Support tensor visualization. 1.Tensor display in a table, it can support no...

Support tensor visualization. 1.Tensor display in a table, it can support no more than two dimensions tensor visualization; 2.Tensor histogram visualization for all step in cache.

mindinsight/backend/datavisual/train_visual_api.py

@@ -25,6 +25,7 @@ from mindinsight.conf import settings
 from mindinsight.datavisual.utils.tools import get_train_id
 from mindinsight.datavisual.utils.tools import if_nan_inf_to_none
 from mindinsight.datavisual.processors.histogram_processor import HistogramProcessor
+from mindinsight.datavisual.processors.tensor_processor import TensorProcessor
 from mindinsight.datavisual.processors.images_processor import ImageProcessor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 from mindinsight.datavisual.processors.graph_processor import GraphProcessor
@@ -173,6 +174,25 @@ def get_scalars():
     return jsonify({'scalars': scalars})
 
 
+@BLUEPRINT.route("/datavisual/tensors", methods=["GET"])
+def get_tensors():
+    """
+    Interface to obtain tensor data.
+
+    Returns:
+        Response, which contains a JSON object.
+    """
+    train_ids = request.args.getlist('train_id')
+    tags = request.args.getlist('tag')
+    step = request.args.get("step", default=None)
+    dims = request.args.get("dims", default=None)
+    detail = request.args.get("detail", default=None)
+
+    processor = TensorProcessor(DATA_MANAGER)
+    response = processor.get_tensors(train_ids, tags, step, dims, detail)
+    return jsonify(response)
+
+
 def init_module(app):
     """
     Init module entry.

mindinsight/conf/constants.py

@@ -57,3 +57,5 @@ MAX_IMAGE_STEP_SIZE_PER_TAG = 10
 MAX_SCALAR_STEP_SIZE_PER_TAG = 1000
 MAX_GRAPH_STEP_SIZE_PER_TAG = 1
 MAX_HISTOGRAM_STEP_SIZE_PER_TAG = 50
+MAX_TENSOR_STEP_SIZE_PER_TAG = 50
+MAX_TENSOR_RESPONSE_DATA_SIZE = 300000

mindinsight/datavisual/common/enums.py

@@ -38,6 +38,7 @@ class PluginNameEnum(BaseEnum):
     SCALAR = 'scalar'
     GRAPH = 'graph'
     HISTOGRAM = 'histogram'
+    TENSOR = 'tensor'
 
 
 @enum.unique

mindinsight/datavisual/common/exceptions.py

@@ -161,6 +161,33 @@ class HistogramNotExistError(MindInsightException):
                                                      http_code=400)
 
 
+class TensorNotExistError(MindInsightException):
+    """Unable to get tensor values based on a given condition."""
+    def __init__(self, error_detail):
+        error_msg = f'Tensor value is not exist. Detail: {error_detail}'
+        super(TensorNotExistError, self).__init__(DataVisualErrors.TENSOR_NOT_EXIST,
+                                                  error_msg,
+                                                  http_code=400)
+
+
+class StepTensorDataNotInCacheError(MindInsightException):
+    """Tensor data with specific step does not in cache."""
+    def __init__(self, error_detail):
+        error_msg = f'Tensor data not in cache. Detail: {error_detail}'
+        super(StepTensorDataNotInCacheError, self).__init__(DataVisualErrors.STEP_TENSOR_DATA_NOT_IN_CACHE,
+                                                            error_msg,
+                                                            http_code=400)
+
+
+class ResponseDataExceedMaxValueError(MindInsightException):
+    """Response data exceed max value based on a given condition."""
+    def __init__(self, error_detail):
+        error_msg = f'Response data exceed max value. Detail: {error_detail}'
+        super(ResponseDataExceedMaxValueError, self).__init__(DataVisualErrors.MAX_RESPONSE_DATA_EXCEEDED_ERROR,
+                                                              error_msg,
+                                                              http_code=400)
+
+
 class TrainJobDetailNotInCacheError(MindInsightException):
     """Detail info of given train job is not in cache."""
     def __init__(self, error_detail="no detail provided."):

