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Merge pull request #7206 from andyjpaddle/fix_vl

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Fix visionlan predict format
This commit is contained in:
贾晓
2022-08-15 18:57:45 +08:00
committed by GitHub
parent 2c1afec3af c9504a9425
commit edd18b6743
5 changed files with 16 additions and 16 deletions
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doc/doc_ch/algorithm_rec_visionlan.md
+2 -2
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@@ -101,7 +101,7 @@ python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pre
执行如下命令进行模型推理：
```shell
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt'
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False
# 预测文件夹下所有图像时，可修改image_dir为文件夹，如 --image_dir='./doc/imgs_words_en/'。
```
@@ -110,7 +110,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png'
执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
结果如下：
```shell
Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982)
Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)
```
**注意**：
doc/doc_en/algorithm_rec_visionlan_en.md
+2 -2
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@@ -90,7 +90,7 @@ After the conversion is successful, there are three files in the directory:
For VisionLAN text recognition model inference, the following commands can be executed:
```
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/dict36.txt'
python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png' --rec_model_dir='./inference/rec_r45_visionlan/' --rec_algorithm='VisionLAN' --rec_image_shape='3,64,256' --rec_char_dict_path='./ppocr/utils/ic15_dict.txt' --use_space_char=False
```
![](../imgs_words/en/word_2.png)
@@ -98,7 +98,7 @@ python3 tools/infer/predict_rec.py --image_dir='./doc/imgs_words/en/word_2.png'
After executing the command, the prediction result (recognized text and score) of the image above is printed to the screen, an example is as follows:
The result is as follows:
```shell
Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.97076982)
Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.9999493)
```
<a name="4-2"></a>
ppocr/losses/__init__.py
+1 -1
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@@ -67,7 +67,7 @@ def build_loss(config):
'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss','StrokeFocusLoss'
'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss', 'StrokeFocusLoss'
]
config = copy.deepcopy(config)
module_name = config.pop('name')
ppocr/postprocess/rec_postprocess.py
+1 -1
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@@ -780,7 +780,7 @@ class VLLabelDecode(BaseRecLabelDecode):
) + length[i])].topk(1)[0][:, 0]
preds_prob = paddle.exp(
paddle.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6))
text.append((preds_text, preds_prob))
text.append((preds_text, preds_prob.numpy()[0]))
if label is None:
return text
label = self.decode(label)
tools/program.py
+10 -10
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@@ -490,7 +490,7 @@ def eval(model,
break
images = batch[0]
start = time.time()
# use amp
if scaler:
with paddle.amp.auto_cast(level='O2'):
@@ -508,10 +508,10 @@ def eval(model,
1, 2, 0).astype(np.uint8)
fm_lr = (lr_img[i].numpy() * 255).transpose(
1, 2, 0).astype(np.uint8)
cv2.imwrite("output/images/{}_{}_sr.jpg".format(sum_images,
i), fm_sr)
cv2.imwrite("output/images/{}_{}_lr.jpg".format(sum_images,
i), fm_lr)
cv2.imwrite("output/images/{}_{}_sr.jpg".format(
sum_images, i), fm_sr)
cv2.imwrite("output/images/{}_{}_lr.jpg".format(
sum_images, i), fm_lr)
else:
preds = model(images)
else:
@@ -529,10 +529,10 @@ def eval(model,
1, 2, 0).astype(np.uint8)
fm_lr = (lr_img[i].numpy() * 255).transpose(
1, 2, 0).astype(np.uint8)
cv2.imwrite("output/images/{}_{}_sr.jpg".format(sum_images,
i), fm_sr)
cv2.imwrite("output/images/{}_{}_lr.jpg".format(sum_images,
i), fm_lr)
cv2.imwrite("output/images/{}_{}_sr.jpg".format(
sum_images, i), fm_sr)
cv2.imwrite("output/images/{}_{}_lr.jpg".format(
sum_images, i), fm_lr)
else:
preds = model(images)
@@ -652,7 +652,7 @@ def preprocess(is_train=False):
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
'Gestalt'
]