fix amp train for re

tools/infer/predict_det_eval.py

-# Copyright (c) 2020 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 os
-import sys
-
-__dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
-
-os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
-
-import cv2
-import numpy as np
-import time
-import sys
-
-import tools.infer.utility as utility
-from ppocr.utils.logging import get_logger
-from ppocr.utils.utility import get_image_file_list, check_and_read_gif
-from ppocr.data import create_operators, transform
-from ppocr.postprocess import build_post_process
-import json
-logger = get_logger()
-
-
-class TextDetector(object):
-    def __init__(self, args):
-        self.args = args
-        self.det_algorithm = args.det_algorithm
-        self.use_onnx = args.use_onnx
-        pre_process_list = [{
-            'DetResizeForTest': {
-                'limit_side_len': args.det_limit_side_len,
-                'limit_type': args.det_limit_type,
-            }
-        }, {
-            'NormalizeImage': {
-                'std': [0.229, 0.224, 0.225],
-                'mean': [0.485, 0.456, 0.406],
-                'scale': '1./255.',
-                'order': 'hwc'
-            }
-        }, {
-            'ToCHWImage': None
-        }, {
-            'KeepKeys': {
-                'keep_keys': ['image', 'shape']
-            }
-        }]
-        postprocess_params = {}
-        if self.det_algorithm == "DB":
-            postprocess_params['name'] = 'DBPostProcess'
-            postprocess_params["thresh"] = args.det_db_thresh
-            postprocess_params["box_thresh"] = args.det_db_box_thresh
-            postprocess_params["max_candidates"] = 1000
-            postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
-            postprocess_params["use_dilation"] = args.use_dilation
-            postprocess_params["score_mode"] = args.det_db_score_mode
-        elif self.det_algorithm == "EAST":
-            postprocess_params['name'] = 'EASTPostProcess'
-            postprocess_params["score_thresh"] = args.det_east_score_thresh
-            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
-            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
-        elif self.det_algorithm == "SAST":
-            pre_process_list[0] = {
-                'DetResizeForTest': {
-                    'resize_long': args.det_limit_side_len
-                }
-            }
-            postprocess_params['name'] = 'SASTPostProcess'
-            postprocess_params["score_thresh"] = args.det_sast_score_thresh
-            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
-            self.det_sast_polygon = args.det_sast_polygon
-            if self.det_sast_polygon:
-                postprocess_params["sample_pts_num"] = 6
-                postprocess_params["expand_scale"] = 1.2
-                postprocess_params["shrink_ratio_of_width"] = 0.2
-            else:
-                postprocess_params["sample_pts_num"] = 2
-                postprocess_params["expand_scale"] = 1.0
-                postprocess_params["shrink_ratio_of_width"] = 0.3
-        elif self.det_algorithm == "PSE":
-            postprocess_params['name'] = 'PSEPostProcess'
-            postprocess_params["thresh"] = args.det_pse_thresh
-            postprocess_params["box_thresh"] = args.det_pse_box_thresh
-            postprocess_params["min_area"] = args.det_pse_min_area
-            postprocess_params["box_type"] = args.det_pse_box_type
-            postprocess_params["scale"] = args.det_pse_scale
-            self.det_pse_box_type = args.det_pse_box_type
-        elif self.det_algorithm == "FCE":
-            pre_process_list[0] = {
-                'DetResizeForTest': {
-                    'rescale_img': [1080, 736]
-                }
-            }
-            postprocess_params['name'] = 'FCEPostProcess'
-            postprocess_params["scales"] = args.scales
-            postprocess_params["alpha"] = args.alpha
-            postprocess_params["beta"] = args.beta
-            postprocess_params["fourier_degree"] = args.fourier_degree
-            postprocess_params["box_type"] = args.det_fce_box_type
-        else:
-            logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
-            sys.exit(0)
-
-        self.preprocess_op = create_operators(pre_process_list)
-        self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
-            args, 'det', logger)
-
-        if self.use_onnx:
