refactor & standardize ppmat and fix last errors for diffnmr & infgcn

Install.md

@@ -90,7 +90,7 @@ Generate crystal structures with four atoms using a pretrained MatterGen model:
 python structure_generation/sample.py \
     --model_name='mattergen_mp20' \
     --weights_name='latest.pdparams' \
-    --save_path='result_mattergen_mp20/' \
+    --output_dir='result_mattergen_mp20/' \
     --mode='by_num_atoms' \
     --num_atoms=4
 ```
@@ -113,11 +113,12 @@ Predict electron density using a trained InfGCN checkpoint:
 
 ```bash
 python electronic_structure/predict.py \
-    --config='electronic_structure/configs/infgcn/infgcn_qm9.yaml' \
-    --checkpoint='path/to/infgcn_qm9.pdparams' \
-    --split='validation' \
-    --index=0 \
-    --output_dir='output/infgcn_qm9/validation_0' \
+    --model_name='infgcn_qm9' \
+    --weights_name='best.pdparams' \
+    --mol_input='electronic_structure/configs/infgcn/example/methane.mol' \
+    --atom_file='electronic_structure/configs/qm9.json' \
+    --mol_grid_shape=8 \
+    --output_dir='output/infgcn_qm9/methane' \
     --save_pred_cube
 ```
 
@@ -130,9 +131,9 @@ Run NMR spectrum elucidation using a trained DiffNMR checkpoint:
 
 ```bash
 python spectrum_elucidation/sample.py \
-    --config_path='spectrum_elucidation/configs/diffnmr/DiffNMR.yaml' \
-    --checkpoint_path='path/to/DiffNMR_nless15_best.pdparams' \
-    --save_path='result_diffnmr_nless15/'
+    --model_name='diffnmr_msdnmr_nless15' \
+    --weights_name='best.pdparams' \
+    --output_dir='result_diffnmr_nless15/'
 ```
 
 ### 2.6 Spectrum Enhancement
@@ -142,7 +143,9 @@ Enhance STEM images using a pretrained SFIN model:
 ```bash
 python spectrum_enhancement/predict.py \
     --model_name='sfin_haadf_enhance' \
-    --split='val'
+    --weights_name='best.pdparams' \
+    --input_path='path/to/noisy_image.png' \
+    --output_dir='result_sfin/'
 ```
 
 For more usage instructions, refer to the task-specific README files or the

Install_cn.md

@@ -89,7 +89,7 @@ python property_prediction/predict.py \
 python structure_generation/sample.py \
     --model_name='mattergen_mp20' \
     --weights_name='latest.pdparams' \
-    --save_path='result_mattergen_mp20/' \
+    --output_dir='result_mattergen_mp20/' \
     --mode='by_num_atoms' \
     --num_atoms=4
 ```
@@ -112,11 +112,12 @@ python interatomic_potentials/predict.py \
 
 ```bash
 python electronic_structure/predict.py \
-    --config='electronic_structure/configs/infgcn/infgcn_qm9.yaml' \
-    --checkpoint='path/to/infgcn_qm9.pdparams' \
-    --split='validation' \
-    --index=0 \
-    --output_dir='output/infgcn_qm9/validation_0' \
+    --model_name='infgcn_qm9' \
+    --weights_name='best.pdparams' \
+    --mol_input='electronic_structure/configs/infgcn/example/methane.mol' \
+    --atom_file='electronic_structure/configs/qm9.json' \
+    --mol_grid_shape=8 \
+    --output_dir='output/infgcn_qm9/methane' \
     --save_pred_cube
 ```
 
@@ -129,9 +130,9 @@ python electronic_structure/predict.py \
 
 ```bash
 python spectrum_elucidation/sample.py \
-    --config_path='spectrum_elucidation/configs/diffnmr/DiffNMR.yaml' \
-    --checkpoint_path='path/to/DiffNMR_nless15_best.pdparams' \
-    --save_path='result_diffnmr_nless15/'
+    --model_name='diffnmr_msdnmr_nless15' \
+    --weights_name='best.pdparams' \
+    --output_dir='result_diffnmr_nless15/'
 ```
 
 ### 2.6 谱图增强
@@ -141,7 +142,9 @@ python spectrum_elucidation/sample.py \
 ```bash
 python spectrum_enhancement/predict.py \
     --model_name='sfin_haadf_enhance' \
-    --split='val'
+    --weights_name='best.pdparams' \
+    --input_path='path/to/noisy_image.png' \
+    --output_dir='result_sfin/'
 ```
 
 更多使用说明请参考各任务 README 或 [Get Started](./get_started.md)。

README.md

@@ -19,6 +19,12 @@
   </a>
 </p>
 
+<p align="center">
+  <a href="README.md"><img src="https://img.shields.io/badge/English-blue" alt="English"></a>
+  <a href="README_zh.md"><img src="https://img.shields.io/badge/简体中文-lightgrey" alt="简体中文"></a>
+  <a href="README_ja.md"><img src="https://img.shields.io/badge/日本語-lightgrey" alt="日本語"></a>
+</p>
+
 ## 🚀 Introduction
 
