diff --git a/.gitignore b/.gitignore index 2260189a0aa6105c5ead510efe24f27dfb046882..6a98a38b72ef9a59a2fdab266697661cdd1fa136 100644 --- a/.gitignore +++ b/.gitignore @@ -82,3 +82,7 @@ ppdet/version.py # NPU meta folder kernel_meta/ + +# MAC +*.DS_Store + diff --git a/docs/tutorials/GETTING_STARTED.md b/docs/tutorials/GETTING_STARTED.md index cdca9fbb8ec7022b11ab2f039acf6b6915957221..85279488a99680e031afdc09526fd02143131a11 100644 --- a/docs/tutorials/GETTING_STARTED.md +++ b/docs/tutorials/GETTING_STARTED.md @@ -53,7 +53,7 @@ list below can be viewed by `--help` | --draw_threshold | infer | Threshold to reserve the result for visualization | 0.5 | such as `--draw_threshold 0.7` | | --infer_dir | infer | Directory for images to perform inference on | None | One of `infer_dir` and `infer_img` is requied | | --infer_img | infer | Image path | None | One of `infer_dir` and `infer_img` is requied, `infer_img` has higher priority over `infer_dir` | - +| --save_results | infer | Whether to save detection results to file | False | Optional diff --git a/docs/tutorials/GETTING_STARTED_cn.md b/docs/tutorials/GETTING_STARTED_cn.md index ea94a5e09b4ef04edd4c415808faea81bdc6fba1..8d10683f6e2b09c0e5e56b4b5dfa6f6ef0aff191 100644 --- a/docs/tutorials/GETTING_STARTED_cn.md +++ b/docs/tutorials/GETTING_STARTED_cn.md @@ -215,7 +215,7 @@ visualdl --logdir vdl_dir/scalar/ | --draw_threshold | infer | 可视化时分数阈值 | 0.5 | 例如`--draw_threshold=0.7` | | --infer_dir | infer | 用于预测的图片文件夹路径 | None | `--infer_img`和`--infer_dir`必须至少设置一个 | | --infer_img | infer | 用于预测的图片路径 | None | `--infer_img`和`--infer_dir`必须至少设置一个，`infer_img`具有更高优先级 | -| --save_txt | infer | 是否在文件夹下将图片的预测结果保存到文本文件中 | False | 可选 | +| --save_results | infer | 是否在文件夹下将图片的预测结果保存到文件中 | False | 可选 | ## 8 模型导出 diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index 0f2f21359cee93b9ff9b885a727c1b5acb24debc..6697d3c4111d1d6a370a198791aac60c8cf17231 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -25,6 +25,7 @@ from tqdm import tqdm import numpy as np import typing from PIL import Image, ImageOps, ImageFile + ImageFile.LOAD_TRUNCATED_IMAGES = True import paddle @@ -552,10 +553,45 @@ class Trainer(object): images, draw_threshold=0.5, output_dir='output', - save_txt=False): + save_results=False): self.dataset.set_images(images) loader = create('TestReader')(self.dataset, 0) + def setup_metrics_for_loader(): + # mem + metrics = copy.deepcopy(self._metrics) + mode = self.mode + save_prediction_only = self.cfg[ + 'save_prediction_only'] if 'save_prediction_only' in self.cfg else None + output_eval = self.cfg[ + 'output_eval'] if 'output_eval' in self.cfg else None + + # modify + self.mode = '_test' + self.cfg['save_prediction_only'] = True + self.cfg['output_eval'] = output_dir + self._init_metrics() + + # restore + self.mode = mode + self.cfg.pop('save_prediction_only') + if save_prediction_only is not None: + self.cfg['save_prediction_only'] = save_prediction_only + + self.cfg.pop('output_eval') + if output_eval is not None: + self.cfg['output_eval'] = output_eval + + _metrics = copy.deepcopy(self._metrics) + self._metrics = metrics + + return _metrics + + if save_results: + metrics = setup_metrics_for_loader() + else: + metrics = [] + imid2path = self.dataset.get_imid2path() anno_file = self.dataset.get_anno() @@ -574,6 +610,9 @@ class Trainer(object): # forward outs = self.model(data) + for _m in metrics: + _m.update(data, outs) + for key in ['im_shape', 'scale_factor', 'im_id']: if isinstance(data, typing.Sequence): outs[key] = data[0][key] @@ -583,11 +622,16 @@ class Trainer(object): if hasattr(value, 'numpy'): outs[key] = value.numpy() results.append(outs) + # sniper if