mindinsight/datavisual/data_transform/events_data.py

@@ -41,7 +41,8 @@ CONFIG = {
             PluginNameEnum.SCALAR.value: settings.MAX_SCALAR_STEP_SIZE_PER_TAG,
             PluginNameEnum.IMAGE.value: settings.MAX_IMAGE_STEP_SIZE_PER_TAG,
             PluginNameEnum.GRAPH.value: settings.MAX_GRAPH_STEP_SIZE_PER_TAG,
-            PluginNameEnum.HISTOGRAM.value: settings.MAX_HISTOGRAM_STEP_SIZE_PER_TAG
+            PluginNameEnum.HISTOGRAM.value: settings.MAX_HISTOGRAM_STEP_SIZE_PER_TAG,
+            PluginNameEnum.TENSOR.value: settings.MAX_TENSOR_STEP_SIZE_PER_TAG
         }
 }
 

mindinsight/datavisual/data_transform/histogram.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Histogram data."""
+import math
+
+from mindinsight.utils.exceptions import ParamValueError
+from mindinsight.datavisual.utils.utils import calc_histogram_bins
+
+
+def mask_invalid_number(num):
+    """Mask invalid number to 0."""
+    if math.isnan(num) or math.isinf(num):
+        return type(num)(0)
+
+    return num
+
+
+class Bucket:
+    """
+    Bucket data class.
+
+    Args:
+        left (double): Left edge of the histogram bucket.
+        width (double): Width of the histogram bucket.
+        count (int): Count of numbers fallen in the histogram bucket.
+    """
+    def __init__(self, left, width, count):
+        self._left = left
+        self._width = width
+        self._count = count
+
+    @property
+    def left(self):
+        """Gets left edge of the histogram bucket."""
+        return self._left
+
+    @property
+    def count(self):
+        """Gets count of numbers fallen in the histogram bucket."""
+        return self._count
+
+    @property
+    def width(self):
+        """Gets width of the histogram bucket."""
+        return self._width
+
+    @property
+    def right(self):
+        """Gets right edge of the histogram bucket."""
+        return self._left + self._width
+
+    def as_tuple(self):
+        """Gets the bucket as tuple."""
+        return self._left, self._width, self._count
+
+    def __repr__(self):
+        """Returns repr(self)."""
+        return "Bucket(left={}, width={}, count={})".format(self._left, self._width, self._count)
+
+
+class Histogram:
+    """
+    Histogram data class.
+
+    Args:
+        buckets (tuple[Bucket])
+    """
+
+    # Max quantity of original buckets.
+    MAX_ORIGINAL_BUCKETS_COUNT = 90
+
+    def __init__(self, buckets, max_val, min_val, count):
+        self._visual_max = max_val
+        self._visual_min = min_val
+        self._count = count
+        self._original_buckets = buckets
+        # default bin number
+        self._visual_bins = calc_histogram_bins(count)
+        # Note that tuple is immutable, so sharing tuple is often safe.
+        self._re_sampled_buckets = ()
+
+    @property
+    def original_buckets_count(self):
+        """Gets original buckets quantity."""
+        return len(self._original_buckets)
+
+    def set_visual_range(self, max_val: float, min_val: float, bins: int) -> None:
+        """
+        Sets visual range for later re-sampling.
+
+        It's caller's duty to ensure input is valid.
+
+        Why we need visual range for histograms? Aligned buckets between steps can help users know about the trend of
+        tensors. Miss aligned buckets between steps might miss-lead users about the trend of a tensor. Because for
+        given tensor, if you have thinner buckets, count of every bucket will get lower, however, if you have
+        thicker buckets, count of every bucket will get higher.  When they are displayed together, user might think
+        the histogram with thicker buckets has more values. This is miss-leading. So we need to unify buckets across
+        steps. Visual range for histogram is a technology for unifying buckets.
+
+        Args:
+            max_val (float): Max value for visual histogram.
+            min_val (float): Min value for visual histogram.
+            bins (int): Bins number for visual histogram.
+        """
+        if max_val < min_val:
+            raise ParamValueError(
+                "Invalid input. max_val({}) is less or equal than min_val({}).".format(max_val, min_val))
+
+        if bins < 1:
+            raise ParamValueError("Invalid input bins({}). Must be greater than 0.".format(bins))
+
+        self._visual_max = max_val
+        self._visual_min = min_val
+        self._visual_bins = bins
+
+        # mark _re_sampled_buckets to empty
+        self._re_sampled_buckets = ()
+
+    def _calc_intersection_len(self, max1, min1, max2, min2):
+        """Calculates intersection length of [min1, max1] and [min2, max2]."""
+        if max1 < min1:
+            raise ParamValueError(
+                "Invalid input. max1({}) is less than min1({}).".format(max1, min1))
+
+        if max2 < min2:
+            raise ParamValueError(
+                "Invalid input. max2({}) is less than min2({}).".format(max2, min2))
+
+        if min1 <= min2:
+            if max1 <= min2:
+                # return value must be calculated by max1.__sub__
+                return max1 - max1
+            if max1 <= max2:
+                return max1 - min2
+            # max1 > max2
+            return max2 - min2
+
+        # min1 > min2
+        if max2 <= min1:
+            return max2 - max2
+        if max2 <= max1:
+            return max2 - min1
+        return max1 - min1
+
+    def _re_sample_buckets(self):
+        """Re-samples buckets according to visual_max, visual_min and visual_bins."""
+        if self._visual_max == self._visual_min:
+            # Adjust visual range if max equals min.
+            self._visual_max += 0.5
+            self._visual_min -= 0.5
+
+        width = (self._visual_max - self._visual_min) / self._visual_bins
+
+        if not self._count:
+            self._re_sampled_buckets = tuple(
+                Bucket(self._visual_min + width * i, width, 0)
+                for i in range(self._visual_bins))
+            return
+
+        re_sampled = []
+        original_pos = 0
+        original_bucket = self._original_buckets[original_pos]
+        for i in range(self._visual_bins):
+            cur_left = self._visual_min + width * i
+            cur_right = cur_left + width
+            cur_estimated_count = 0.0
+
+            # Skip no bucket range.
+            if cur_right <= original_bucket.left:
+                re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
+                continue
+
+            # Skip no intersect range.
+            while cur_left >= original_bucket.right:
+                original_pos += 1
+                if original_pos >= len(self._original_buckets):
+                    break
+                original_bucket = self._original_buckets[original_pos]
+
+            # entering with this condition: cur_right > original_bucket.left and cur_left < original_bucket.right
+            while True:
+                if original_pos >= len(self._original_buckets):
+                    break
+                original_bucket = self._original_buckets[original_pos]
+
+                intersection = self._calc_intersection_len(
+                    min1=cur_left, max1=cur_right,
+                    min2=original_bucket.left, max2=original_bucket.right)
+                if not original_bucket.width:
+                    estimated_count = original_bucket.count
+                else:
+                    estimated_count = (intersection / original_bucket.width) * original_bucket.count
+
+                cur_estimated_count += estimated_count
+                if cur_right > original_bucket.right:
+                    # Need to sample next original bucket to this visual bucket.
+                    original_pos += 1
+                else:
+                    # Current visual bucket has taken all intersect buckets into account.
+                    break
+
+            re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
+
+        self._re_sampled_buckets = tuple(re_sampled)
+
+    def buckets(self, convert_to_tuple=True):
+        """
+        Get visual buckets instead of original buckets.
+
+        Args:
+            convert_to_tuple (bool): Whether convert bucket object to tuple.
+
+        Returns:
+            tuple, contains buckets.
+        """
+        if not self._re_sampled_buckets:
+            self._re_sample_buckets()
+
+        if not convert_to_tuple:
+            return self._re_sampled_buckets
+
+        return tuple(bucket.as_tuple() for bucket in self._re_sampled_buckets)

