change Normalize, Log1p, CropData, SqueezeData to inplace transform t…

…o avoid copying data for efficiency

ppsci/data/process/transform/preprocess.py

@@ -71,6 +71,8 @@ def __call__(self, input_dict, label_dict, weight_dict):
 class Normalize:
     """Normalize data class.
 
+    NOTE: This transform will modify the input data dict inplace.
+
     Args:
         mean (Union[np.ndarray, Tuple[float, ...]]): Mean of training dataset.
         std (Union[np.ndarray, Tuple[float, ...]]): Standard Deviation of training dataset.
@@ -96,20 +98,20 @@ def __init__(
         self.apply_keys = apply_keys
 
     def __call__(self, input_item, label_item, weight_item):
-        input_item_copy = {**input_item}
-        label_item_copy = {**label_item}
         if "input" in self.apply_keys:
-            for key, value in input_item_copy.items():
-                input_item_copy[key] = (value - self.mean) / self.std
+            for key, value in input_item.items():
+                input_item[key] = (value - self.mean) / self.std
         if "label" in self.apply_keys:
-            for key, value in label_item_copy.items():
-                label_item_copy[key] = (value - self.mean) / self.std
-        return input_item_copy, label_item_copy, weight_item
+            for key, value in label_item.items():
+                label_item[key] = (value - self.mean) / self.std
+        return input_item, label_item, weight_item
 
 
 class Log1p:
     """Calculates the natural logarithm of one plus the data, element-wise.
 
+    NOTE: This transform will modify the input data dict inplace.
+
     Args:
         scale (float, optional): Scale data. Defaults to 1.0.
         apply_keys (Tuple[str, ...], optional): Which data is the log1p method applied to. Defaults to ("input", "label").
@@ -132,22 +134,22 @@ def __init__(
         self.apply_keys = apply_keys
 
     def __call__(self, input_item, label_item, weight_item):
-        input_item_copy = {**input_item}
-        label_item_copy = {**label_item}
         if "input" in self.apply_keys:
-            for key, value in input_item_copy.items():
-                input_item_copy[key] = np.log1p(value / self.scale)
+            for key, value in input_item.items():
+                input_item[key] = np.log1p(value / self.scale)
         if "label" in self.apply_keys:
-            for key, value in label_item_copy.items():
-                label_item_copy[key] = np.log1p(value / self.scale)
-        return input_item_copy, label_item_copy, weight_item
+            for key, value in label_item.items():
+                label_item[key] = np.log1p(value / self.scale)
+        return input_item, label_item, weight_item
 
 
 class CropData:
     """Crop data class.
 
     This class is used to crop data based on a specified bounding box.
 
+    NOTE: This transform will modify the input data dict inplace.
+
     Args:
         xmin (Tuple[int, ...]): Bottom left corner point, [x0, y0].
         xmax (Tuple[int, ...]): Top right corner point, [x1, y1].
@@ -182,24 +184,24 @@ def __init__(
         self.apply_keys = apply_keys
 
     def __call__(self, input_item, label_item, weight_item):
-        input_item_copy = {**input_item}
-        label_item_copy = {**label_item}
         if "input" in self.apply_keys:
-            for key, value in input_item_copy.items():
-                input_item_copy[key] = value[
+            for key, value in input_item.items():
+                input_item[key] = value[
                     :, self.xmin[0] : self.xmax[0], self.xmin[1] : self.xmax[1]
                 ]
         if "label" in self.apply_keys:
-            for key, value in label_item_copy.items():
-                label_item_copy[key] = value[
+            for key, value in label_item.items():
+                label_item[key] = value[
                     :, self.xmin[0] : self.xmax[0], self.xmin[1] : self.xmax[1]
                 ]
-        return input_item_copy, label_item_copy, weight_item
+        return input_item, label_item, weight_item
 
 
 class SqueezeData:
     """Squeeze data class.
 
+    NOTE: This transform will modify the input data dict inplace.
+
     Args:
         apply_keys (Tuple[str, ...], optional): Which data is the squeeze method applied to. Defaults to ("input", "label").
 
@@ -216,27 +218,25 @@ def __init__(self, apply_keys: Tuple[str, ...] = ("input", "label")):
         self.apply_keys = apply_keys
 
     def __call__(self, input_item, label_item, weight_item):
-        input_item_copy = {**input_item}
-        label_item_copy = {**label_item}
         if "input" in self.apply_keys:
-            for key, value in input_item_copy.items():
+            for key, value in input_item.items():
                 if value.ndim == 4:
                     B, C, H, W = value.shape
-                    input_item_copy[key] = value.reshape((B * C, H, W))
+                    input_item[key] = value.reshape((B * C, H, W))
                 if value.ndim != 3:
                     raise ValueError(
                         f"Only support squeeze data to ndim=3 now, but got ndim={value.ndim}"
                     )
         if "label" in self.apply_keys:
-            for key, value in label_item_copy.items():
+            for key, value in label_item.items():
                 if value.ndim == 4:
                     B, C, H, W = value.shape
-                    label_item_copy[key] = value.reshape((B * C, H, W))
+                    label_item[key] = value.reshape((B * C, H, W))
                 if value.ndim != 3:
                     raise ValueError(
                         f"Only support squeeze data to ndim=3 now, but got ndim={value.ndim}"
                     )
-        return input_item_copy, label_item_copy, weight_item
+        return input_item, label_item, weight_item
 
 
 class FunctionalTransform: