1814 Enhance transform chain error logging

monai/transforms/transform.py

@@ -16,7 +16,9 @@
 from typing import Any, Callable, Dict, Generator, Hashable, Iterable, List, Optional, Tuple
 
 import numpy as np
+import torch
 
+from monai import transforms
 from monai.config import KeysCollection
 from monai.utils import MAX_SEED, ensure_tuple
 
@@ -45,6 +47,27 @@ def apply_transform(transform: Callable, data, map_items: bool = True):
             return [transform(item) for item in data]
         return transform(data)
     except Exception as e:
+
+        if not isinstance(transform, transforms.compose.Compose):
+            # log the input data information of exact transform in the transform chain
+            datastats = transforms.utility.array.DataStats(data_shape=False, value_range=False)
+            datastats._logger.info("input data information of the runtime error transform:")
+            if isinstance(data, (list, tuple)):
+                data = data[0]
+
+            def _log_stats(data, prefix: Optional[str] = "Data"):
+                if isinstance(data, (np.ndarray, torch.Tensor)):
+                    # log data type, shape, range for array
+                    datastats(img=data, data_shape=True, value_range=True, prefix=prefix)  # type: ignore
+                else:
+                    # log data type and value for other meta data
+                    datastats(img=data, data_value=True, prefix=prefix)
+
+            if isinstance(data, dict):
+                for k, v in data.items():
+                    _log_stats(data=v, prefix=k)
+            else:
+                _log_stats(data=data)
         raise RuntimeError(f"applying transform {transform}") from e
 
 

monai/transforms/utility/array.py

@@ -394,6 +394,7 @@ class DataStats(Transform):
     def __init__(
         self,
         prefix: str = "Data",
+        data_type: bool = True,
         data_shape: bool = True,
         value_range: bool = True,
         data_value: bool = False,
@@ -403,6 +404,7 @@ def __init__(
         """
         Args:
             prefix: will be printed in format: "{prefix} statistics".
+            data_type: whether to show the type of input data.
             data_shape: whether to show the shape of input data.
             value_range: whether to show the value range of input data.
             data_value: whether to show the raw value of input data.
@@ -419,6 +421,7 @@ def __init__(
         if not isinstance(prefix, str):
             raise AssertionError("prefix must be a string.")
         self.prefix = prefix
+        self.data_type = data_type
         self.data_shape = data_shape
         self.value_range = value_range
         self.data_value = data_value
@@ -438,6 +441,7 @@ def __call__(
         self,
         img: NdarrayTensor,
         prefix: Optional[str] = None,
+        data_type: Optional[bool] = None,
         data_shape: Optional[bool] = None,
         value_range: Optional[bool] = None,
         data_value: Optional[bool] = None,
@@ -448,6 +452,8 @@ def __call__(
         """
         lines = [f"{prefix or self.prefix} statistics:"]
 
+        if self.data_type if data_type is None else data_type:
+            lines.append(f"Type: {type(img)}")
         if self.data_shape if data_shape is None else data_shape:
             lines.append(f"Shape: {img.shape}")
         if self.value_range if value_range is None else value_range:

monai/transforms/utility/dictionary.py

@@ -507,6 +507,7 @@ def __init__(
         self,
         keys: KeysCollection,
         prefix: Union[Sequence[str], str] = "Data",
+        data_type: Union[Sequence[bool], bool] = True,
         data_shape: Union[Sequence[bool], bool] = True,
         value_range: Union[Sequence[bool], bool] = True,
         data_value: Union[Sequence[bool], bool] = False,
@@ -520,6 +521,8 @@ def __init__(
                 See also: :py:class:`monai.transforms.compose.MapTransform`
             prefix: will be printed in format: "{prefix} statistics".
                 it also can be a sequence of string, each element corresponds to a key in ``keys``.
+            data_type: whether to show the type of input data.
+                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
             data_shape: whether to show the shape of input data.
                 it also can be a sequence of bool, each element corresponds to a key in ``keys``.
             value_range: whether to show the value range of input data.
@@ -538,6 +541,7 @@ def __init__(
         """
         super().__init__(keys, allow_missing_keys)
         self.prefix = ensure_tuple_rep(prefix, len(self.keys))
+        self.data_type = ensure_tuple_rep(data_type, len(self.keys))
         self.data_shape = ensure_tuple_rep(data_shape, len(self.keys))
         self.value_range = ensure_tuple_rep(value_range, len(self.keys))
         self.data_value = ensure_tuple_rep(data_value, len(self.keys))
@@ -547,12 +551,13 @@ def __init__(
 
     def __call__(self, data: Mapping[Hashable, NdarrayTensor]) -> Dict[Hashable, NdarrayTensor]:
         d = dict(data)
-        for key, prefix, data_shape, value_range, data_value, additional_info in self.key_iterator(
-            d, self.prefix, self.data_shape, self.value_range, self.data_value, self.additional_info
+        for key, prefix, data_type, data_shape, value_range, data_value, additional_info in self.key_iterator(
+            d, self.prefix, self.data_type, self.data_shape, self.value_range, self.data_value, self.additional_info
         ):
             d[key] = self.printer(
                 d[key],
                 prefix,
+                data_type,
                 data_shape,
                 value_range,
                 data_value,

