refine yinglong predictor and use config instead of argparser

deploy/python_infer/base.py

@@ -163,7 +163,7 @@ def _create_paddle_predictor(
 
         # enable memory optim
         config.enable_memory_optim()
-        config.disable_glog_info()
+        # config.disable_glog_info()
         # enable zero copy
         config.switch_use_feed_fetch_ops(False)
         config.switch_ir_optim(self.ir_optim)

examples/yinglong/conf/yinglong_12.yaml

@@ -0,0 +1,55 @@
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    # dir: outputs_yinglong/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+    dir: ./outputs_yinglong_12
+  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
+          - INFER.pretrained_model_path
+          - mode
+          - output_dir
+          - log_freq
+  callbacks:
+    init_callback:
+      _target_: ppsci.utils.callbacks.InitCallback
+  sweep:
+    # output directory for multirun
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: train # running mode: train/eval
+seed: 2023
+output_dir: ${hydra:run.dir}
+log_freq: 20
+
+# inference settings
+INFER:
+  pretrained_model_path: null
+  export_path: ./inference/yinglong_12
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${INFER.export_path}.onnx
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: false
+  min_subgraph_size: 30
+  gpu_mem: 100
+  gpu_id: 0
+  max_batch_size: 1
+  num_cpu_threads: 10
+  batch_size: 1
+  mean_path: ./hrrr_example_24vars/stat/mean_crop.npy
+  std_path: ./hrrr_example_24vars/stat/std_crop.npy
+  input_file: ./hrrr_example_24vars/valid/2022/01/01.h5
+  init_time: 2022/01/01/00
+  nwp_file: ./hrrr_example_24vars/nwp_convert/2022/01/01/00.h5
+  num_timestamps: 22

examples/yinglong/conf/yinglong_24.yaml

@@ -0,0 +1,55 @@
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    # dir: outputs_yinglong/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+    dir: ./outputs_yinglong_24
+  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
+          - INFER.pretrained_model_path
+          - mode
+          - output_dir
+          - log_freq
+  callbacks:
+    init_callback:
+      _target_: ppsci.utils.callbacks.InitCallback
+  sweep:
+    # output directory for multirun
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: train # running mode: train/eval
+seed: 2023
+output_dir: ${hydra:run.dir}
+log_freq: 20
+
+# inference settings
+INFER:
+  pretrained_model_path: null
+  export_path: ./inference/yinglong_24
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${INFER.export_path}.onnx
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: false
+  min_subgraph_size: 30
+  gpu_mem: 100
+  gpu_id: 0
+  max_batch_size: 1
+  num_cpu_threads: 10
+  batch_size: 1
+  mean_path: ./hrrr_example_69vars/stat/mean_crop.npy
+  std_path: ./hrrr_example_69vars/stat/std_crop.npy
+  input_file: ./hrrr_example_69vars/valid/2022/01/01.h5
+  init_time: 2022/01/01/00
+  nwp_file: ./hrrr_example_69vars/nwp_convert/2022/01/01/00.h5
+  num_timestamps: 22

examples/yinglong/predict_12layers.py

@@ -12,74 +12,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
 from os import path as osp
 
 import h5py
+import hydra
 import numpy as np
 import paddle
 import pandas as pd
+from omegaconf import DictConfig
 from packaging import version
 
 from examples.yinglong.plot import save_plot_weather_from_dict
-from examples.yinglong.predictor import YingLong
+from examples.yinglong.predictor import YingLongPredictor
 from ppsci.utils import logger
 
 
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model_file",
-        type=str,
-        default="./yinglong_models/yinglong_12.pdmodel",
-        help="Model filename",
-    )
-    parser.add_argument(
-        "--params_file",
-        type=str,
-        default="./yinglong_models/yinglong_12.pdiparams",
-        help="Parameter filename",
-    )
-    parser.add_argument(
-        "--mean_path",
-        type=str,
-        default="./hrrr_example_24vars/stat/mean_crop.npy",
-        help="Mean filename",
-    )
-    parser.add_argument(
-        "--std_path",
-        type=str,
-        default="./hrrr_example_24vars/stat/std_crop.npy",
-        help="Standard deviation filename",
-    )
-    parser.add_argument(
-        "--input_file",
-        type=str,
-        default="./hrrr_example_24vars/valid/2022/01/01.h5",
-        help="Input filename",
-    )
-    parser.add_argument(
-        "--init_time", type=str, default="2022/01/01/00", help="Init time"
-    )
-    parser.add_argument(
-        "--nwp_file",
-        type=str,
-        default="./hrrr_example_24vars/nwp_convert/2022/01/01/00.h5",
-        help="NWP filename",
-    )
-    parser.add_argument(
-        "--num_timestamps", type=int, default=22, help="Number of timestamps"
-    )
-    parser.add_argument(
-        "--output_path", type=str, default="output", help="Output file path"
-    )
-
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-    logger.init_logger("ppsci", osp.join(args.output_path, "predict.log"), "info")
+def inference(cfg: DictConfig):
     # log paddlepaddle's version
     if version.Version(paddle.__version__) != version.Version("0.0.0"):
         paddle_version = paddle.__version__
@@ -93,19 +41,20 @@ def main():
 
