[Doc] Fix document and formulations for cvit

README.md

@@ -46,8 +46,8 @@ PaddleScience 是一个基于深度学习框架 PaddlePaddle 开发的科学计
 
 | 问题类型 | 案例名称 | 优化算法 | 模型类型 | 训练方式 | 数据集 | 参考资料 |
 |-----|---------|-----|---------|----|---------|---------|
-| 一维线性对流问题 | [1D 线性对流](https://paddlescience-docs.readthedocs.io/zh/examples/adv_cvit.md) | 数据驱动 | ViT | 监督学习 | [Data](https://github.com/Zhengyu-Huang/Operator-Learning/tree/main/data) | [Paper](https://arxiv.org/abs/2405.13998) |
-| 非定常不可压流体 | [2D 方腔浮力驱动流](https://paddlescience-docs.readthedocs.io/zh/examples/ns_cvit.md) | 数据驱动 | ViT | 监督学习 | [Data](https://huggingface.co/datasets/pdearena/NavierStokes-2D) | [Paper](https://arxiv.org/abs/2405.13998) |
+| 一维线性对流问题 | [1D 线性对流](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/adv_cvit/) | 数据驱动 | ViT | 监督学习 | [Data](https://github.com/Zhengyu-Huang/Operator-Learning/tree/main/data) | [Paper](https://arxiv.org/abs/2405.13998) |
+| 非定常不可压流体 | [2D 方腔浮力驱动流](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/ns_cvit/) | 数据驱动 | ViT | 监督学习 | [Data](https://huggingface.co/datasets/pdearena/NavierStokes-2D) | [Paper](https://arxiv.org/abs/2405.13998) |
 | 定常不可压流体 | [Re3200 2D 定常方腔流](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/ldc2d_steady) | 机理驱动 | MLP | 无监督学习 | - |  |
 | 定常不可压流体 | [2D 达西流](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/darcy2d) | 机理驱动 | MLP | 无监督学习 | - |   |
 | 定常不可压流体 | [2D 管道流](https://paddlescience-docs.readthedocs.io/zh/latest/zh/examples/labelfree_DNN_surrogate) | 机理驱动 | MLP | 无监督学习 | - | [Paper](https://arxiv.org/abs/1906.02382) |

deploy/python_infer/pinn_predictor.py

@@ -159,7 +159,7 @@ def predict(
                 ed = min(num_samples, batch_id * batch_size)
                 batch_input_dict = {key: input_dict[key][st:ed] for key in input_dict}
             else:
-                batch_input_dict = {key: input_dict[key] for key in input_dict}
+                batch_input_dict = {**input_dict}
 
             # send batch input data to input handle(s)
             if self.engine != "onnx":

docs/zh/api/data/process/batch_transform.md

@@ -1,9 +1,10 @@
 # Data.batch_transform(批预处理) 模块
 
-::: ppsci.data.process.transform
+::: ppsci.data.process.batch_transform
     handler: python
     options:
       members:
         - build_batch_transforms
+        - FunctionalBatchTransform
         - default_collate_fn
       show_root_heading: true

docs/zh/development.md

@@ -926,7 +926,7 @@ PaddleScience 是一个开源的代码库，由多人共同参与开发，因此
 PaddleScience 使用了包括 [isort](https://github.com/PyCQA/isort#installing-isort)、[black](https://github.com/psf/black) 等自动化代码检查、格式化插件，
 让 commit 的代码遵循 python [PEP8](https://pep8.org/) 代码风格规范。
 
