[Doc] Update url (PaddlePaddle#966)

* update SPINN url

* update url

* annotate latex which may occur error when latex font is not installed

* add mtl submodule to loss.__all__

* update doc

* update pre-commit guidance

* update synchronization of upstream guidance

* update docs

* update batch size computation

* rename IJACA -> IJCAI

* update description

* remove redundant import

docs/zh/development.md

@@ -909,7 +909,18 @@ PaddleScience 文档基于 [Mkdocs-Material](https://squidfunk.github.io/mkdocs-
 
 ### 3.4 预览文档
 
-在 `PaddleScience/` 目录下执行以下命令，等待构建完成后，点击显示的链接进入本地网页预览文档内容。
+假设撰写好的文档位置为 `PaddleScience/docs/zh/examples/your_exmaple.md`，为了让其在 PaddleScience 官网 [经典案例](https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/allen_cahn/) 左侧目录中展示，需要修改 `PaddleScience/mkdocs.yml`。将 `your_exmaple.md` 的相对路径仿照其他案例，添加到 `- 经典案例:` 下的列表中，如下高亮行所示。
+
+``` yaml hl_lines="23" title="PaddleScience/mkdocs.yml"
+...
+--8<--
+mkdocs.yml:38:58
+--8<--
+        - EXAMPLE_NAME: docs/zh/examples/your_exmaple.md
+...
+```
+
+然后在 `PaddleScience/` 目录下执行以下命令，等待构建完成后，点击显示的链接进入本地网页预览文档内容。
 
 ``` sh
 mkdocs serve
@@ -944,11 +955,11 @@ mkdocs serve
 
 ### 4.2 同步上游代码
 
-在开发过程中，上游代码可能经过更新，因此需要执行以下命令，先将上游的最新代码拉取下来，合并到当前代码中，与上游最新代码进行同步。
+在开发过程中，上游代码可能经过更新，因此需要执行以下命令，将上游的最新代码拉取下来，合并到当前代码中，与上游最新代码进行同步。
 
 ``` sh
 git remote add upstream https://github.com/PaddlePaddle/PaddleScience.git
-git fetch upstream upstream_develop
+git fetch upstream develop:upstream_develop
 git merge upstream_develop
 ```
 

docs/zh/examples/allen_cahn.md

@@ -207,7 +207,7 @@ examples/allen_cahn/allen_cahn_piratenet.py:138:156
 --8<--
 ```
 
-### 3.9 模型训练、评估与可视化
+### 3.8 模型训练、评估与可视化
 
 完成上述设置之后，只需要将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`，然后启动训练、评估、可视化。
 

jointContribution/IJACA_2024/README.md → jointContribution/IJCAI_2024/README.md

@@ -1,24 +1,29 @@
-# IJACA_Code
-The paddle version of the top three in each track of the IJACA 2024 competition.
+# IJCAI_Code
+
+The paddle version of the top three in each track of the IJCAI 2024 competition.
 
 Inference codes only now.
 
 ## Dataset
+
 Please refer to the .ipynb files in each directory to download the data and set the corresponding parameters.
 
 ## Checkpoint
+
 Donwload checkpoints:
+
 ``` sh
-cd PaddleScience/jointContribution/IJACA_2024
+cd PaddleScience/jointContribution/IJCAI_2024
 # linux
-wget -nc https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJACA_2024_ckpts.tar.gz
+wget -nc https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJCAI_2024_ckpts.tar.gz
 # windows
-# curl https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJACA_2024_ckpts.tar.gz
+# curl https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJCAI_2024_ckpts.tar.gz
 ```
 
 Unzip the checkpoints and move them to the corresponding directory:
+
 ``` sh
-tar -xvzf IJACA_2024_ckpts.tar.gz
+tar -xvzf IJCAI_2024_ckpts.tar.gz
 
 # aminos
 mkdir -p ./aminos/Logger/states/
@@ -40,17 +45,21 @@ mv ./ckpts/bju/pretrained_checkpoint.pdparams ./bju/pretrained_checkpoint.pdpara
 ```
 
