!1030 Fix that ui cannot get the error when tensor is too large

From: @jiang-shuqiang
Reviewed-by: @wenkai_dist,@lilongfei15
Signed-off-by: @lilongfei15

mindinsight/datavisual/common/exceptions.py

@@ -204,3 +204,12 @@ class TrainJobDetailNotInCacheError(MindInsightException):
         super().__init__(DataVisualErrors.TRAIN_JOB_DETAIL_NOT_IN_CACHE,
                          error_msg,
                          http_code=400)
+
+
+class TensorTooLargeError(MindInsightException):
+    """The given tensor is too large to shown on UI."""
+    def __init__(self, error_detail):
+        error_msg = f'Tensor is too large to show on UI. Detail: {error_detail}'
+        super(TensorTooLargeError, self).__init__(DataVisualErrors.TENSOR_TOO_LARGE,
+                                                  error_msg,
+                                                  http_code=400)

mindinsight/datavisual/data_transform/ms_data_loader.py

@@ -482,10 +482,9 @@ class _SummaryParser(_Parser):
 
         elif plugin == PluginNameEnum.TENSOR.value:
             tensor_event_value = TensorContainer(tensor_event_value)
-            if tensor_event_value.size > MAX_TENSOR_COUNT:
+            if tensor_event_value.error_code is not None:
                 logger.warning('tag: %s/tensor, dims: %s, tensor count: %d exceeds %d and drop it.',
                                value.tag, tensor_event_value.dims, tensor_event_value.size, MAX_TENSOR_COUNT)
-                return None
 
         elif plugin == PluginNameEnum.IMAGE.value:
             tensor_event_value = ImageContainer(tensor_event_value)

mindinsight/datavisual/data_transform/tensor_container.py

@@ -17,10 +17,12 @@ import numpy as np
 
 from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket
 from mindinsight.datavisual.utils.utils import calc_histogram_bins
+from mindinsight.datavisual.common.exceptions import TensorTooLargeError
 from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.utils.tensor import TensorUtils
 
 MAX_TENSOR_COUNT = 10000000
+TENSOR_TOO_LARGE_ERROR = TensorTooLargeError("").error_code
 
 
 def calc_original_buckets(np_value, stats):
@@ -74,6 +76,10 @@ class TensorContainer:
         self._dims = tuple(tensor_message.dims)
         self._data_type = tensor_message.data_type
         self._np_array = self.get_ndarray(tensor_message.float_data)
+        self._error_code = None
+        if self._np_array.size > MAX_TENSOR_COUNT:
+            self._error_code = TENSOR_TOO_LARGE_ERROR
+            self._np_array = np.array([])
         self._stats = TensorUtils.get_statistics_from_tensor(self._np_array)
         original_buckets = calc_original_buckets(self._np_array, self._stats)
         self._count = sum(bucket.count for bucket in original_buckets)
@@ -81,11 +87,17 @@ class TensorContainer:
         self._min = self._stats.min
         self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
 
+
     @property
     def size(self):
         """Get size of tensor."""
         return self._np_array.size
 
+    @property
+    def error_code(self):
+        """Get size of tensor."""
+        return self._error_code
+
     @property
     def dims(self):
         """Get dims of tensor."""
@@ -128,6 +140,8 @@ class TensorContainer:
 
     def buckets(self):
         """Get histogram buckets."""
+        if self._histogram is None:
+            return None
         return self._histogram.buckets()
 
     def get_ndarray(self, tensor):

mindinsight/datavisual/processors/tensor_processor.py

@@ -23,7 +23,7 @@ from mindinsight.utils.tensor import TensorUtils, MAX_DIMENSIONS_FOR_TENSOR
 from mindinsight.conf.constants import MAX_TENSOR_RESPONSE_DATA_SIZE
 from mindinsight.datavisual.common.validation import Validation
 from mindinsight.datavisual.common.exceptions import StepTensorDataNotInCacheError, TensorNotExistError
-from mindinsight.datavisual.common.exceptions import ResponseDataExceedMaxValueError
+from mindinsight.datavisual.common.exceptions import ResponseDataExceedMaxValueError, TensorTooLargeError
 from mindinsight.datavisual.data_transform.tensor_container import TensorContainer
 from mindinsight.datavisual.processors.base_processor import BaseProcessor
 from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
@@ -153,7 +153,9 @@ class TensorProcessor(BaseProcessor):
                 "data_type": anf_ir_pb2.DataType.Name(value.data_type)
             }
             if detail and detail == 'stats':
-                stats = TensorUtils.get_statistics_dict(stats=value.stats, overall_stats=value.stats)
+                stats = None
+                if value.error_code is None:
+                    stats = TensorUtils.get_statistics_dict(stats=value.stats, overall_stats=value.stats)
                 value_dict.update({"statistics": stats})
 
             values.append({
@@ -206,6 +208,8 @@ class TensorProcessor(BaseProcessor):
             if step != tensor.step:
                 continue
             step_in_cache = True
+            if value.error_code is not None:
+                raise TensorTooLargeError("Step: {}".format(tensor.step))
             res_data = TensorUtils.get_specific_dims_data(value.ndarray, dims)
             flatten_data = res_data.flatten().tolist()
             if len(flatten_data) > MAX_TENSOR_RESPONSE_DATA_SIZE:
@@ -285,6 +289,8 @@ class TensorProcessor(BaseProcessor):
         for tensor in tensors:
             # This value is an instance of TensorContainer
             value = tensor.value
+            if value.error_code is not None:
+                raise TensorTooLargeError("Step: {}".format(tensor.step))
             buckets = value.buckets()
             values.append({
                 "wall_time": tensor.wall_time,

mindinsight/utils/constant.py

@@ -83,6 +83,7 @@ class DataVisualErrors(Enum):
     MAX_RESPONSE_DATA_EXCEEDED_ERROR = 19
     STEP_TENSOR_DATA_NOT_IN_CACHE = 20
     CRC_LENGTH_FAILED = 21
+    TENSOR_TOO_LARGE = 22
 
 
 class ScriptConverterErrors(Enum):

mindinsight/utils/tensor.py

@@ -216,6 +216,8 @@ class TensorUtils:
             ParamValueError, If the length of param dims is not equal to the length of tensor dims.
             IndexError, If the param dims and tensor shape is unmatched.
         """
+        if ndarray.size == 0:
+            return ndarray
         if len(ndarray.shape) != len(dims):
             raise ParamValueError("Invalid dims. The length of param dims and tensor shape should be the same.")
         try: