = Clicker(gt_mask=instances_mask)
pred_mask = np.zeros_like(instances_mask)
ious_list = []
with torch.no_grad():
predictor.set_input_image(image_nd)
for click_number in range(max_clicks):
clicker.make_next_click(pred_mask)
pred_probs = predictor.get_prediction(clicker)
pred_mask = pred_probs > pred_thr
iou = utils.get_iou(instances_mask, pred_mask)
ious_list.append(iou)
if iou >= max_iou_thr:
break
return clicker.clicks_list, np.array(ious_list, dtype=np.float32), pred_probs
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