mindinsight/datavisual/data_transform/histogram_container.py

@@ -13,90 +13,27 @@
 # limitations under the License.
 # ============================================================================
 """Histogram data container."""
-import math
-
+from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket, mask_invalid_number
 from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Summary
-from mindinsight.utils.exceptions import ParamValueError
-from mindinsight.datavisual.utils.utils import calc_histogram_bins
-
-
-def _mask_invalid_number(num):
-    """Mask invalid number to 0."""
-    if math.isnan(num) or math.isinf(num):
-        return type(num)(0)
-
-    return num
-
-
-class Bucket:
-    """
-    Bucket data class.
-
-    Args:
-        left (double): Left edge of the histogram bucket.
-        width (double): Width of the histogram bucket.
-        count (int): Count of numbers fallen in the histogram bucket.
-    """
-    def __init__(self, left, width, count):
-        self._left = left
-        self._width = width
-        self._count = count
-
-    @property
-    def left(self):
-        """Gets left edge of the histogram bucket."""
-        return self._left
-
-    @property
-    def count(self):
-        """Gets count of numbers fallen in the histogram bucket."""
-        return self._count
-
-    @property
-    def width(self):
-        """Gets width of the histogram bucket."""
-        return self._width
-
-    @property
-    def right(self):
-        """Gets right edge of the histogram bucket."""
-        return self._left + self._width
-
-    def as_tuple(self):
-        """Gets the bucket as tuple."""
-        return self._left, self._width, self._count
-
-    def __repr__(self):
-        """Returns repr(self)."""
-        return "Bucket(left={}, width={}, count={})".format(self._left, self._width, self._count)
 
 
 class HistogramContainer:
     """
-    Histogram data container.
+        Histogram data container.
 
-    Args:
-        histogram_message (Summary.Histogram): Histogram message in summary file.
+        Args:
+            histogram_message (Summary.Histogram): Histogram message in summary file.
     """
 
-    # Max quantity of original buckets.
-    MAX_ORIGINAL_BUCKETS_COUNT = 90
-
     def __init__(self, histogram_message: Summary.Histogram):
         self._msg = histogram_message
         original_buckets = [Bucket(bucket.left, bucket.width, bucket.count) for bucket in self._msg.buckets]
         # Ensure buckets are sorted from min to max.
         original_buckets.sort(key=lambda bucket: bucket.left)
-        self._original_buckets = tuple(original_buckets)
-        self._count = sum(bucket.count for bucket in self._original_buckets)
-        self._max = _mask_invalid_number(histogram_message.max)
-        self._min = _mask_invalid_number(histogram_message.min)
-        self._visual_max = self._max
-        self._visual_min = self._min
-        # default bin number
-        self._visual_bins = calc_histogram_bins(self._count)
-        # Note that tuple is immutable, so sharing tuple is often safe.
-        self._re_sampled_buckets = ()
+        self._count = sum(bucket.count for bucket in original_buckets)
+        self._max = mask_invalid_number(histogram_message.max)
+        self._min = mask_invalid_number(histogram_message.min)
+        self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
 
     @property
     def max(self):
@@ -114,148 +51,10 @@ class HistogramContainer:
         return self._count
 