-因此在 commit 您的代码之前，请务必先执行以下命令安装 `pre-commit`，否则提交的 PR 会被 code-style 检测到代码未格式化而无法合入。
+因此在 commit 您的代码之前，请务必先在 `PaddleScience/` 目录下执行以下命令安装 `pre-commit`，否则提交的 PR 会被 code-style 检测到代码未格式化而无法合入。
 
 ``` sh
 pip install pre-commit

docs/zh/examples/adv_cvit.md

@@ -49,17 +49,22 @@ CVit 作为一种算子学习模型，以输入函数 $u$、函数 $s$ 的查询
 本问题求解如下方程：
 
 Formulation The 1D advection equation in $\Omega=[0,1)$ is
+
 $$
 \begin{aligned}
 & \frac{\partial u}{\partial t}+c \frac{\partial u}{\partial x}=0 \quad x \in \Omega, \\
 & u(0)=u_0
 \end{aligned}
 $$
+
 where $c=1$ is the constant advection speed, and periodic boundary conditions are imposed. We are interested in the map from the initial $u_0$ to solution $u(\cdot, T)$ at $T=0.5$. The initial condition $u_0$ is assumed to be
+
 $$
 u_0=-1+2 \mathbb{1}\left\{\tilde{u_0} \geq 0\right\}
 $$
+
 where $\widetilde{u_0}$ a centered Gaussian
+
 $$
 \widetilde{u_0} \sim \mathbb{N}(0, \mathrm{C}) \quad \text { and } \quad \mathrm{C}=\left(-\Delta+\tau^2\right)^{-d} \text {; }
 $$

docs/zh/examples/ns_cvit.md

@@ -54,6 +54,7 @@ CVit 作为一种算子学习模型，以输入函数 $u$、函数 $s$ 的查询
 本问题基于固定方腔的不可压 buoyancy-driven flow 即方腔内的浮力驱动流动问题，求解如下方程：
 
 Formulation We consider the vorticity-stream $(\omega-\psi)$ formulation of the incompressible Navier-Stokes equations on a two-dimensional periodic domain, $D=D_u=D_v=[0,2 \pi]^2$ :
+
 $$
 \begin{aligned}
 & \frac{\partial \omega}{\partial t}+(v \cdot \nabla) \omega-v \Delta \omega=f^{\prime} \\

ppsci/solver/solver.py

@@ -710,7 +710,7 @@ def predict(
         self,
         input_dict: Dict[str, Union[np.ndarray, paddle.Tensor]],
         expr_dict: Optional[Dict[str, Callable]] = None,
-        batch_size: int = 64,
+        batch_size: Optional[int] = 64,
         no_grad: bool = True,
         return_numpy: bool = False,
     ) -> Dict[str, Union[paddle.Tensor, np.ndarray]]:
@@ -720,7 +720,9 @@ def predict(
             input_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Input data in dict.
             expr_dict (Optional[Dict[str, Callable]]): Expression dict, which guide to
                 compute equation variable with callable function. Defaults to None.
-            batch_size (int, optional): Predicting by batch size. Defaults to 64.
+            batch_size (Optional[int]): Predicting by batch size. If None, data in
+                `input_dict` will be used directly for inference without any batch slicing.
+                Defaults to 64.
             no_grad (bool): Whether set stop_gradient=True for entire prediction, mainly
                 for memory-efficiency. Defaults to True.
             return_numpy (bool): Whether convert result from Tensor to numpy ndarray.
@@ -773,26 +775,32 @@ def predict(
             if self.world_size > 1
             else input_dict
         )
-        local_batch_num = (local_num_samples_pad + (batch_size - 1)) // batch_size
+        local_batch_num = (
+            (local_num_samples_pad + (batch_size - 1)) // batch_size
+            if batch_size is not None
+            else 1
+        )
 
         pred_dict = misc.Prettydefaultdict(list)
         with self.no_grad_context_manager(no_grad), self.no_sync_context_manager(
             self.world_size > 1, self.model
         ):
             for batch_id in range(local_batch_num):
-                batch_input_dict = {}
-                st = batch_id * batch_size
-                ed = min(local_num_samples_pad, (batch_id + 1) * batch_size)
-
                 # prepare batch input dict
-                for key in local_input_dict:
-                    if not paddle.is_tensor(local_input_dict[key]):
-                        batch_input_dict[key] = paddle.to_tensor(
-                            local_input_dict[key][st:ed], paddle.get_default_dtype()
-                        )
-                    else:
-                        batch_input_dict[key] = local_input_dict[key][st:ed]
-                    batch_input_dict[key].stop_gradient = no_grad
+                batch_input_dict = {}
+                if batch_size is not None:
+                    st = batch_id * batch_size
+                    ed = min(local_num_samples_pad, (batch_id + 1) * batch_size)
+                    for key in local_input_dict:
+                        if not paddle.is_tensor(local_input_dict[key]):
+                            batch_input_dict[key] = paddle.to_tensor(
+                                local_input_dict[key][st:ed], paddle.get_default_dtype()
+                            )
+                        else:
+                            batch_input_dict[key] = local_input_dict[key][st:ed]
+                        batch_input_dict[key].stop_gradient = no_grad
