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eval_utils.py
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eval_utils.py
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import pickle
import time
import numpy as np
from pcdet import models
import torch
import tqdm
from pcdet.models import load_data_to_gpu
from pcdet.utils import common_utils
from pcdet.models.model_utils import fusion_utils
def statistics_info(cfg, ret_dict, metric, disp_dict):
for key in metric.keys():
if key in ret_dict:
metric[key] += ret_dict[key]
min_thresh = cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST[0]
disp_dict['recall_%s' % str(min_thresh)] = \
'(%d, %d) / %d' % (metric['recall_roi_%s' % str(min_thresh)], metric['recall_rcnn_%s' % str(min_thresh)], metric['gt_num'])
def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, save_to_file=False, result_dir=None, fuse_conv_bn=False):
result_dir.mkdir(parents=True, exist_ok=True)
final_output_dir = result_dir / 'final_result' / 'data'
if save_to_file:
final_output_dir.mkdir(parents=True, exist_ok=True)
metric = {
'gt_num': 0,
}
for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
metric['recall_roi_%s' % str(cur_thresh)] = 0
metric['recall_rcnn_%s' % str(cur_thresh)] = 0
dataset = dataloader.dataset
class_names = dataset.class_names
det_annos = []
if fuse_conv_bn:
model = fusion_utils.fuse_module(model)
logger.info('*************** EPOCH %s EVALUATION *****************' % epoch_id)
if dist_test:
num_gpus = torch.cuda.device_count()
local_rank = cfg.LOCAL_RANK % num_gpus
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
broadcast_buffers=False
)
model.eval()
if cfg.LOCAL_RANK == 0:
progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval', dynamic_ncols=True)
start_time = time.time()
run_time = 0
for i, batch_dict in enumerate(dataloader):
torch.cuda.synchronize()
run_start_time = time.time()
load_data_to_gpu(batch_dict)
with torch.no_grad():
pred_dicts, ret_dict = model(batch_dict)
torch.cuda.synchronize()
run_end_time = time.time()
run_duration = run_end_time - run_start_time
run_time += run_duration
disp_dict = {}
statistics_info(cfg, ret_dict, metric, disp_dict)
annos = dataset.generate_prediction_dicts(
batch_dict, pred_dicts, class_names,
output_path=final_output_dir if save_to_file else None
)
det_annos += annos
if cfg.LOCAL_RANK == 0:
progress_bar.set_postfix(disp_dict)
progress_bar.update()
if cfg.LOCAL_RANK == 0:
progress_bar.close()
if dist_test:
rank, world_size = common_utils.get_dist_info()
det_annos = common_utils.merge_results_dist(det_annos, len(dataset), tmpdir=result_dir / 'tmpdir')
metric = common_utils.merge_results_dist([metric], world_size, tmpdir=result_dir / 'tmpdir')
else:
world_size = 1
logger.info('*************** Performance of EPOCH %s *****************' % epoch_id)
logger.info('Run time per sample: %.4f second.' % (run_time / (len(dataloader.dataset) / world_size)))
sec_per_example = (time.time() - start_time) / (len(dataloader.dataset) / world_size)
logger.info('Generate label finished(sec_per_example: %.4f second).' % sec_per_example)
if cfg.LOCAL_RANK != 0:
return {}
ret_dict = {}
if dist_test:
for key, val in metric[0].items():
for k in range(1, world_size):
metric[0][key] += metric[k][key]
metric = metric[0]
gt_num_cnt = metric['gt_num']
for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
cur_roi_recall = metric['recall_roi_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
cur_rcnn_recall = metric['recall_rcnn_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
logger.info('recall_roi_%s: %f' % (cur_thresh, cur_roi_recall))
logger.info('recall_rcnn_%s: %f' % (cur_thresh, cur_rcnn_recall))
ret_dict['recall/roi_%s' % str(cur_thresh)] = cur_roi_recall
ret_dict['recall/rcnn_%s' % str(cur_thresh)] = cur_rcnn_recall
total_pred_objects = 0
for anno in det_annos:
total_pred_objects += anno['name'].__len__()
logger.info('Average predicted number of objects(%d samples): %.3f'
% (len(det_annos), total_pred_objects / max(1, len(det_annos))))
with open(result_dir / 'result.pkl', 'wb') as f:
pickle.dump(det_annos, f)
result_str, result_dict = dataset.evaluation(
det_annos, class_names,
eval_metric=cfg.MODEL.POST_PROCESSING.EVAL_METRIC,
output_path=final_output_dir
)
logger.info(result_str)
ret_dict.update(result_dict)
logger.info('Result is save to %s' % result_dir)
logger.info('****************Evaluation done.*****************')
return ret_dict
if __name__ == '__main__':
pass