tests/test_data_stats.py

@@ -23,6 +23,7 @@
 TEST_CASE_1 = [
     {
         "prefix": "test data",
+        "data_type": False,
         "data_shape": False,
         "value_range": False,
         "data_value": False,
@@ -36,58 +37,80 @@
 TEST_CASE_2 = [
     {
         "prefix": "test data",
-        "data_shape": True,
+        "data_type": True,
+        "data_shape": False,
         "value_range": False,
         "data_value": False,
         "additional_info": None,
         "logger_handler": None,
     },
     np.array([[0, 1], [1, 2]]),
-    "test data statistics:\nShape: (2, 2)",
+    "test data statistics:\nType: <class 'numpy.ndarray'>",
 ]
 
 TEST_CASE_3 = [
     {
         "prefix": "test data",
+        "data_type": True,
         "data_shape": True,
-        "value_range": True,
+        "value_range": False,
         "data_value": False,
         "additional_info": None,
         "logger_handler": None,
     },
     np.array([[0, 1], [1, 2]]),
-    "test data statistics:\nShape: (2, 2)\nValue range: (0, 2)",
+    "test data statistics:\nType: <class 'numpy.ndarray'>\nShape: (2, 2)",
 ]
 
 TEST_CASE_4 = [
     {
         "prefix": "test data",
+        "data_type": True,
         "data_shape": True,
         "value_range": True,
-        "data_value": True,
+        "data_value": False,
         "additional_info": None,
         "logger_handler": None,
     },
     np.array([[0, 1], [1, 2]]),
-    "test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]",
+    "test data statistics:\nType: <class 'numpy.ndarray'>\nShape: (2, 2)\nValue range: (0, 2)",
 ]
 
 TEST_CASE_5 = [
     {
         "prefix": "test data",
+        "data_type": True,
         "data_shape": True,
         "value_range": True,
         "data_value": True,
-        "additional_info": np.mean,
+        "additional_info": None,
         "logger_handler": None,
     },
     np.array([[0, 1], [1, 2]]),
-    "test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]\nAdditional info: 1.0",
+    "test data statistics:\nType: <class 'numpy.ndarray'>\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]",
 ]
 
 TEST_CASE_6 = [
     {
         "prefix": "test data",
+        "data_type": True,
+        "data_shape": True,
+        "value_range": True,
+        "data_value": True,
+        "additional_info": np.mean,
+        "logger_handler": None,
+    },
+    np.array([[0, 1], [1, 2]]),
+    (
+        "test data statistics:\nType: <class 'numpy.ndarray'>\nShape: (2, 2)\n"
+        "Value range: (0, 2)\nValue: [[0 1]\n [1 2]]\nAdditional info: 1.0"
+    ),
+]
+
+TEST_CASE_7 = [
+    {
+        "prefix": "test data",
+        "data_type": True,
         "data_shape": True,
         "value_range": True,
         "data_value": True,
@@ -96,32 +119,34 @@
     },
     torch.tensor([[0, 1], [1, 2]]),
     (
-        "test data statistics:\nShape: torch.Size([2, 2])\nValue range: (0, 2)\n"
+        "test data statistics:\nType: <class 'torch.Tensor'>\nShape: torch.Size([2, 2])\nValue range: (0, 2)\n"
         "Value: tensor([[0, 1],\n        [1, 2]])\nAdditional info: 1.0"
     ),
 ]
 
-TEST_CASE_7 = [
+TEST_CASE_8 = [
     np.array([[0, 1], [1, 2]]),
-    "test data statistics:\nShape: (2, 2)\nValue range: (0, 2)\nValue: [[0 1]\n [1 2]]\nAdditional info: 1.0\n",
+    "test data statistics:\nType: <class 'numpy.ndarray'>\nShape: (2, 2)\nValue range: (0, 2)\n"
+    "Value: [[0 1]\n [1 2]]\nAdditional info: 1.0\n",
 ]
 
 
 class TestDataStats(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
     def test_value(self, input_param, input_data, expected_print):
         transform = DataStats(**input_param)
         _ = transform(input_data)
         self.assertEqual(transform.output, expected_print)
 
-    @parameterized.expand([TEST_CASE_7])
+    @parameterized.expand([TEST_CASE_8])
     def test_file(self, input_data, expected_print):
         with tempfile.TemporaryDirectory() as tempdir:
             filename = os.path.join(tempdir, "test_data_stats.log")
             handler = logging.FileHandler(filename, mode="w")
             handler.setLevel(logging.INFO)
             input_param = {
                 "prefix": "test data",
+                "data_type": True,
                 "data_shape": True,
                 "value_range": True,
                 "data_value": True,