     @property
-    def original_msg(self):
-        """Gets original proto message."""
-        return self._msg
-
-    @property
-    def original_buckets_count(self):
-        """Gets original buckets quantity."""
-        return len(self._original_buckets)
-
-    def set_visual_range(self, max_val: float, min_val: float, bins: int) -> None:
-        """
-        Sets visual range for later re-sampling.
-
-        It's caller's duty to ensure input is valid.
-
-        Why we need visual range for histograms? Aligned buckets between steps can help users know about the trend of
-        tensors. Miss aligned buckets between steps might miss-lead users about the trend of a tensor. Because for
-        given tensor, if you have thinner buckets, count of every bucket will get lower, however, if you have
-        thicker buckets, count of every bucket will get higher.  When they are displayed together, user might think
-        the histogram with thicker buckets has more values. This is miss-leading. So we need to unify buckets across
-        steps. Visual range for histogram is a technology for unifying buckets.
-
-        Args:
-            max_val (float): Max value for visual histogram.
-            min_val (float): Min value for visual histogram.
-            bins (int): Bins number for visual histogram.
-        """
-        if max_val < min_val:
-            raise ParamValueError(
-                "Invalid input. max_val({}) is less or equal than min_val({}).".format(max_val, min_val))
-
-        if bins < 1:
-            raise ParamValueError("Invalid input bins({}). Must be greater than 0.".format(bins))
-
-        self._visual_max = max_val
-        self._visual_min = min_val
-        self._visual_bins = bins
-
-        # mark _re_sampled_buckets to empty
-        self._re_sampled_buckets = ()
-
-    def _calc_intersection_len(self, max1, min1, max2, min2):
-        """Calculates intersection length of [min1, max1] and [min2, max2]."""
-        if max1 < min1:
-            raise ParamValueError(
-                "Invalid input. max1({}) is less than min1({}).".format(max1, min1))
-
-        if max2 < min2:
-            raise ParamValueError(
-                "Invalid input. max2({}) is less than min2({}).".format(max2, min2))
-
-        if min1 <= min2:
-            if max1 <= min2:
-                # return value must be calculated by max1.__sub__
-                return max1 - max1
-            if max1 <= max2:
-                return max1 - min2
-            # max1 > max2
-            return max2 - min2
-
-        # min1 > min2
-        if max2 <= min1:
-            return max2 - max2
-        if max2 <= max1:
-            return max2 - min1
-        return max1 - min1
-
-    def _re_sample_buckets(self):
-        """Re-samples buckets according to visual_max, visual_min and visual_bins."""
-        if self._visual_max == self._visual_min:
-            # Adjust visual range if max equals min.
-            self._visual_max += 0.5
-            self._visual_min -= 0.5
-
-        width = (self._visual_max - self._visual_min) / self._visual_bins
-
-        if not self.count:
-            self._re_sampled_buckets = tuple(
-                Bucket(self._visual_min + width * i, width, 0)
-                for i in range(self._visual_bins))
-            return
-
-        re_sampled = []
-        original_pos = 0
-        original_bucket = self._original_buckets[original_pos]
-        for i in range(self._visual_bins):
-            cur_left = self._visual_min + width * i
-            cur_right = cur_left + width
-            cur_estimated_count = 0.0
-
-            # Skip no bucket range.
-            if cur_right <= original_bucket.left:
-                re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
-                continue
-
-            # Skip no intersect range.
-            while cur_left >= original_bucket.right:
-                original_pos += 1
-                if original_pos >= len(self._original_buckets):
-                    break
-                original_bucket = self._original_buckets[original_pos]
-
-            # entering with this condition: cur_right > original_bucket.left and cur_left < original_bucket.right
-            while True:
-                if original_pos >= len(self._original_buckets):
-                    break
-                original_bucket = self._original_buckets[original_pos]
-
-                intersection = self._calc_intersection_len(
-                    min1=cur_left, max1=cur_right,
-                    min2=original_bucket.left, max2=original_bucket.right)
-                if not original_bucket.width:
-                    estimated_count = original_bucket.count
-                else:
-                    estimated_count = (intersection / original_bucket.width) * original_bucket.count
-
-                cur_estimated_count += estimated_count
-                if cur_right > original_bucket.right:
-                    # Need to sample next original bucket to this visual bucket.
-                    original_pos += 1
-                else:
-                    # Current visual bucket has taken all intersect buckets into account.
-                    break
-
-            re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
-
-        self._re_sampled_buckets = tuple(re_sampled)
-
-    def buckets(self, convert_to_tuple=True):
-        """
-        Get visual buckets instead of original buckets.
-
-        Args:
-            convert_to_tuple (bool): Whether convert bucket object to tuple.
-
-        Returns:
-            tuple, contains buckets.
-        """
-        if not self._re_sampled_buckets:
-            self._re_sample_buckets()
-
-        if not convert_to_tuple:
-            return self._re_sampled_buckets
+    def histogram(self):
+        """Gets histogram data"""
+        return self._histogram
 
-        return tuple(bucket.as_tuple() for bucket in self._re_sampled_buckets)
+    def buckets(self):
+        """Gets histogram buckets"""
+        return self._histogram.buckets()

mindinsight/datavisual/data_transform/ms_data_loader.py

@@ -36,7 +36,9 @@ from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summar
 from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import UnknownError
+from mindinsight.datavisual.data_transform.histogram import Histogram
 from mindinsight.datavisual.data_transform.histogram_container import HistogramContainer
+from mindinsight.datavisual.data_transform.tensor_container import TensorContainer
 
 HEADER_SIZE = 8
 CRC_STR_SIZE = 4
@@ -390,6 +392,7 @@ class _SummaryParser(_Parser):
             'scalar_value': PluginNameEnum.SCALAR,
             'image': PluginNameEnum.IMAGE,
             'histogram': PluginNameEnum.HISTOGRAM,
+            'tensor': PluginNameEnum.TENSOR
         }
 
         if event.HasField('summary'):
@@ -404,10 +407,12 @@ class _SummaryParser(_Parser):
                         tensor_event_value = HistogramContainer(tensor_event_value)
                         # Drop steps if original_buckets_count exceeds HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT
                         # to avoid time-consuming re-sample process.
-                        if tensor_event_value.original_buckets_count > HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT:
+                        if tensor_event_value.histogram.original_buckets_count > Histogram.MAX_ORIGINAL_BUCKETS_COUNT:
                             logger.info('original_buckets_count exceeds '
                                         'HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT')
                             continue
+                    elif plugin == 'tensor':
+                        tensor_event_value = TensorContainer(tensor_event_value)
 
