Save infer results to file #5792 (#5809)

* save infer results to file.

* update, for mac temp file

* update func name, fix mem, fix dir

.gitignore

@@ -82,3 +82,7 @@ ppdet/version.py
 
 # NPU meta folder
 kernel_meta/
+
+# MAC
+*.DS_Store
+

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
 
 
 

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

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):

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()

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():