 **PaddleMaterials** is an end-to-end AI4Materials toolkit built on the **PaddlePaddle** deep learning framework. Designed as a data-mechanism dual-driven platform for developing and deploying foundation models in materials science, **PPMat** enables researchers to efficiently build AI models and accelerate material discovery using pretrained models.
@@ -106,7 +112,7 @@ Generate novel crystal structures using a pretrained MatterGen model:
 python structure_generation/sample.py \
     --model_name='mattergen_mp20' \
     --weights_name='latest.pdparams' \
-    --save_path='result_mattergen_mp20/' \
+    --output_dir='result_mattergen_mp20/' \
     --mode='by_num_atoms' \
     --num_atoms=4
 ```
@@ -125,29 +131,33 @@ python interatomic_potentials/predict.py \
 
 #### Electronic Structure
 
-Predict electron density using a pretrained InfGCN checkpoint:
+Predict electron density from the bundled methane example using a pretrained InfGCN
+model:
 
 ```bash
 python electronic_structure/predict.py \
-    --config='electronic_structure/configs/infgcn/infgcn_qm9.yaml' \
-    --checkpoint='path/to/infgcn_qm9.pdparams' \
-    --split='validation' \
-    --index=0 \
-    --output_dir='output/infgcn_qm9/validation_0' \
+    --model_name='infgcn_qm9' \
+    --weights_name='best.pdparams' \
+    --mol_input='electronic_structure/configs/infgcn/example/methane.mol' \
+    --atom_file='electronic_structure/configs/qm9.json' \
+    --mol_grid_shape=8 \
+    --output_dir='output/infgcn_qm9/methane' \
     --save_pred_cube
 ```
 
-See the [InfGCN prediction guide](electronic_structure/configs/infgcn/README.md#prediction) for dataset and checkpoint preparation.
+See the [InfGCN prediction guide](electronic_structure/configs/infgcn/README.md#prediction)
+for dataset-based and local-checkpoint inference.
 
 #### Spectrum Elucidation
 
-Run NMR spectrum elucidation using a pretrained DiffNMR checkpoint:
+Run NMR spectrum elucidation using the bundled example and a pretrained DiffNMR
+model:
 
 ```bash
 python spectrum_elucidation/sample.py \
-    --config_path='spectrum_elucidation/configs/diffnmr/DiffNMR.yaml' \
-    --checkpoint_path='path/to/DiffNMR_nless15_best.pdparams' \
-    --save_path='result_diffnmr_nless15/'
+    --model_name='diffnmr_msdnmr_nless15' \
+    --weights_name='best.pdparams' \
+    --output_dir='result_diffnmr_nless15/'
 ```
 
 #### Spectrum Enhancement
@@ -157,7 +167,9 @@ Enhance STEM images using a pretrained SFIN model:
 ```bash
 python spectrum_enhancement/predict.py \
     --model_name='sfin_haadf_enhance' \
-    --split='val'
+    --weights_name='best.pdparams' \
+    --input_path='path/to/noisy_image.png' \
+    --output_dir='result_sfin/'
 ```
 
 ---

README_PYPI.md

@@ -108,7 +108,7 @@ Generate novel crystal structures using a pretrained MatterGen model:
 python structure_generation/sample.py \
     --model_name='mattergen_mp20' \
     --weights_name='latest.pdparams' \
-    --save_path='result_mattergen_mp20/' \
+    --output_dir='result_mattergen_mp20/' \
     --mode='by_num_atoms' \
     --num_atoms=4
 ```
@@ -127,29 +127,33 @@ python interatomic_potentials/predict.py \
 
 #### Electronic Structure
 
-Predict electron density using a pretrained InfGCN checkpoint:
+Predict electron density from the bundled methane example using a pretrained InfGCN
+model:
 
 ```bash
 python electronic_structure/predict.py \
-    --config='electronic_structure/configs/infgcn/infgcn_qm9.yaml' \
-    --checkpoint='path/to/infgcn_qm9.pdparams' \
-    --split='validation' \
-    --index=0 \
-    --output_dir='output/infgcn_qm9/validation_0' \
+    --model_name='infgcn_qm9' \
+    --weights_name='best.pdparams' \
+    --mol_input='electronic_structure/configs/infgcn/example/methane.mol' \
+    --atom_file='electronic_structure/configs/qm9.json' \
+    --mol_grid_shape=8 \
+    --output_dir='output/infgcn_qm9/methane' \
     --save_pred_cube
 ```
 
-See the [InfGCN prediction guide](https://github.com/PaddlePaddle/PaddleMaterials/blob/develop/electronic_structure/configs/infgcn/README.md#prediction) for dataset and checkpoint preparation.
+See the [InfGCN prediction guide](https://github.com/PaddlePaddle/PaddleMaterials/blob/develop/electronic_structure/configs/infgcn/README.md#prediction)
+for dataset-based and local-checkpoint inference.
 
 #### Spectrum Elucidation
 
-Run NMR spectrum elucidation using a pretrained DiffNMR checkpoint:
+Run NMR spectrum elucidation using the bundled example and a pretrained DiffNMR
+model:
 
 ```bash
 python spectrum_elucidation/sample.py \
-    --config_path='spectrum_elucidation/configs/diffnmr/DiffNMR.yaml' \
-    --checkpoint_path='path/to/DiffNMR_nless15_best.pdparams' \
-    --save_path='result_diffnmr_nless15/'
+    --model_name='diffnmr_msdnmr_nless15' \
+    --weights_name='best.pdparams' \
+    --output_dir='result_diffnmr_nless15/'
 ```
 
 #### Spectrum Enhancement
@@ -159,7 +163,9 @@ Enhance STEM images using a pretrained SFIN model:
 ```bash
 python spectrum_enhancement/predict.py \
     --model_name='sfin_haadf_enhance' \
-    --split='val'
+    --weights_name='best.pdparams' \
+    --input_path='path/to/noisy_image.png' \
+    --output_dir='result_sfin/'
 ```
 
 ---