                     tensor_event = TensorEvent(wall_time=event.wall_time,
                                                step=event.step,

mindinsight/datavisual/data_transform/reservoir.py

@@ -205,12 +205,12 @@ class HistogramReservoir(Reservoir):
             visual_range = _VisualRange()
             max_count = 0
             for sample in self._samples:
-                histogram = sample.value
-                if histogram.count == 0:
+                histogram_container = sample.value
+                if histogram_container.count == 0:
                     # ignore empty tensor
                     continue
-                max_count = max(histogram.count, max_count)
-                visual_range.update(histogram.max, histogram.min)
+                max_count = max(histogram_container.count, max_count)
+                visual_range.update(histogram_container.max, histogram_container.min)
 
             if visual_range.max == visual_range.min and not max_count:
                 logger.info("Max equals to min. Count is zero.")
@@ -225,7 +225,7 @@ class HistogramReservoir(Reservoir):
                 bins,
                 max_count)
             for sample in self._samples:
-                histogram = sample.value
+                histogram = sample.value.histogram
                 histogram.set_visual_range(visual_range.max, visual_range.min, bins)
 
             self._visual_range_up_to_date = True
@@ -245,6 +245,6 @@ class ReservoirFactory:
         Returns:
             Reservoir, reservoir instance for given plugin name.
         """
-        if plugin_name == PluginNameEnum.HISTOGRAM.value:
+        if plugin_name in (PluginNameEnum.HISTOGRAM.value, PluginNameEnum.TENSOR.value):
             return HistogramReservoir(size)
         return Reservoir(size)