 ## Inference
+
 First enter the corresponding directory. For example "aminos":
+
 ``` sh
 cd aminos
 ```
 
 Install requirements:
+
 ``` sh
 pip install -r requirements.txt
 ```
 
 Run Inference:
+
 ``` py
 ### aminos
 python infer.py --dataset_dir "./Datasets" --load_index="90"

jointContribution/IJACA_2024/bju/download_dataset.ipynb → jointContribution/IJCAI_2024/bju/download_dataset.ipynb

@@ -2366,8 +2366,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!wget https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJACA_2024_ckpts.tar.gz\n",
-    "!tar -zxvf IJACA_2024_ckpts.tar.gz\n",
+    "!wget https://paddle-org.bj.bcebos.com/paddlescience/models/contrib/IJCAI_2024_ckpts.tar.gz\n",
+    "!tar -zxvf IJCAI_2024_ckpts.tar.gz\n",
     "! mv ckpts/bju/geom/ckpt/checkpoint_pointbert.pdparams ./geom/ckpt/checkpoint_pointbert.pdparams"
    ]
   }

ppsci/solver/printer.py

@@ -12,13 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import datetime
 from typing import TYPE_CHECKING
 from typing import Dict
+from typing import Optional
 
 from paddle import device
 
@@ -114,7 +111,7 @@ def log_train_info(
 def log_eval_info(
     solver: "solver.Solver",
     batch_size: int,
-    epoch_id: int,
+    epoch_id: Optional[int],
     iters_per_epoch: int,
     iter_id: int,
 ):

ppsci/solver/train.py

@@ -17,6 +17,9 @@
 import sys
 import time
 from typing import TYPE_CHECKING
+from typing import Dict
+from typing import Sequence
+from typing import Union
 
 import paddle
 from paddle.distributed.fleet.utils import hybrid_parallel_util as hpu
@@ -29,6 +32,29 @@
     from ppsci import solver
 
 
+def _compute_batch_size(
+    input_dict: Dict[str, Union[paddle.Tensor, Sequence[paddle.Tensor]]]
+) -> int:
+    """Compute batch size from given input dict.
+
+    NOTE: Returned `batch_size` might be inaccurate, but it won't affect the correctness
+    of the training results because `batch_size` is now only used for timing.
+
+    Args:
+        input_dict (Dict[str, Union[paddle.Tensor, Sequence[paddle.Tensor]]]): Given input dict.
+
+    Returns:
+        int: Batch size of input dict.
+    """
+    sample = next(iter(input_dict.values()))
+    if hasattr(sample, "shape"):
+        return sample.shape[0]
+    elif hasattr(sample, "__len__"):  # Might be inaccurate here.
+        return len(sample)
+    else:
+        raise ValueError("Unsupported type of input dict value.")
+
+
 def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
     """Train program for one epoch.
 
@@ -77,7 +103,7 @@ def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int):
             input_dicts.append(input_dict)
             label_dicts.append(label_dict)
             weight_dicts.append(weight_dict)
-            total_batch_size += next(iter(input_dict.values())).shape[0]
+            total_batch_size += _compute_batch_size(input_dict)
             reader_tic = time.perf_counter()
 
         loss_dict = misc.Prettydefaultdict(float)
@@ -227,7 +253,7 @@ def train_LBFGS_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int
             input_dicts.append(input_dict)
             label_dicts.append(label_dict)
             weight_dicts.append(weight_dict)
-            total_batch_size += next(iter(input_dict.values())).shape[0]
+            total_batch_size += _compute_batch_size(input_dict)
             reader_tic = time.perf_counter()
 
         def closure() -> paddle.Tensor:

ppsci/utils/misc.py

@@ -638,7 +638,7 @@ def check_flag_enabled(flag_name: str) -> bool:
     """Check whether the flag is enabled.
 
     Args:
-        flag_name(str): Flag name to be checked whether enabled or disabled.
+        flag_name (str): Flag name to be checked whether enabled or disabled.
 
     Returns:
         bool: Whether given flag name is enabled in environment.