+                else:
+                    batch_input_dict = {**local_input_dict}
 
                 # forward
                 with self.autocast_context_manager(self.use_amp, self.amp_level):

ppsci/utils/config.py

@@ -42,15 +42,15 @@ class EMAConfig(BaseModel):
         def decay_check(cls, v):
             if v <= 0 or v >= 1:
                 raise ValueError(
-                    f"'decay' should be in (0, 1) when is type of float, but got {v}"
+                    f"'ema.decay' should be in (0, 1) when is type of float, but got {v}"
                 )
             return v
 
         @field_validator("avg_freq")
         def avg_freq_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'avg_freq' should be a positive integer when is type of int, "
+                    "'ema.avg_freq' should be a positive integer when is type of int, "
                     f"but got {v}"
                 )
             return v
@@ -63,15 +63,17 @@ class SWAConfig(BaseModel):
         @field_validator("avg_range")
         def avg_range_check(cls, v, info: ValidationInfo):
             if isinstance(v, tuple) and v[0] > v[1]:
-                raise ValueError(f"'avg_range' should be a valid range, but got {v}.")
+                raise ValueError(
+                    f"'swa.avg_range' should be a valid range, but got {v}."
+                )
             if isinstance(v, tuple) and v[0] < 0:
                 raise ValueError(
-                    "The start epoch of 'avg_range' should be a non-negtive integer"
+                    "The start epoch of 'swa.avg_range' should be a non-negtive integer"
                     f" , but got {v[0]}."
                 )
             if isinstance(v, tuple) and v[1] > info.data["epochs"]:
                 raise ValueError(
-                    "The end epoch of 'avg_range' should not be lager than "
+                    "The end epoch of 'swa.avg_range' should not be lager than "
                     f"'epochs'({info.data['epochs']}), but got {v[1]}."
                 )
             return v
@@ -80,7 +82,7 @@ def avg_range_check(cls, v, info: ValidationInfo):
         def avg_freq_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'avg_freq' should be a positive integer when is type of int, "
+                    "'swa.avg_freq' should be a positive integer when is type of int, "
                     f"but got {v}"
                 )
             return v
@@ -107,7 +109,7 @@ class TrainConfig(BaseModel):
         def epochs_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'epochs' should be a positive integer when is type of int, "
+                    "'TRAIN.epochs' should be a positive integer when is type of int, "
                     f"but got {v}"
                 )
             return v
@@ -116,7 +118,7 @@ def epochs_check(cls, v):
         def iters_per_epoch_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'iters_per_epoch' should be a positive integer when is type of int"
+                    "'TRAIN.iters_per_epoch' should be a positive integer when is type of int"
                     f", but got {v}"
                 )
             return v
@@ -125,7 +127,7 @@ def iters_per_epoch_check(cls, v):
         def update_freq_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'update_freq' should be a positive integer when is type of int"
+                    "'TRAIN.update_freq' should be a positive integer when is type of int"
                     f", but got {v}"
                 )
             return v
@@ -134,7 +136,7 @@ def update_freq_check(cls, v):
         def save_freq_check(cls, v):
             if v < 0:
                 raise ValueError(
-                    "'save_freq' should be a non-negtive integer when is type of int"
+                    "'TRAIN.save_freq' should be a non-negtive integer when is type of int"
                     f", but got {v}"
                 )
             return v
@@ -144,8 +146,8 @@ def start_eval_epoch_check(cls, v, info: ValidationInfo):
             if info.data["eval_during_train"]:
                 if v <= 0:
                     raise ValueError(
-                        f"'start_eval_epoch' should be a positive integer when "
-                        f"'eval_during_train' is True, but got {v}"
+                        f"'TRAIN.start_eval_epoch' should be a positive integer when "
+                        f"'TRAIN.eval_during_train' is True, but got {v}"
                     )
             return v
 
@@ -154,8 +156,8 @@ def eval_freq_check(cls, v, info: ValidationInfo):
             if info.data["eval_during_train"]:
                 if v <= 0:
                     raise ValueError(
-                        f"'eval_freq' should be a positive integer when "
-                        f"'eval_during_train' is True, but got {v}"
+                        f"'TRAIN.eval_freq' should be a positive integer when "
+                        f"'TRAIN.eval_during_train' is True, but got {v}"
                     )
             return v
 
@@ -176,6 +178,15 @@ class EvalConfig(BaseModel):
         pretrained_model_path: Optional[str] = None
         eval_with_no_grad: bool = False
         compute_metric_by_batch: bool = False
+        batch_size: Optional[int] = 256
+
+        @field_validator("batch_size")
+        def batch_size_check(cls, v):
+            if isinstance(v, int) and v <= 0:
+                raise ValueError(
+                    f"'EVAL.batch_size' should be greater than 0 or None, but got {v}"
+                )
+            return v
 
     class InferConfig(BaseModel):
         """
@@ -203,12 +214,12 @@ class InferConfig(BaseModel):
         def engine_check(cls, v, info: ValidationInfo):
             if v == "tensorrt" and info.data["device"] != "gpu":
                 raise ValueError(
-                    "'device' should be 'gpu' when 'engine' is 'tensorrt', "
+                    "'INFER.device' should be 'gpu' when 'INFER.engine' is 'tensorrt', "
                     f"but got '{info.data['device']}'"
                 )
             if v == "mkldnn" and info.data["device"] != "cpu":
                 raise ValueError(
-                    "'device' should be 'cpu' when 'engine' is 'mkldnn', "
+                    "'INFER.device' should be 'cpu' when 'INFER.engine' is 'mkldnn', "
                     f"but got '{info.data['device']}'"
                 )
 
@@ -218,46 +229,50 @@ def engine_check(cls, v, info: ValidationInfo):
         def min_subgraph_size_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'min_subgraph_size' should be greater than 0, " f"but got {v}"
+                    "'INFER.min_subgraph_size' should be greater than 0, "
+                    f"but got {v}"
                 )
             return v
 
         @field_validator("gpu_mem")
         def gpu_mem_check(cls, v):
             if v <= 0:
-                raise ValueError("'gpu_mem' should be greater than 0, " f"but got {v}")
+                raise ValueError(
+                    "'INFER.gpu_mem' should be greater than 0, " f"but got {v}"
+                )
             return v
 
         @field_validator("gpu_id")
         def gpu_id_check(cls, v):
             if v < 0:
                 raise ValueError(
-                    "'gpu_id' should be greater than or equal to 0, " f"but got {v}"
+                    "'INFER.gpu_id' should be greater than or equal to 0, "
+                    f"but got {v}"
                 )
             return v
 
         @field_validator("max_batch_size")
         def max_batch_size_check(cls, v):
             if v <= 0:
                 raise ValueError(
-                    "'max_batch_size' should be greater than 0, " f"but got {v}"
+                    "'INFER.max_batch_size' should be greater than 0, " f"but got {v}"
                 )
             return v
 
         @field_validator("num_cpu_threads")
         def num_cpu_threads_check(cls, v):
             if v < 0:
                 raise ValueError(
-                    "'num_cpu_threads' should be greater than or equal to 0, "
+                    "'INFER.num_cpu_threads' should be greater than or equal to 0, "
                     f"but got {v}"
                 )
             return v
 
         @field_validator("batch_size")
         def batch_size_check(cls, v):
-            if v <= 0:
+            if isinstance(v, int) and v <= 0:
                 raise ValueError(
-                    "'batch_size' should be greater than 0, " f"but got {v}"
+                    f"'INFER.batch_size' should be greater than 0 or None, but got {v}"
                 )
             return v
 
@@ -326,7 +341,8 @@ def use_wandb_check(cls, v, info: ValidationInfo):
         - TRAIN/swa: swa_default  <-- 'swa_default' used here
       - EVAL: eval_default        <-- 'eval_default' used here
       - INFER: infer_default      <-- 'infer_default' used here
-      - _self_
+      - _self_                    <-- config defined in current yaml
+
     mode: train
     seed: 42
     ...
@@ -384,6 +400,7 @@ def use_wandb_check(cls, v, info: ValidationInfo):
         "EVAL.pretrained_model_path",
         "EVAL.eval_with_no_grad",
         "EVAL.compute_metric_by_batch",
+        "EVAL.batch_size",
         "INFER.pretrained_model_path",
         "INFER.export_path",
         "INFER.pdmodel_path",