mindinsight/datavisual/data_transform/tensor_container.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tensor data container."""
+import threading
+
+import numpy as np
+
+from mindinsight.datavisual.common.log import logger
+from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket
+from mindinsight.datavisual.utils.utils import calc_histogram_bins
+from mindinsight.utils.exceptions import ParamValueError
+
+F32_MIN, F32_MAX = np.finfo(np.float32).min, np.finfo(np.float32).max
+
+
+class Statistics:
+    """Statistics data class.
+
+    Args:
+        max_value (float): max value of tensor data.
+        min_value (float): min value of tensor data.
+        avg_value (float): avg value of tensor data.
+        count (int): total count of tensor data.
+        nan_count (int): count of NAN.
+        neg_inf_count (int): count of negative INF.
+        pos_inf_count (int): count of positive INF.
+    """
+
+    def __init__(self, max_value=0, min_value=0, avg_value=0,
+                 count=0, nan_count=0, neg_inf_count=0, pos_inf_count=0):
+        self._max = max_value
+        self._min = min_value
+        self._avg = avg_value
+        self._count = count
+        self._nan_count = nan_count
+        self._neg_inf_count = neg_inf_count
+        self._pos_inf_count = pos_inf_count
+
+    @property
+    def max(self):
+        """Get max value of tensor."""
+        return self._max
+
+    @property
+    def min(self):
+        """Get min value of tensor."""
+        return self._min
+
+    @property
+    def avg(self):
+        """Get avg value of tensor."""
+        return self._avg
+
+    @property
+    def count(self):
+        """Get total count of tensor."""
+        return self._count
+
+    @property
+    def nan_count(self):
+        """Get count of NAN."""
+        return self._nan_count
+
+    @property
+    def neg_inf_count(self):
+        """Get count of negative INF."""
+        return self._neg_inf_count
+
+    @property
+    def pos_inf_count(self):
+        """Get count of positive INF."""
+        return self._pos_inf_count
+
+
+def get_statistics_from_tensor(tensors):
+    """
+    Calculates statistics data of tensor.
+
+    Args:
+        tensors (numpy.ndarray): An numpy.ndarray of tensor data.
+
+    Returns:
+         an instance of Statistics.
+    """
+    ma_value = np.ma.masked_invalid(tensors)
+    total, valid = tensors.size, ma_value.count()
+    invalids = []
+    for isfn in np.isnan, np.isposinf, np.isneginf:
+        if total - valid > sum(invalids):
+            count = np.count_nonzero(isfn(tensors))
+            invalids.append(count)
+        else:
+            invalids.append(0)
+
+    nan_count, pos_inf_count, neg_inf_count = invalids
+    if not valid:
+        logger.warning('There are no valid values in the tensors(size=%d, shape=%s)', total, tensors.shape)
+        statistics = Statistics(max_value=0,
+                                min_value=0,
+                                avg_value=0,
+                                count=total,
+                                nan_count=nan_count,
+                                neg_inf_count=neg_inf_count,
+                                pos_inf_count=pos_inf_count)
+        return statistics
+
+    # BUG: max of a masked array with dtype np.float16 returns inf
+    # See numpy issue#15077
+    if issubclass(tensors.dtype.type, np.floating):
+        tensor_min = ma_value.min(fill_value=np.PINF)
+        tensor_max = ma_value.max(fill_value=np.NINF)
+        if tensor_min < F32_MIN or tensor_max > F32_MAX:
+            logger.warning('Values(%f, %f) are too large, you may encounter some undefined '
+                           'behaviours hereafter.', tensor_min, tensor_max)
+    else:
+        tensor_min = ma_value.min()
+        tensor_max = ma_value.max()
+    tensor_sum = ma_value.sum(dtype=np.float64)
+    statistics = Statistics(max_value=tensor_max,
+                            min_value=tensor_min,
+                            avg_value=tensor_sum / valid,
+                            count=total,
+                            nan_count=nan_count,
+                            neg_inf_count=neg_inf_count,
+                            pos_inf_count=pos_inf_count)
+    return statistics
+
+
+def _get_data_from_tensor(tensor):
+    """
+    Get data from tensor and convert to tuple.
+
+    Args:
+        tensor (TensorProto): Tensor proto data.
+
+    Returns:
+        tuple, the item of tensor value.
+    """
+    return tuple(tensor.float_data)
+
+
+def calc_original_buckets(np_value, stats):
+    """
+    Calculate buckets from tensor data.
+
+    Args:
+        np_value (numpy.ndarray): An numpy.ndarray of tensor data.
+        stats (Statistics): An instance of Statistics about tensor data.
+
+    Returns:
+        list, a list of bucket about tensor data.
+
+    Raises:
+        ParamValueError, If np_value or stats is None.
+    """
+    if np_value is None or stats is None:
+        raise ParamValueError("Invalid input. np_value or stats is None.")
+    valid_count = stats.count - stats.nan_count - stats.neg_inf_count - stats.pos_inf_count
+    if not valid_count:
+        return []
+
+    bins = calc_histogram_bins(valid_count)
+    first_edge, last_edge = stats.min, stats.max
+
+    if not first_edge < last_edge:
+        first_edge -= 0.5
+        last_edge += 0.5
+
+    bins = np.linspace(first_edge, last_edge, bins + 1, dtype=np_value.dtype)
+    hists, edges = np.histogram(np_value, bins=bins)
+
+    buckets = []
+    for hist, edge1, edge2 in zip(hists, edges, edges[1:]):
+        bucket = Bucket(edge1, edge2 - edge1, hist)
+        buckets.append(bucket)
+
+    return buckets
+
+
+class TensorContainer:
+    """
+    Tensor data container.
+
+    Args:
+        tensor_message (Summary.TensorProto): Tensor message in summary file.
+    """
+
+    def __init__(self, tensor_message):
+        self._lock = threading.Lock
+        self._msg = tensor_message
+        self._dims = tensor_message.dims
+        self._data_type = tensor_message.data_type
+        self._np_array = None
+        self._data = _get_data_from_tensor(tensor_message)
+        self._stats = get_statistics_from_tensor(self.get_or_calc_ndarray())
+        original_buckets = calc_original_buckets(self.get_or_calc_ndarray(), self._stats)
+        self._count = sum(bucket.count for bucket in original_buckets)
+        self._max = self._stats.max
+        self._min = self._stats.min
+        self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
+
+    @property
+    def dims(self):
+        """Get dims of tensor."""
+        return self._dims
+
+    @property
+    def data_type(self):
+        """Get data type of tensor."""
+        return self._data_type
+
+    @property
+    def max(self):
+        """Get max value of tensor."""
+        return self._max
+
+    @property
+    def min(self):
+        """Get min value of tensor."""
+        return self._min
+
+    @property
+    def stats(self):
+        """Get statistics data of tensor."""
+        return self._stats
+
+    @property
+    def count(self):
+        """Get count value of tensor."""
+        return self._count
+
+    @property
+    def histogram(self):
+        """Get histogram data."""
+        return self._histogram
+
+    def buckets(self):
+        """Get histogram buckets."""
+        return self._histogram.buckets()
+
+    def get_or_calc_ndarray(self):
+        """Get or calculate ndarray."""
+        with self._lock():
+            if self._np_array is None:
+                self._convert_to_numpy_array()
+            return self._np_array
+
+    def _convert_to_numpy_array(self):
+        """Convert a list data to numpy array."""
+        try:
+            ndarray = np.array(self._data).reshape(self._dims)
+        except ValueError as ex:
+            logger.error("Reshape array fail, detail: %r", str(ex))
+            return
+
+        self._msg = None
+        self._np_array = ndarray

