Add nowcastnet

docs/zh/api/data/dataset.md

@@ -20,5 +20,6 @@
         - VtuDataset
         - MeshAirfoilDataset
         - MeshCylinderDataset
+        - RadarDataset
         - build_dataset
       show_root_heading: false

docs/zh/examples/nowcastnet.md

@@ -0,0 +1,91 @@
+# NowcastNet
+
+=== "模型训练命令"
+
+    暂无
+
+=== "模型评估命令"
+
+    ``` sh
+    # linux
+    wget https://paddle-org.bj.bcebos.com/paddlescience/datasets/nowcastnet/nowcastnet.zip
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/nowcastnet/nowcastnet.zip --output nowcastnet.zip
+    unzip nowcastnet.zip -d datasets/
+    python nowcastnet.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/nowcastnet/nowcastnet_pretrained.pdparams
+    ```
+
+## 1. 背景简介
+
+近年来，深度学习方法已被应用于天气预报，尤其是雷达观测的降水预报。这些方法利用大量雷达复合观测数据来训练神经网络模型，以端到端的方式进行训练，无需明确参考降水过程的物理定律。
+这里复现了一个针对极端降水的非线性短临预报模型——NowcastNet，该模型将物理演变方案和条件学习法统一到一个神经网络框架中，实现了端到端的优化。
+
+## 2. 模型原理
+
+本章节仅对 NowcastNet 的模型原理进行简单地介绍，详细的理论推导请阅读 [Skilful nowcasting of extreme precipitation with NowcastNet](https://www.nature.com/articles/s41586-023-06184-4#Abs1)。
+
+模型的总体结构如图所示：
+
+<figure markdown>
+  ![nowcastnet-arch](nowcastnet/nowcastnet.png){ loading=lazy style="margin:0 auto"}
+  <figcaption>NowcastNet 网络模型</figcaption>
+</figure>
+
+模型使用预训练权重推理，接下来将介绍模型的推理过程。
+
+## 3. 模型构建
+
+在该案例中，用 PaddleScience 代码表示如下：
+
+``` py linenums="24" title="examples/nowcastnet/nowcastnet.py"
+--8<--
+examples/nowcastnet/nowcastnet.py:24:36
+--8<--
+```
+
+``` yaml linenums="35" title="examples/nowcastnet/conf/nowcastnet.yaml"
+--8<--
+examples/nowcastnet/conf/nowcastnet.yaml:35:53
+--8<--
+```
+
+其中，`input_keys` 和 `output_keys` 分别代表网络模型输入、输出变量的名称。
+
+## 4 模型评估可视化
+
+完成上述设置之后，将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`：
+
+``` py linenums="57" title="examples/nowcastnet/nowcastnet.py"
+--8<--
+examples/nowcastnet/nowcastnet.py:57:61
+--8<--
+```
+
+然后构建 VisualizerRadar 生成图片结果：
+
+``` py linenums="69" title="examples/nowcastnet/nowcastnet.py"
+--8<--
+examples/nowcastnet/nowcastnet.py:69:82
+--8<--
+
+## 5. 完整代码
+
+``` py linenums="1" title="examples/nowcastnet/nowcastnet.py"
+--8<--
+examples/nowcastnet/nowcastnet.py
+--8<--
+```
+
+## 6. 结果展示
+
+下图展示了模型的预测结果和真值结果。
+
+<figure markdown>
+  ![result](nowcastnet/pd.gif){ loading=lazy style="margin:0 auto;"}
+  <figcaption>模型预测结果</figcaption>
+</figure>
+
+<figure markdown>
+  ![result](nowcastnet/gt.gif){ loading=lazy style="margin:0 auto;"}
+  <figcaption>模型真值结果</figcaption>
+</figure>

examples/nowcastnet/conf/nowcastnet.yaml

@@ -0,0 +1,57 @@
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    dir: outputs_nowcastnet/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  job:
+    name: ${mode} # name of logfile
+    chdir: false # keep current working direcotry unchaned
+    config:
+      override_dirname:
+        exclude_keys:
+          - TRAIN.checkpoint_path
+          - TRAIN.pretrained_model_path
+          - EVAL.pretrained_model_path
+          - mode
+          - output_dir
+          - log_freq
+  sweep:
+    # output directory for multirun
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: eval # running mode: train/eval
+seed: 42
+output_dir: ${hydra:run.dir}
+NORMAL_DATASET_PATH: datasets/mrms/figure
+LARGE_DATASET_PATH: datasets/mrms/large_figure
+
+# set working condition
+CASE_TYPE: normal  # normal/large
+NUM_SAVE_SAMPLES: 10
+CPU_WORKER: 0
+
+# model settings
+MODEL:
+  normal:
+    input_keys: ["input"]
+    output_keys: ["output"]
+    input_length: 9
+    total_length: 29
+    image_width: 512
+    image_height: 512
+    image_ch: 2
+    ngf: 32
+  large:
+    input_keys: ["input"]
+    output_keys: ["output"]
+    input_length: 9
+    total_length: 29
+    image_width: 1024
+    image_height: 1024
+    image_ch: 2
+    ngf: 32
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: checkpoints/paddle_mrms_model

