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RE-OWOD
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@ -5,9 +5,14 @@ import time
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from collections import OrderedDict
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from contextlib import contextmanager
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import torch
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import numpy as np
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import pickle
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import sys
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import pdb
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from detectron2.utils.comm import get_world_size, is_main_process
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from detectron2.utils.logger import log_every_n_seconds
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from detectron2.structures import pairwise_iou
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class DatasetEvaluator:
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@ -98,7 +103,10 @@ class DatasetEvaluators(DatasetEvaluator):
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return results
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def inference_on_dataset(model, data_loader, evaluator,GENERATE_CALI):
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class_score = [[] for _ in range(21)]
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def inference_on_dataset(cfg, model, data_loader, evaluator):
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"""
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Run model on the data_loader and evaluate the metrics with evaluator.
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Also benchmark the inference speed of `model.forward` accurately.
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@ -118,7 +126,6 @@ def inference_on_dataset(model, data_loader, evaluator,GENERATE_CALI):
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Returns:
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The return value of `evaluator.evaluate()`
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"""
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global class_score
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num_devices = get_world_size()
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@ -141,44 +148,41 @@ def inference_on_dataset(model, data_loader, evaluator,GENERATE_CALI):
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total_compute_time = 0
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start_compute_time = time.perf_counter()
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outputs = model(inputs)
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if GENERATE_CALI is True:
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single_input = inputs[0]['instances']
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single_res = outputs[0]['instances']
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if len(single_res) > 0:
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match_quality_matrix = pairwise_iou(
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single_input.gt_boxes.to(single_res.pred_boxes.device), single_res.pred_boxes
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)
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# match_quality_matrix is M (gt) x N (predicted)
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# Max over gt elements (dim 0) to find best gt candidate for each prediction
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matched_vals, matches = match_quality_matrix.max(dim=0)
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# TODO: add some check to analazy =======================
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for i in range(len(matched_vals)): # N, prediction
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pre_iou = matched_vals[i] # max iou
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pre_gt_class = single_input.gt_classes[matches[i]].to(single_res.pred_boxes.device)
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pre_res_class_score = single_res.scores[i]
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pre_res_class = single_res.pred_classes[i]
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if pre_iou < 0.7:
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# prediction's iou is not enough
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# if pre_gt_class == 5 or pre_gt_class == 18:
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# # 5 and 14 confuse
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# print("in iou, < 0.5, gt class:{}, pred class:{}, ".format(pre_gt_class, pre_res_class))
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# print(pre_res_class_score)
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continue
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# has intersection with gt box
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if pre_gt_class == pre_res_class:
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class_score[pre_gt_class].append(pre_res_class_score)
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# logger.info(
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# "class_score:{}, iou_below_but_right:{}, unknown_to_bg:{}, all_bg:{}, true_bg:{}".format(
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# "None", iou_below_but_right, unknown_to_bg, all_bg, true_bg
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# )
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# )
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# ================================================================
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# if cfg.OWOD.GENERATE_CALI:
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# # for testing class average scores
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# # inputs[0]: {'file_name', 'image_id', 'height', 'width', 'image'}
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# # outputs[0]: {'instances'}
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# # ================================================================
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# single_input = inputs[0]['instances']
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# single_res = outputs[0]['instances']
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# if len(single_res) > 0:
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# match_quality_matrix = pairwise_iou(
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# single_input.gt_boxes.to(single_res.pred_boxes.device), single_res.pred_boxes
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# )
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# # match_quality_matrix is M (gt) x N (predicted)
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# # Max over gt elements (dim 0) to find best gt candidate for each prediction
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# matched_vals, matches = match_quality_matrix.max(dim=0)
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# # TODO: add some check to analazy =======================
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# for i in range(len(matched_vals)): # N, prediction
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# pre_iou = matched_vals[i] # max iou
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# pre_gt_class = single_input.gt_classes[matches[i]].to(single_res.pred_boxes.device)
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# pre_res_class_score = single_res.scores[i]
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# pre_res_class = single_res.pred_classes[i]
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# # prediction's iou is not enough
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# if pre_iou < cfg.OWOD.CALIBRATION:
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# continue
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# # submit gt boxes
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# if pre_gt_class == pre_res_class:
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# class_score[pre_gt_class].append(pre_res_class_score)
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#
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# cali_path = cfg.OWOD.CALI_PATH + '.pickle'
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# score_file = open(cali_path, 'wb')
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# pickle.dump(class_score, score_file)
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# score_file.close()
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# # ================================================================
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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@ -213,10 +217,6 @@ def inference_on_dataset(model, data_loader, evaluator,GENERATE_CALI):
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total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
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)
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)
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if GENERATE_CALI is True:
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score_file = open('analyze/1122/t3_ori_set_train_scores_cali_07.pickle', 'wb')
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pickle.dump(class_score, score_file)
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score_file.close()
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results = evaluator.evaluate()
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# An evaluator may return None when not in main process.
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