sorted outputs when export model
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@ -142,8 +142,8 @@ class TextDetector(object):
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outputs.append(output)
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outs_dict = {}
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if self.det_algorithm == "EAST":
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outs_dict['f_score'] = outputs[0]
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outs_dict['f_geo'] = outputs[1]
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outs_dict['f_geo'] = outputs[0]
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outs_dict['f_score'] = outputs[1]
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else:
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outs_dict['maps'] = outputs[0]
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dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
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@ -169,14 +169,7 @@ if __name__ == "__main__":
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total_time += elapse
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count += 1
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print("Predict time of %s:" % image_file, elapse)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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draw_img = draw_ocr(img, dt_boxes, None, None, False)
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draw_img_save = "./inference_results/"
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if not os.path.exists(draw_img_save):
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os.makedirs(draw_img_save)
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cv2.imwrite(
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os.path.join(draw_img_save, os.path.basename(image_file)),
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draw_img[:, :, ::-1])
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print("The visualized image saved in {}".format(
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os.path.join(draw_img_save, os.path.basename(image_file))))
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"""
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add visualized code
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"""
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print("Avg Time:", total_time / (count - 1))
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@ -114,7 +114,6 @@ if __name__ == "__main__":
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valid_image_file_list.append(image_file)
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img_list.append(img)
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rec_res, predict_time = text_recognizer(img_list)
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rec_res, predict_time = text_recognizer(img_list)
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for ino in range(len(img_list)):
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print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
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print("Total predict time for %d images:%.3f" %
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@ -191,8 +191,8 @@ def build_export(config, main_prog, startup_prog):
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func_infor = config['Architecture']['function']
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model = create_module(func_infor)(params=config)
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image, outputs = model(mode='export')
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fetches_var = [outputs[name] for name in outputs]
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fetches_var_name = [name for name in outputs]
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fetches_var = sorted([outputs[name] for name in outputs])
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fetches_var_name = [name for name in fetches_var]
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feeded_var_names = [image.name]
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target_vars = fetches_var
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return feeded_var_names, target_vars, fetches_var_name
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