docs: update blog

Author: SWHL

docs/blog/posts/support_pp_doc_layout.md

@@ -25,8 +25,11 @@ PP-DocLayout系列模型在版面分析方面效果很好，目前已经作为Pa
 - Python： 3.10.14
 - 其他依赖环境：
 
-    ```text
-
+    ```text linenums="1"
+    paddle2onnx==2.1.0
+    paddlepaddle==3.3.0
+    onnx==1.17.0
+    onnxruntime==1.23.2
     ```
 
 ### 转换命令
@@ -37,27 +40,61 @@ paddle2onnx  --model_dir=models/PP-DocLayoutV2  --model_filename inference.json
 
 #### 比较结果
 
-按照上面直接转换后，在相同输入下，ONNX模型和Paddle模型推理误差较大。
+按照上面直接转换后，在相同输入下，ONNX模型和Paddle模型推理结果误差为14.8%。在我看来，这个误差其实挺大的。
 
-```bash
->> np.testing.assert_allclose(batch_preds[0], ort_outputs[0], atol=1e-3, rtol=0)
+但是从可视化示例图结果来看，两者并无明显区别。可能在某些图上会有较大区别。
+
+我在`/xxxx/miniforge3/envs/wjh_debug/lib/python3.10/site-packages/paddlex/inference/models/layout_analysis/predictor.py`中插入以下代码（在L103行左右），来保证输入相同，比较输出：
+
+```python linenums="1"
+import onnxruntime
+import numpy as np
+
+model_path = "models/PP-DocLayoutV2/inference_v5_op15_pd_cpu_fixed.onnx"
+ort_session = onnxruntime.InferenceSession(model_path)
+ort_inputs = {
+    "im_shape": batch_inputs[0],
+    "image": batch_inputs[1],
+    "scale_factor": batch_inputs[2],
+}
+ort_outputs = ort_session.run(None, ort_inputs)
+
+# do infer
+batch_preds = self.infer(batch_inputs)
+
+# 千分位是否相同
+np.testing.assert_allclose(batch_preds[0], ort_outputs[0], atol=1e-3, rtol=0)
+```
+
+输出结果如下：
+
+```bash linenums="1"
 Traceback (most recent call last):
-  File "<string>", line 1, in <module>
-  File "/xxx/miniforge3/envs/wjh_debug/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 1504, in assert_allclose
+  File "/xxxx/paddleocr/test_pp_doc_layoutv2.py", line 4, in <module>
+    output = model.predict(
+  File "/xxxx/lib/python3.10/site-packages/paddleocr/_models/base.py", line 57, in predict
+    result = list(self.predict_iter(*args, **kwargs))
+  File "/xxxx/lib/python3.10/site-packages/paddlex/inference/models/base/predictor/base_predictor.py", line 281, in __call__
+    yield from self.apply(input, **kwargs)
+  File "/xxxx/lib/python3.10/site-packages/paddlex/inference/models/base/predictor/base_predictor.py", line 338, in apply
+    prediction = self.process(batch_data, **kwargs)
+  File "/xxxx/lib/python3.10/site-packages/paddlex/inference/models/layout_analysis/predictor.py", line 119, in process
+    np.testing.assert_allclose(batch_preds[0], ort_outputs[0], atol=1e-3, rtol=0)
+  File "/xxxx/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 1504, in assert_allclose
     assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
-  File "/xxx/miniforge3/envs/wjh_debug/lib/python3.10/contextlib.py", line 79, in inner
+  File "/xxxx/lib/python3.10/contextlib.py", line 79, in inner
     return func(*args, **kwds)
-  File "/xxx/miniforge3/envs/wjh_debug/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 797, in assert_array_compare
+  File "/xxxx/lib/python3.10/site-packages/numpy/testing/_private/utils.py", line 797, in assert_array_compare
     raise AssertionError(msg)
 AssertionError:
 Not equal to tolerance rtol=0, atol=0.001
 
-Mismatched elements: 1321 / 2400 (55%)
-Max absolute difference: 819.32886
-Max relative difference: 294.
- x: array([[2.200000e+01, 9.889959e-01, 3.354025e+01, ..., 6.150441e+02,
+Mismatched elements: 354 / 2400 (14.8%)
+Max absolute difference: 196.
+Max relative difference: 194.
+ x: array([[2.200000e+01, 9.889924e-01, 3.354079e+01, ..., 6.150450e+02,
         2.900000e+02, 2.900000e+02],
-       [2.200000e+01, 9.888721e-01, 3.372420e+01, ..., 8.526017e+02,...
+       [2.200000e+01, 9.888635e-01, 3.372379e+01, ..., 8.526023e+02,...
  y: array([[2.200000e+01, 9.889925e-01, 3.354081e+01, ..., 6.150450e+02,
         2.900000e+02, 2.900000e+02],
        [2.200000e+01, 9.888635e-01, 3.372382e+01, ..., 8.526024e+02,...