-            img_h, img_w = self.input_tensor.shape[2:]
-            if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
-                pre_process_list[0] = {
-                    'DetResizeForTest': {
-                        'image_shape': [img_h, img_w]
-                    }
-                }
-        self.preprocess_op = create_operators(pre_process_list)
-
-        if args.benchmark:
-            import auto_log
-            pid = os.getpid()
-            gpu_id = utility.get_infer_gpuid()
-            self.autolog = auto_log.AutoLogger(
-                model_name="det",
-                model_precision=args.precision,
-                batch_size=1,
-                data_shape="dynamic",
-                save_path=None,
-                inference_config=self.config,
-                pids=pid,
-                process_name=None,
-                gpu_ids=gpu_id if args.use_gpu else None,
-                time_keys=[
-                    'preprocess_time', 'inference_time', 'postprocess_time'
-                ],
-                warmup=2,
-                logger=logger)
-
-    def order_points_clockwise(self, pts):
-        rect = np.zeros((4, 2), dtype="float32")
-        s = pts.sum(axis=1)
-        rect[0] = pts[np.argmin(s)]
-        rect[2] = pts[np.argmax(s)]
-        tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
-        diff = np.diff(np.array(tmp), axis=1)
-        rect[1] = tmp[np.argmin(diff)]
-        rect[3] = tmp[np.argmax(diff)]
-        return rect
-
-    def clip_det_res(self, points, img_height, img_width):
-        for pno in range(points.shape[0]):
-            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
-            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
-        return points
-
-    def filter_tag_det_res(self, dt_boxes, image_shape):
-        img_height, img_width = image_shape[0:2]
-        dt_boxes_new = []
-        for box in dt_boxes:
-            box = self.order_points_clockwise(box)
-            box = self.clip_det_res(box, img_height, img_width)
-            rect_width = int(np.linalg.norm(box[0] - box[1]))
-            rect_height = int(np.linalg.norm(box[0] - box[3]))
-            if rect_width <= 3 or rect_height <= 3:
-                continue
-            dt_boxes_new.append(box)
-        dt_boxes = np.array(dt_boxes_new)
-        return dt_boxes
-
-    def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
-        img_height, img_width = image_shape[0:2]
-        dt_boxes_new = []
-        for box in dt_boxes:
-            box = self.clip_det_res(box, img_height, img_width)
-            dt_boxes_new.append(box)
-        dt_boxes = np.array(dt_boxes_new)
-        return dt_boxes
-
-    def __call__(self, img):
-        ori_im = img.copy()
-        data = {'image': img}
-
-        st = time.time()
-
-        if self.args.benchmark:
-            self.autolog.times.start()
-
-        data = transform(data, self.preprocess_op)
-        img, shape_list = data
-        if img is None:
-            return None, 0
-        img = np.expand_dims(img, axis=0)
-        shape_list = np.expand_dims(shape_list, axis=0)
-        img = img.copy()
-
-        if self.args.benchmark:
-            self.autolog.times.stamp()
-        if self.use_onnx:
-            input_dict = {}
-            input_dict[self.input_tensor.name] = img
-            outputs = self.predictor.run(self.output_tensors, input_dict)
-        else:
-            self.input_tensor.copy_from_cpu(img)
-            self.predictor.run()
-            outputs = []
-            for output_tensor in self.output_tensors:
-                output = output_tensor.copy_to_cpu()
-                outputs.append(output)
-            if self.args.benchmark:
-                self.autolog.times.stamp()
-
-        preds = {}
-        if self.det_algorithm == "EAST":
-            preds['f_geo'] = outputs[0]
-            preds['f_score'] = outputs[1]
-        elif self.det_algorithm == 'SAST':
-            preds['f_border'] = outputs[0]
-            preds['f_score'] = outputs[1]
-            preds['f_tco'] = outputs[2]
-            preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm in ['DB', 'PSE']:
-            preds['maps'] = outputs[0]
-        elif self.det_algorithm == 'FCE':
-            for i, output in enumerate(outputs):
-                preds['level_{}'.format(i)] = output
-        else:
-            raise NotImplementedError
-
-        #self.predictor.try_shrink_memory()
-        post_result = self.postprocess_op(preds, shape_list)
