Merge pull request #806 from will-jl944/develop_jf

import json_results from ppdet

dygraph/PaddleDetection

@@ -1 +1 @@
-Subproject commit c987dc1e543f1e489a32b165d7078b591d2ca363
+Subproject commit 6ec36068ee450df33c0038fb0cdc575b21510acd

dygraph/paddlex/cv/models/utils/det_metrics/coco_utils.py

@@ -21,8 +21,7 @@ import copy
 import numpy as np
 import itertools
 from ppdet.metrics.map_utils import draw_pr_curve
-from .json_results import get_det_res, get_det_poly_res, get_seg_res, get_solov2_segm_res
-
+from ppdet.metrics.json_results import get_det_res, get_det_poly_res, get_seg_res, get_solov2_segm_res
 import paddlex.utils.logging as logging
 
 

dygraph/paddlex/cv/models/utils/det_metrics/json_results.py

@@ -1,126 +0,0 @@
-#   Copyright (c) 2021 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.
-
-import six
-import numpy as np
-
-
-def get_det_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
-    det_res = []
-    k = 0
-    for i in range(len(bbox_nums)):
-        cur_image_id = int(image_id[i][0])
-        det_nums = bbox_nums[i]
-        for j in range(det_nums):
-            dt = bboxes[k]
-            k = k + 1
-            num_id, score, xmin, ymin, xmax, ymax = dt.tolist()
-            if int(num_id) < 0:
-                continue
-            category_id = label_to_cat_id_map[int(num_id)]
-            w = xmax - xmin + bias
-            h = ymax - ymin + bias
-            bbox = [xmin, ymin, w, h]
-            dt_res = {
-                'image_id': cur_image_id,
-                'category_id': category_id,
-                'bbox': bbox,
-                'score': score
-            }
-            det_res.append(dt_res)
-    return det_res
-
-
-def get_det_poly_res(bboxes, bbox_nums, image_id, label_to_cat_id_map, bias=0):
-    det_res = []
-    k = 0
-    for i in range(len(bbox_nums)):
-        cur_image_id = int(image_id[i][0])
-        det_nums = bbox_nums[i]
-        for j in range(det_nums):
-            dt = bboxes[k]
-            k = k + 1
-            num_id, score, x1, y1, x2, y2, x3, y3, x4, y4 = dt.tolist()
-            if int(num_id) < 0:
-                continue
-            category_id = label_to_cat_id_map[int(num_id)]
-            rbox = [x1, y1, x2, y2, x3, y3, x4, y4]
-            dt_res = {
-                'image_id': cur_image_id,
-                'category_id': category_id,
-                'bbox': rbox,
-                'score': score
-            }
-            det_res.append(dt_res)
-    return det_res
-
-
-def get_seg_res(masks, bboxes, mask_nums, image_id, label_to_cat_id_map):
-    import pycocotools.mask as mask_util
-    seg_res = []
-    k = 0
-    for i in range(len(mask_nums)):
-        cur_image_id = int(image_id[i][0])
-        det_nums = mask_nums[i]
-        for j in range(det_nums):
-            mask = masks[k].astype(np.uint8)
-            score = float(bboxes[k][1])
-            label = int(bboxes[k][0])
-            k = k + 1
-            if label == -1:
-                continue
-            cat_id = label_to_cat_id_map[label]
-            rle = mask_util.encode(
-                np.array(
-                    mask[:, :, None], order="F", dtype="uint8"))[0]
-            if six.PY3:
-                if 'counts' in rle:
-                    rle['counts'] = rle['counts'].decode("utf8")
-            sg_res = {
-                'image_id': cur_image_id,
-                'category_id': cat_id,
-                'segmentation': rle,
-                'score': score
-            }
-            seg_res.append(sg_res)
-    return seg_res
-
-
-def get_solov2_segm_res(results, image_id, num_id_to_cat_id_map):
-    import pycocotools.mask as mask_util
-    segm_res = []
-    # for each batch
-    segms = results['segm'].astype(np.uint8)
-    clsid_labels = results['cate_label']
-    clsid_scores = results['cate_score']
-    lengths = segms.shape[0]
-    im_id = int(image_id[0][0])
-    if lengths == 0 or segms is None:
-        return None
-    # for each sample
-    for i in range(lengths - 1):
-        clsid = int(clsid_labels[i])
-        catid = num_id_to_cat_id_map[clsid]
-        score = float(clsid_scores[i])
-        mask = segms[i]
-        segm = mask_util.encode(np.array(mask[:, :, np.newaxis], order='F'))[0]
-        segm['counts'] = segm['counts'].decode('utf8')
-        coco_res = {
-            'image_id': im_id,
-            'category_id': catid,
-            'segmentation': segm,
-            'score': score
-        }
-        segm_res.append(coco_res)
-    return segm_res