Modify file structure to separate python and cpp code (#223)

Modify code structure

python/fastdeploy/vision/evaluation/utils/coco_metrics.py

@@ -0,0 +1,89 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import sys
+from collections import OrderedDict
+from .coco_utils import get_infer_results, cocoapi_eval
+
+
+class COCOMetric(object):
+    def __init__(self, coco_gt, **kwargs):
+        self.clsid2catid = {
+            i: cat['id']
+            for i, cat in enumerate(coco_gt.loadCats(coco_gt.getCatIds()))
+        }
+        self.coco_gt = coco_gt
+        self.classwise = kwargs.get('classwise', False)
+        self.bias = 0
+        self.reset()
+
+    def reset(self):
+        # only bbox and mask evaluation support currently
+        self.details = {
+            'gt': copy.deepcopy(self.coco_gt.dataset),
+            'bbox': [],
+            'mask': []
+        }
+        self.eval_stats = {}
+
+    def update(self, im_id, outputs):
+        outs = {}
+        # outputs Tensor -> numpy.ndarray
+        for k, v in outputs.items():
+            outs[k] = v
+
+        outs['im_id'] = im_id
+        infer_results = get_infer_results(
+            outs, self.clsid2catid, bias=self.bias)
+        self.details['bbox'] += infer_results[
+            'bbox'] if 'bbox' in infer_results else []
+        self.details['mask'] += infer_results[
+            'mask'] if 'mask' in infer_results else []
+
+    def accumulate(self):
+        if len(self.details['bbox']) > 0:
+            bbox_stats = cocoapi_eval(
+                copy.deepcopy(self.details['bbox']),
+                'bbox',
+                coco_gt=self.coco_gt,
+                classwise=self.classwise)
+            self.eval_stats['bbox'] = bbox_stats
+            sys.stdout.flush()
+
+        if len(self.details['mask']) > 0:
+            seg_stats = cocoapi_eval(
+                copy.deepcopy(self.details['mask']),
+                'segm',
+                coco_gt=self.coco_gt,
+                classwise=self.classwise)
+            self.eval_stats['mask'] = seg_stats
+            sys.stdout.flush()
+
+    def log(self):
+        pass
+
+    def get(self):
+        if 'bbox' not in self.eval_stats:
+            return {'bbox_mmap': 0.}
+        if 'mask' in self.eval_stats:
+            return OrderedDict(
+                zip(['bbox_mmap', 'segm_mmap'],
+                    [self.eval_stats['bbox'][0], self.eval_stats['mask'][0]]))
+        else:
+            return {'bbox_mmap': self.eval_stats['bbox'][0]}