     logger.info(f"Using paddlepaddle {paddle_version}")
 
-    num_timestamps = args.num_timestamps
+    num_timestamps = cfg.INFER.num_timestamps
     # create predictor
-    predictor = YingLong(
-        args.model_file, args.params_file, args.mean_path, args.std_path
-    )
+    # predictor = YingLong(
+    #     args.model_file, args.params_file, args.mean_path, args.std_path
+    # )
+    predictor = YingLongPredictor(cfg)
 
     # load data
     # HRRR Crop use 24 atmospheric variable，their index in the dataset is from 0 to 23.
     # The variable name is 'z50', 'z500', 'z850', 'z1000', 't50', 't500', 't850', 'z1000',
     # 's50', 's500', 's850', 's1000', 'u50', 'u500', 'u850', 'u1000', 'v50', 'v500',
     # 'v850', 'v1000', 'mslp', 'u10', 'v10', 't2m'.
-    input_file = h5py.File(args.input_file, "r")["fields"]
-    nwp_file = h5py.File(args.nwp_file, "r")["fields"]
+    input_file = h5py.File(cfg.INFER.input_file, "r")["fields"]
+    nwp_file = h5py.File(cfg.INFER.nwp_file, "r")["fields"]
 
     # input_data.shape: (1, 24, 440, 408)
     input_data = input_file[0:1]
@@ -115,18 +64,18 @@ def main():
     ground_truth = input_file[1 : num_timestamps + 1]
 
     # create time stamps
-    cur_time = pd.to_datetime(args.init_time, format="%Y/%m/%d/%H")
+    cur_time = pd.to_datetime(cfg.INFER.init_time, format="%Y/%m/%d/%H")
     time_stamps = [[cur_time]]
     for _ in range(num_timestamps):
         cur_time += pd.Timedelta(hours=1)
         time_stamps.append([cur_time])
 
     # run predictor
-    pred_data = predictor(input_data, time_stamps, nwp_data)
+    pred_data = predictor.predict(input_data, time_stamps, nwp_data)
     pred_data = pred_data.squeeze(axis=1)  # (num_timestamps, 24, 440, 408)
 
     # save predict data
-    save_path = osp.join(args.output_path, "result.npy")
+    save_path = osp.join(cfg.output_dir, "result.npy")
     np.save(save_path, pred_data)
     logger.info(f"Save output to {save_path}")
 
@@ -139,15 +88,15 @@ def main():
     data_dict = {}
     visu_keys = []
     for i in range(num_timestamps):
-        visu_key = f"Init time: {args.init_time}h\n Ground truth: {i+1}h"
+        visu_key = f"Init time: {cfg.INFER.init_time}h\n Ground truth: {i+1}h"
         visu_keys.append(visu_key)
         data_dict[visu_key] = ground_truth_wind[i]
-        visu_key = f"Init time: {args.init_time}h\n YingLong-12 Layers: {i+1}h"
+        visu_key = f"Init time: {cfg.INFER.init_time}h\n YingLong-12 Layers: {i+1}h"
         visu_keys.append(visu_key)
         data_dict[visu_key] = pred_wind[i]
 
     save_plot_weather_from_dict(
-        foldername=args.output_path,
+        foldername=cfg.output_dir,
         data_dict=data_dict,
         visu_keys=visu_keys,
         xticks=np.linspace(0, 407, 7),
@@ -159,7 +108,15 @@ def main():
         colorbar_label="m/s",
         num_timestamps=12,  # only plot 12 timestamps
     )
-    logger.info(f"Save plot to {args.output_path}")
+    logger.info(f"Save plot to {cfg.output_dir}")
+
+
+@hydra.main(version_base=None, config_path="./conf", config_name="yinglong_12.yaml")
+def main(cfg: DictConfig):
+    if cfg.mode == "infer":
+        inference(cfg)
+    else:
+        raise ValueError(f"cfg.mode should in ['infer'], but got '{cfg.mode}'")
 
 
 if __name__ == "__main__":