[Refine&Doc] Fix ijcai url and refien eval code (#970)

* update ijcai2024 url

* support custom collate_fn via setting 'collate_fn' in dataloader_cfg

* update batch_size computation in eval

* update code

* fix for CI

* add content in recent updating

* add spinn aistudio url

Author: HydrogenSulfate

README.md

@@ -15,7 +15,7 @@
 
 🔥 [IJCAI 2024: 任意三维几何外形车辆的风阻快速预测竞赛](https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5)，track A, B, C 代码：
 
-- [paddle实现](../jointContribution/IJCAI_2024/README.md)
+- [paddle实现](./jointContribution/IJCAI_2024/README.md)
 - [pytorch实现](https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5)(点击**排行榜**可查看各个赛道前10名的代码)
 
 <!-- --8<-- [start:description] -->
@@ -108,6 +108,8 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
 <!-- --8<-- [start:update] -->
 ## 🕘最近更新
 
+- 添加 [IJCAI 2024: 任意三维几何外形车辆的风阻快速预测竞赛](https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5)，track A, B, C 的 paddle/pytorch 代码链接。
+- 添加 SPINN(基于 Helmholtz3D 方程求解) [helmholtz3d](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/spinn/)。
 - 添加 CVit(基于 Advection 方程和 N-S 方程求解) [CVit(Navier-Stokes)](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/ns_cvit/)、[CVit(Advection)](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/adv_cvit/)。
 - 添加 PirateNet(基于 Allen-cahn 方程和 N-S 方程求解) [Allen-Cahn](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/allen_cahn/)、[LDC2D(Re3200)](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/ldc2d_steady/)。
 - 基于 PaddleScience 的快速热仿真方法 [A fast general thermal simulation model based on MultiBranch Physics-Informed deep operator neural network](https://pubs.aip.org/aip/pof/article-abstract/36/3/037142/3277890/A-fast-general-thermal-simulation-model-based-on?redirectedFrom=fulltext) 被 Physics of Fluids 2024 接受。

docs/index.md

@@ -6,7 +6,7 @@
 
 🔥 [IJCAI 2024: 任意三维几何外形车辆的风阻快速预测竞赛](https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5)，track A, B, C 代码：
 
-- [paddle实现](../jointContribution/IJCAI_2024/README.md)
+- [paddle实现](https://github.com/PaddlePaddle/PaddleScience/tree/develop/jointContribution/IJCAI_2024)
 - [pytorch实现](https://competition.atomgit.com/competitionInfo?id=7f3f276465e9e845fd3a811d2d6925b5)(点击**排行榜**可查看各个赛道前10名的代码)
 
 <style>

docs/zh/examples/spinn.md

@@ -1,6 +1,6 @@
 # SPINN(helmholtz3d)
 
-<!-- <a href="https://aistudio.baidu.com/projectdetail/8219967" class="md-button md-button--primary" style>AI Studio快速体验</a> -->
+<a href="https://aistudio.baidu.com/projectdetail/8219967" class="md-button md-button--primary" style>AI Studio快速体验</a>
 
 === "模型训练命令"
 

ppsci/data/__init__.py

@@ -15,6 +15,8 @@
 import copy
 import random
 from functools import partial
+from typing import Callable
+from typing import Optional
 
 import numpy as np
 import paddle.distributed as dist
@@ -101,9 +103,11 @@ def build_dataloader(_dataset, cfg):
 
     # build collate_fn if specified
     batch_transforms_cfg = cfg.pop("batch_transforms", None)
-    collate_fn = None
+    collate_fn: Optional[Callable] = cfg.pop("collate_fn", None)
     if isinstance(batch_transforms_cfg, (list, tuple)):
-        collate_fn = batch_transform.build_batch_transforms(batch_transforms_cfg)
+        collate_fn = batch_transform.build_batch_transforms(
+            batch_transforms_cfg, collate_fn
+        )
 
     # build init function
     _DEFAULT_NUM_WORKERS = 1

ppsci/data/process/batch_transform/__init__.py

@@ -19,6 +19,7 @@
 from typing import Any
 from typing import Callable
 from typing import List
+from typing import Optional
 
 import numpy as np
 import paddle
@@ -118,15 +119,17 @@ def build_transforms(cfg):
     return transform.Compose(transform_list)
 
 
-def build_batch_transforms(cfg):
+def build_batch_transforms(cfg, collate_fn: Optional[Callable]):
     cfg = copy.deepcopy(cfg)
     batch_transforms: Callable[[List[Any]], List[Any]] = build_transforms(cfg)
+    if collate_fn is None:
+        collate_fn = default_collate_fn
 
     def collate_fn_batch_transforms(batch: List[Any]):
         # apply batch transform on separate samples
         batch = batch_transforms(batch)
 
         # then collate separate samples into batched data
-        return default_collate_fn(batch)
+        return collate_fn(batch)
 
     return collate_fn_batch_transforms

ppsci/solver/eval.py

@@ -25,6 +25,7 @@
 from paddle import io
 
 from ppsci.solver import printer
+from ppsci.solver.train import _compute_batch_size
 from ppsci.utils import misc
 
 if TYPE_CHECKING:
@@ -128,7 +129,7 @@ def _eval_by_dataset(
             batch_cost = time.perf_counter() - batch_tic
             solver.eval_time_info["reader_cost"].update(reader_cost)
             solver.eval_time_info["batch_cost"].update(batch_cost)
-            batch_size = next(iter(input_dict.values())).shape[0]
+            batch_size = _compute_batch_size(input_dict)
             printer.update_eval_loss(solver, loss_dict, batch_size)
             if (
                 iter_id == 1
@@ -216,7 +217,7 @@ def _eval_by_batch(
             input_dict, label_dict, weight_dict = batch
             reader_cost = time.perf_counter() - reader_tic
 
-            batch_size = next(iter(input_dict.values())).shape[0]
+            batch_size = _compute_batch_size(input_dict)
             for v in input_dict.values():
                 if hasattr(v, "stop_gradient"):
                     v.stop_gradient = False