Merge branch 'PaddlePaddle:develop' into try_model

Author: smallpoxscattered

docs/zh/cooperation.md

@@ -1,3 +1,17 @@
 # 共创计划
 
-PaddleScience 作为一个开源项目，欢迎来各行各业的伙伴携手共建基于飞桨的 AI for Science 领域顶尖开源项目, 打造活跃的前瞻性的 AI for Science 开源社区，建立产学研闭环，推动科研创新与产业赋能。点击了解 [飞桨AI for Science共创计划](https://www.paddlepaddle.org.cn/science)
+PaddleScience 作为一个开源项目，欢迎来各行各业的伙伴携手共建基于飞桨的 AI for Science 领域顶尖开源项目，打造活跃的前瞻性的 AI for Science 开源社区，建立产学研闭环，推动科研创新与产业赋能。点击了解 [飞桨 AI for Science 共创计划](https://www.paddlepaddle.org.cn/science)
+
+## 项目精选
+
+- 使用嵌套傅立叶神经算子进行实时高分辨二氧化碳地质封存预测: <https://aistudio.baidu.com/projectdetail/7390303>
+- 多源异构数据与机理融合的极端天气预报算法研究: <https://aistudio.baidu.com/projectdetail/7586532>
+- 基于强化学习的复杂系统控制 —— 以疾病传播: <https://aistudio.baidu.com/projectdetail/7520457>
+- 基于 Transformer 架构的流体流动降阶模拟: <https://aistudio.baidu.com/projectdetail/7509905>
+- 基于 Transformer 的神经算子预测模型: <https://aistudio.baidu.com/projectdetail/7309026>
+- 基于 PINN 方法求解可压缩流体欧拉方程组的正问题: <https://aistudio.baidu.com/projectdetail/7502148>
+- 基于连续演化数据预测双曲方程简断解: <https://aistudio.baidu.com/projectdetail/7620492>
+- 拉格朗日粒子流体 Benchmark 开源数据集: <https://aistudio.baidu.com/projectdetail/7507477>
+- 基于 PINN 方法求解可压缩流体欧拉方程组的正问题: <https://aistudio.baidu.com/projectdetail/7593837>
+- 数据驱动 AI 模型的 PDE 方程可解释性评估: <https://aistudio.baidu.com/projectdetail/7463477>
+- 数据驱动 AI 模型的 PDE 方程可解释性评估: <https://aistudio.baidu.com/projectdetail/7512749>

mkdocs.yml

@@ -86,7 +86,7 @@ nav:
         - NowcastNet: zh/examples/nowcastnet.md
         - DGMR: zh/examples/dgmr.md
         - EarthFormer: zh/examples/earthformer.md
-  - API文档:
+  - API 文档:
     - ppsci:
       - ppsci.arch: zh/api/arch.md
       - ppsci.autodiff: zh/api/autodiff.md

ppsci/data/dataset/dgmr_dataset.py

@@ -14,19 +14,15 @@
 
 from __future__ import annotations
 
+import importlib
 from typing import Tuple
 
 import numpy as np
-import paddle
 from numpy.random import default_rng
+from paddle import io
 
-try:
-    import datasets
-except ModuleNotFoundError:
-    pass
 
-
-class DGMRDataset(paddle.io.Dataset):
+class DGMRDataset(io.Dataset):
     """
     Dataset class for DGMR (Deep Generative Model for Radar) model.
     This open-sourced UK dataset has been mirrored to HuggingFace Datasets https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km.
@@ -59,6 +55,13 @@ def __init__(
         self.label_keys = label_keys
         self.num_input_frames = num_input_frames
         self.num_target_frames = num_target_frames
+        if not importlib.util.find_spec("datasets"):
+            raise ModuleNotFoundError(
+                "Please install datasets with `pip install datasets`"
+                " before exporting onnx model."
+            )
+        import datasets
+
         self.reader = datasets.load_dataset(
             dataset_path, "sample", split=split, streaming=True, trust_remote_code=True
         )

ppsci/data/dataset/enso_dataset.py

@@ -15,20 +15,14 @@
 from __future__ import annotations
 
 import importlib
+from pathlib import Path
 from typing import Dict
 from typing import Optional
 from typing import Tuple
 
 import numpy as np
 from paddle import io
 
-try:
-    from pathlib import Path
-
-    import xarray as xr
-except ModuleNotFoundError:
-    pass
-
 NINO_WINDOW_T = 3  # Nino index is the sliding average over sst, window size is 3
 CMIP6_SST_MAX = 10.198975563049316
 CMIP6_SST_MIN = -16.549121856689453
@@ -146,6 +140,7 @@ def read_raw_data(ds_dir, out_dir=None):
         out_dir (str): the path of output. Defaults to None.
 
     """
+    import xarray as xr
 
     train_cmip = xr.open_dataset(Path(ds_dir) / "CMIP_train.nc").transpose(
         "year", "month", "lat", "lon"
@@ -275,11 +270,6 @@ def __init__(
                 "To use RadarDataset, please install 'xarray' via: `pip install "
                 "xarray` first."
             )
-        if importlib.util.find_spec("pathlib") is None:
-            raise ModuleNotFoundError(
-                "To use RadarDataset, please install 'pathlib' via: `pip install "
-                "pathlib` first."
-            )
         self.input_keys = input_keys
         self.label_keys = label_keys
         self.data_dir = data_dir