[Refine] Refine evaluation output

examples/darcy/darcy2d.py

@@ -109,7 +109,7 @@ def poisson_ref_compute_func(_in):
         cfg.NPOINT_PDE + cfg.NPOINT_BC, evenly=True
     )
     visualizer = {
-        "visualize_p": ppsci.visualize.VisualizerVtu(
+        "visualize_p_ux_uy": ppsci.visualize.VisualizerVtu(
             vis_points,
             {
                 "p": lambda d: d["p"],
@@ -246,7 +246,7 @@ def poisson_ref_compute_func(_in):
         cfg.NPOINT_PDE + cfg.NPOINT_BC, evenly=True
     )
     visualizer = {
-        "visualize_p": ppsci.visualize.VisualizerVtu(
+        "visualize_p_ux_uy": ppsci.visualize.VisualizerVtu(
             vis_points,
             {
                 "p": lambda d: d["p"],

ppsci/solver/eval.py

@@ -17,6 +17,7 @@
 import time
 from typing import TYPE_CHECKING
 from typing import Dict
+from typing import Optional
 from typing import Tuple
 from typing import Union
 
@@ -59,7 +60,7 @@ def _get_dataset_length(
 
 
 def _eval_by_dataset(
-    solver: "solver.Solver", epoch_id: int, log_freq: int
+    solver: "solver.Solver", epoch_id: Optional[int], log_freq: int
 ) -> Tuple[float, Dict[str, Dict[str, float]]]:
     """Evaluate with computing metric on total samples(default process).
 
@@ -68,7 +69,7 @@ def _eval_by_dataset(
 
     Args:
         solver (solver.Solver): Main Solver.
-        epoch_id (int): Epoch id.
+        epoch_id (Optional[int]): Epoch id.
         log_freq (int): Log evaluation information every `log_freq` steps.
 
     Returns:
@@ -189,7 +190,7 @@ def _eval_by_dataset(
 
 
 def _eval_by_batch(
-    solver: "solver.Solver", epoch_id: int, log_freq: int
+    solver: "solver.Solver", epoch_id: Optional[int], log_freq: int
 ) -> Tuple[float, Dict[str, Dict[str, float]]]:
     """Evaluate with computing metric by batch, which is memory-efficient.
 
@@ -199,7 +200,7 @@ def _eval_by_batch(
 
     Args:
         solver (solver.Solver): Main Solver.
-        epoch_id (int): Epoch id.
+        epoch_id (Optional[int]): Epoch id.
         log_freq (int): Log evaluation information every `log_freq` steps.
 
     Returns:
@@ -303,13 +304,13 @@ def _eval_by_batch(
 
 
 def eval_func(
-    solver: "solver.Solver", epoch_id: int, log_freq: int
+    solver: "solver.Solver", epoch_id: Optional[int], log_freq: int
 ) -> Tuple[float, Dict[str, Dict[str, float]]]:
     """Evaluation function.
 
     Args:
         solver (solver.Solver): Main Solver.
-        epoch_id (int): Epoch id.
+        epoch_id (Optional[int]): Epoch id.
         log_freq (int): Log evaluation information every `log_freq` steps.
 
     Returns:

ppsci/solver/printer.py

@@ -132,11 +132,17 @@ def log_eval_info(
 
     epoch_width = len(str(solver.epochs))
     iters_width = len(str(iters_per_epoch))
-    logger.info(
-        f"[Eval][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
-        f"[Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
-        f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
-    )
+    if isinstance(epoch_id, int):
+        logger.info(
+            f"[Eval][Epoch {epoch_id:>{epoch_width}}/{solver.epochs}]"
+            f"[Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
+            f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
+        )
+    else:
+        logger.info(
+            f"[Eval][Iter {iter_id:>{iters_width}}/{iters_per_epoch}] "
+            f"{metric_msg}, {time_msg}, {ips_msg}, {eta_msg}"
+        )
 
     # logger.scalar(
     #     {

ppsci/solver/solver.py

@@ -619,11 +619,13 @@ def finetune(self, pretrained_model_path: str) -> None:
         self.train()
 
     @misc.run_on_eval_mode
-    def eval(self, epoch_id: int = 0) -> Tuple[float, Dict[str, Dict[str, float]]]:
+    def eval(
+        self, epoch_id: Optional[int] = None
+    ) -> Tuple[float, Dict[str, Dict[str, float]]]:
         """Evaluation.
 