type(self.dataset) == SniperCOCODataSet: results = self.dataset.anno_cropper.aggregate_chips_detections( results) + for _m in metrics: + _m.accumulate() + _m.reset() + for outs in results: batch_res = get_infer_results(outs, clsid2catid) bbox_num = outs['bbox_num'] @@ -619,15 +663,7 @@ class Trainer(object): logger.info("Detection bbox results save in {}".format( save_name)) image.save(save_name, quality=95) - if save_txt: - save_path = os.path.splitext(save_name)[0] + '.txt' - results = {} - results["im_id"] = im_id - if bbox_res: - results["bbox_res"] = bbox_res - if keypoint_res: - results["keypoint_res"] = keypoint_res - save_result(save_path, results, catid2name, draw_threshold) + start = end def _get_save_image_name(self, output_dir, image_path): diff --git a/ppdet/metrics/metrics.py b/ppdet/metrics/metrics.py index 3925267d7f9e5656033e4c851d8b52f1031867ab..4ec5e5d39581f46f9d7e105ee2b56e3ca6be556a 100644 --- a/ppdet/metrics/metrics.py +++ b/ppdet/metrics/metrics.py @@ -22,6 +22,7 @@ import json import paddle import numpy as np import typing +from pathlib import Path from .map_utils import prune_zero_padding, DetectionMAP from .coco_utils import get_infer_results, cocoapi_eval @@ -32,13 +33,8 @@ from ppdet.utils.logger import setup_logger logger = setup_logger(__name__) __all__ = [ - 'Metric', - 'COCOMetric', - 'VOCMetric', - 'WiderFaceMetric', - 'get_infer_results', - 'RBoxMetric', - 'SNIPERCOCOMetric' + 'Metric', 'COCOMetric', 'VOCMetric', 'WiderFaceMetric', 'get_infer_results', + 'RBoxMetric', 'SNIPERCOCOMetric' ] COCO_SIGMAS = np.array([ @@ -74,8 +70,6 @@ class Metric(paddle.metric.Metric): class COCOMetric(Metric): def __init__(self, anno_file, **kwargs): - assert os.path.isfile(anno_file), \ - "anno_file {} not a file".format(anno_file) self.anno_file = anno_file self.clsid2catid = kwargs.get('clsid2catid', None) if self.clsid2catid is None: @@ -86,6 +80,14 @@ class COCOMetric(Metric): self.bias = kwargs.get('bias', 0) self.save_prediction_only = kwargs.get('save_prediction_only', False) self.iou_type = kwargs.get('IouType', 'bbox') + + if not self.save_prediction_only: + assert os.path.isfile(anno_file), \ + "anno_file {} not a file".format(anno_file) + + if self.output_eval is not None: + Path(self.output_eval).mkdir(exist_ok=True) + self.reset() def reset(self): @@ -427,11 +429,13 @@ class SNIPERCOCOMetric(COCOMetric): self.chip_results.append(outs) - def accumulate(self): - results = self.dataset.anno_cropper.aggregate_chips_detections(self.chip_results) + results = self.dataset.anno_cropper.aggregate_chips_detections( + self.chip_results) for outs in results: - infer_results = get_infer_results(outs, self.clsid2catid, bias=self.bias) - self.results['bbox'] += infer_results['bbox'] if 'bbox' in infer_results else [] + infer_results = get_infer_results( + outs, self.clsid2catid, bias=self.bias) + self.results['bbox'] += infer_results[ + 'bbox'] if 'bbox' in infer_results else [] super(SNIPERCOCOMetric, self).accumulate() diff --git a/tools/infer.py b/tools/infer.py index 406171f2b682fc0355b7725ccfb21a4602d8dabd..1980ced033b44253d5ee6311ceef819bcdda4b7c 100755 --- a/tools/infer.py +++ b/tools/infer.py @@ -77,10 +77,10 @@ def parse_args(): default="vdl_log_dir/image", help='VisualDL logging directory for image.') parser.add_argument( - "--save_txt", + "--save_results", type=bool, default=False, - help="Whether to save inference result in txt.") + help="Whether to save inference results to output_dir.") args = parser.parse_args() return args @@ -131,7 +131,7 @@ def run(FLAGS, cfg): images, draw_threshold=FLAGS.draw_threshold, output_dir=FLAGS.output_dir, - save_txt=FLAGS.save_txt) + save_results=FLAGS.save_results) def main():