examples/nowcastnet/nowcastnet.py

@@ -0,0 +1,96 @@
+"""
+Reference: https://codeocean.com/capsule/3935105/tree/v1
+"""
+from os import path as osp
+
+import hydra
+import paddle
+from omegaconf import DictConfig
+
+import ppsci
+from ppsci.utils import logger
+
+
+def train(cfg: DictConfig):
+    print("Not supported.")
+
+
+def evaluate(cfg: DictConfig):
+    # set random seed for reproducibility
+    ppsci.utils.misc.set_random_seed(cfg.seed)
+    # initialize logger
+    logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
+
+    if cfg.CASE_TYPE == "large":
+        dataset_path = cfg.LARGE_DATASET_PATH
+        model_cfg = cfg.MODEL.large
+        output_dir = osp.join(cfg.output_dir, "large")
+    elif cfg.CASE_TYPE == "normal":
+        dataset_path = cfg.NORMAL_DATASET_PATH
+        model_cfg = cfg.MODEL.normal
+        output_dir = osp.join(cfg.output_dir, "normal")
+    else:
+        raise ValueError(
+            f"cfg.CASE_TYPE should in ['normal', 'large'], but got '{cfg.mode}'"
+        )
+    model = ppsci.arch.NowcastNet(**model_cfg)
+
+    input_keys = ("radar_frames",)
+    dataset_param = {
+        "input_keys": input_keys,
+        "label_keys": (),
+        "image_width": model_cfg.image_width,
+        "image_height": model_cfg.image_height,
+        "total_length": model_cfg.total_length,
+        "dataset_path": dataset_path,
+        "data_type": paddle.get_default_dtype(),
+    }
+    test_data_loader = paddle.io.DataLoader(
+        ppsci.data.dataset.RadarDataset(**dataset_param),
+        batch_size=1,
+        shuffle=False,
+        num_workers=cfg.CPU_WORKER,
+        drop_last=True,
+    )
+
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model,
+        output_dir=output_dir,
+        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+    )
+
+    for batch_id, test_ims in enumerate(test_data_loader):
+        test_ims = test_ims[0][input_keys[0]].numpy()
+        frames_tensor = paddle.to_tensor(
+            data=test_ims, dtype=paddle.get_default_dtype()
+        )
+        if batch_id <= cfg.NUM_SAVE_SAMPLES:
+            visualizer = {
+                "v_nowcastnet": ppsci.visualize.VisualizerRadar(
+                    {"input": frames_tensor},
+                    {
+                        "output": lambda out: out["output"],
+                    },
+                    prefix="v_nowcastnet",
+                    case_type=cfg.CASE_TYPE,
+                    total_length=model_cfg.total_length,
+                )
+            }
+            solver.visualizer = visualizer
+            # visualize prediction
+            solver.visualize(batch_id)
+
+
+@hydra.main(version_base=None, config_path="./conf", config_name="nowcastnet.yaml")
+def main(cfg: DictConfig):
+    if cfg.mode == "train":
+        train(cfg)
+    elif cfg.mode == "eval":
+        evaluate(cfg)
+    else:
+        raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+
+
+if __name__ == "__main__":
+    main()

mkdocs.yml

@@ -69,6 +69,7 @@ nav:
         - EPNN: zh/examples/epnn.md
       - 地球科学(AI for Earth Science):
         - FourCastNet: zh/examples/fourcastnet.md
+        - NowcastNet: zh/examples/nowcastnet.md
   - API文档:
     - " ":
       - ppsci.arch: zh/api/arch.md

ppsci/arch/__init__.py

@@ -32,6 +32,7 @@
 from ppsci.arch.afno import PrecipNet  # isort:skip
 from ppsci.arch.unetex import UNetEx  # isort:skip
 from ppsci.arch.epnn import Epnn  # isort:skip
+from ppsci.arch.nowcastnet import NowcastNet  # isort:skip
 from ppsci.utils import logger  # isort:skip
 
 
@@ -52,6 +53,7 @@
     "PrecipNet",
     "UNetEx",
     "Epnn",
+    "NowcastNet",
     "build_model",
 ]