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Merge pull request #7217 from andyjpaddle/fix_sar_export

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fix sar export
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andyjpaddle 2022-08-16 20:39:28 +08:00 committed by GitHub
commit 8210d717ff
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4 changed files with 7 additions and 4 deletions
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2
doc/doc_ch/algorithm_rec_sar.md
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@ -79,7 +79,7 @@ python3 tools/export_model.py -c configs/rec/rec_r31_sar.yml -o Global.pretraine
SAR文本识别模型推理，可以执行如下命令：
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_char_type="ch" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
```
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2
doc/doc_en/algorithm_rec_sar_en.md
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@ -79,7 +79,7 @@ python3 tools/export_model.py -c configs/rec/rec_r31_sar.yml -o Global.pretraine
For SAR text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_char_type="ch" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/en/word_1.png" --rec_model_dir="./inference/rec_sar/" --rec_image_shape="3, 48, 48, 160" --rec_algorithm="SAR" --rec_char_dict_path="ppocr/utils/dict90.txt" --max_text_length=30 --use_space_char=False
```
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2
tools/export_model.py
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@ -58,6 +58,8 @@ def export_single_model(model,
other_shape = [
paddle.static.InputSpec(
shape=[None, 3, 48, 160], dtype="float32"),
[paddle.static.InputSpec(
shape=[None], dtype="float32")]
]
model = to_static(model, input_spec=other_shape)
elif arch_config["algorithm"] == "SVTR":

3
tools/infer/predict_rec.py
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@ -439,7 +439,8 @@ class TextRecognizer(object):
valid_ratios = np.concatenate(valid_ratios)
inputs = [
norm_img_batch,
valid_ratios,
np.array(
[valid_ratios], dtype=np.float32),
]
if self.use_onnx:
input_dict = {}