-        dt_boxes = post_result[0]['points']
-        if (self.det_algorithm == "SAST" and self.det_sast_polygon) or (
-                self.det_algorithm in ["PSE", "FCE"] and
-                self.postprocess_op.box_type == 'poly'):
-            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
-        else:
-            dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
-
-        if self.args.benchmark:
-            self.autolog.times.end(stamp=True)
-        et = time.time()
-        return dt_boxes, et - st
-
-
-if __name__ == "__main__":
-    from ppocr.metrics.eval_det_iou import DetectionIoUEvaluator
-    evaluator = DetectionIoUEvaluator()
-    args = utility.parse_args()
-
-    # image_file_list = get_image_file_list(args.image_dir)
-    def _check_image_file(path):
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        return any([path.lower().endswith(e) for e in img_end])
-
-    def get_image_file_list_from_txt(img_file):
-        imgs_lists = []
-        label_lists = []
-        if img_file is None or not os.path.exists(img_file):
-            raise Exception("not found any img file in {}".format(img_file))
-
-        img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-        root_dir = img_file.split('/')[0]
-        with open(img_file, 'r') as f:
-            lines = f.readlines()
-            for line in lines:
-                line = line.replace('\n', '').split('\t')
-                file_path, label = line[0], line[1]
-                file_path = os.path.join(root_dir, file_path)
-                if os.path.isfile(file_path) and _check_image_file(file_path):
-                    imgs_lists.append(file_path)
-                    label_lists.append(label)
-
-        if len(imgs_lists) == 0:
-            raise Exception("not found any img file in {}".format(img_file))
-        return imgs_lists, label_lists
-
-    image_file_list, label_list = get_image_file_list_from_txt(args.image_dir)
-
-    text_detector = TextDetector(args)
-    count = 0
-    total_time = 0
-    draw_img_save = "./inference_results"
-
-    if args.warmup:
-        img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
-        for i in range(2):
-            res = text_detector(img)
-
-    if not os.path.exists(draw_img_save):
-        os.makedirs(draw_img_save)
-    save_results = []
-    results = []
-    for idx in range(len(image_file_list)):
-        image_file = image_file_list[idx]
-        label = json.loads(label_list[idx])
-        img, flag = check_and_read_gif(image_file)
-        if not flag:
-            img = cv2.imread(image_file)
-        if img is None:
-            logger.info("error in loading image:{}".format(image_file))
-            continue
-        st = time.time()
-        dt_boxes, _ = text_detector(img)
-        elapse = time.time() - st
-        if count > 0:
-            total_time += elapse
-        count += 1
-        save_pred = os.path.basename(image_file) + "\t" + str(
-            json.dumps([x.tolist() for x in dt_boxes])) + "\n"
-        save_results.append(save_pred)
-
-        # for eval
-        gt_info_list = []
-        det_info_list = []
-        for dt_box in dt_boxes:
-            det_info = {
-                'points': np.array(
-                    dt_box, dtype=np.float32),
-                'text': ''
-            }
-            det_info_list.append(det_info)
-        for lab in label:
-            gt_info = {
-                'points': np.array(
-                    lab['points'], dtype=np.float32),
-                'text': '',
-                'ignore': False
-            }
-            gt_info_list.append(gt_info)
-        result = evaluator.evaluate_image(gt_info_list, det_info_list)
-        results.append(result)
-
-    metrics = evaluator.combine_results(results)
-    print('predict det eval on ', args.image_dir)
-    print('metrics: ', metrics)
-
-# logger.info(save_pred)
-# logger.info("The predict time of {}: {}".format(image_file, elapse))
-# src_im = utility.draw_text_det_res(dt_boxes, image_file)
-# img_name_pure = os.path.split(image_file)[-1]
-# img_path = os.path.join(draw_img_save,
-#                         "det_res_{}".format(img_name_pure))
-# cv2.imwrite(img_path, src_im)
-# logger.info("The visualized image saved in {}".format(img_path))
-
-# with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
-#     f.writelines(save_results)
-#     f.close()
-# if args.benchmark:
-#     text_detector.autolog.report()