         Args:
-            epoch_id (int, optional): Epoch id. Defaults to 0.
+            epoch_id (Optional[int]): Epoch id. Defaults to None.
 
         Returns:
             Tuple[float, Dict[str, Dict[str, float]]]: A targe metric value(float) and
@@ -636,23 +638,30 @@ def eval(self, epoch_id: int = 0) -> Tuple[float, Dict[str, Dict[str, float]]]:
         metric_msg = ", ".join(
             [self.eval_output_info[key].avg_info for key in self.eval_output_info]
         )
-        logger.info(f"[Eval][Epoch {epoch_id}][Avg] {metric_msg}")
+
+        if isinstance(epoch_id, int):
+            logger.info(f"[Eval][Epoch {epoch_id}][Avg] {metric_msg}")
+        else:
+            logger.info(f"[Eval][Avg] {metric_msg}")
         self.eval_output_info.clear()
 
         return result
 
     @misc.run_on_eval_mode
-    def visualize(self, epoch_id: int = 0):
+    def visualize(self, epoch_id: Optional[int] = None):
         """Visualization.
 
         Args:
-            epoch_id (int, optional): Epoch id. Defaults to 0.
+            epoch_id (Optional[int]): Epoch id. Defaults to None.
         """
         # set visualize func
         self.visu_func = ppsci.solver.visu.visualize_func
 
         self.visu_func(self, epoch_id)
-        logger.info(f"[Visualize][Epoch {epoch_id}] Finish visualization")
+        if isinstance(epoch_id, int):
+            logger.info(f"[Visualize][Epoch {epoch_id}] Finish visualization")
+        else:
+            logger.info("[Visualize] Finish visualization")
 
     @misc.run_on_eval_mode
     def predict(

ppsci/solver/visu.py

@@ -17,6 +17,7 @@
 import os
 import os.path as osp
 from typing import TYPE_CHECKING
+from typing import Optional
 
 import paddle
 
@@ -26,12 +27,12 @@
     from ppsci import solver
 
 
-def visualize_func(solver: "solver.Solver", epoch_id: int):
+def visualize_func(solver: "solver.Solver", epoch_id: Optional[int]):
     """Visualization program.
 
     Args:
         solver (solver.Solver): Main Solver.
-        epoch_id (int): Epoch id.
+        epoch_id (Optional[int]): Epoch id.
     """
     for _, _visualizer in solver.visualizer.items():
         all_input = misc.Prettydefaultdict(list)
@@ -87,7 +88,9 @@ def visualize_func(solver: "solver.Solver", epoch_id: int):
         # save visualization
         with misc.RankZeroOnly(solver.rank) as is_master:
             if is_master:
-                visual_dir = osp.join(solver.output_dir, "visual", f"epoch_{epoch_id}")
+                visual_dir = osp.join(solver.output_dir, "visual")
+                if epoch_id:
+                    visual_dir = osp.join(visual_dir, f"epoch_{epoch_id}")
                 os.makedirs(visual_dir, exist_ok=True)
                 _visualizer.save(
                     osp.join(visual_dir, _visualizer.prefix),

ppsci/utils/download.py

@@ -157,6 +157,7 @@ def _download(url, path, md5sum=None):
                     if chunk:
                         f.write(chunk)
         shutil.move(tmp_fullname, fullname)
+        logger.message(f"Finished downloading pretrained model and saved to {fullname}")
 
     return fullname
 

ppsci/utils/ema.py

@@ -41,7 +41,7 @@ def __init__(self, model: nn.Layer, decay: Optional[float] = None):
         self.model = model  # As a quick reference to online model
         self.decay = decay
 
-        self.params_shadow: Dict[str, paddle.Tensor] = {}  # ema param or bufer
+        self.params_shadow: Dict[str, paddle.Tensor] = {}  # ema param or buffer
         self.params_backup: Dict[str, paddle.Tensor] = {}  # used for apply and restore
         for name, param_or_buffer in itertools.chain(
             self.model.named_parameters(), self.model.named_buffers()