mindinsight/datavisual/processors/tensor_processor.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tensor Processor APIs."""
+from urllib.parse import unquote
+
+import numpy as np
+
+from mindinsight.datavisual.utils.tools import to_int
+from mindinsight.utils.exceptions import ParamValueError, UrlDecodeError
+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.data_transform.tensor_container import TensorContainer, get_statistics_from_tensor
+from mindinsight.datavisual.processors.base_processor import BaseProcessor
+from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
+
+
+def convert_array_from_str(dims, limit=0):
+    """
+    Convert string of dims data to array.
+
+    Args:
+        dims (str): Specify dims of tensor.
+        limit (int): The max flexible dimension count, default value is 0 which means that there is no limitation.
+
+    Returns:
+        list, a string like this: "[0, 0, :, :]" will convert to this value: [0, 0, None, None].
+
+    Raises:
+        ParamValueError, If flexible dimensions exceed limit value.
+    """
+    dims = dims.replace('[', '') \
+               .replace(']', '')
+    dims_list = []
+    count = 0
+    for dim in dims.split(','):
+        dim = dim.strip()
+        if dim == ':':
+            dims_list.append(None)
+            count += 1
+        else:
+            dims_list.append(to_int(dim, "dim"))
+    if limit and count > limit:
+        raise ParamValueError("Flexible dimensions cannot exceed limit value: {}, size: {}"
+                              .format(limit, count))
+    return dims_list
+
+
+def get_specific_dims_data(ndarray, dims, tensor_dims):
+    """
+    Get specific dims data.
+
+    Args:
+        ndarray (numpy.ndarray): An ndarray of numpy.
+        dims (list): A list of specific dims.
+        tensor_dims (list): A list of tensor dims.
+
+    Returns:
+        numpy.ndarray, an ndarray of specific dims tensor data.
+
+    Raises:
+        ParamValueError, If the length of param dims is not equal to the length of tensor dims or
+                         the index of param dims out of range.
+    """
+    if len(dims) != len(tensor_dims):
+        raise ParamValueError("The length of param dims: {}, is not equal to the "
+                              "length of tensor dims: {}.".format(len(dims), len(tensor_dims)))
+    indices = []
+    for k, d in enumerate(dims):
+        if d is not None:
+            if d >= tensor_dims[k]:
+                raise ParamValueError("The index: {} of param dims out of range: {}.".format(d, tensor_dims[k]))
+            indices.append(d)
+        else:
+            indices.append(slice(0, tensor_dims[k]))
+    return ndarray[tuple(indices)]
+
+
+def get_statistics_dict(tensor_container, tensors):
+    """
+    Get statistics dict according to tensor data.
+
+    Args:
+        tensor_container (TensorContainer): An instance of TensorContainer.
+        tensors (numpy.ndarray or number): An numpy.ndarray or number of tensor data.
+
+    Returns:
+        dict, a dict including 'max', 'min', 'avg', 'count', 'nan_count', 'neg_inf_count', 'pos_inf_count'.
+    """
+    if tensors is None:
+        statistics = {
+            "max": tensor_container.stats.max,
+            "min": tensor_container.stats.min,
+            "avg": tensor_container.stats.avg,
+            "count": tensor_container.stats.count,
+            "nan_count": tensor_container.stats.nan_count,
+            "neg_inf_count": tensor_container.stats.neg_inf_count,
+            "pos_inf_count": tensor_container.stats.pos_inf_count
+        }
+        return statistics
+
+    if not isinstance(tensors, np.ndarray):
+        tensors = np.array(tensors)
+
+    stats = get_statistics_from_tensor(tensors)
+    statistics = {
+        "max": stats.max,
+        "min": stats.min,
+        "avg": stats.avg,
+        "count": stats.count,
+        "nan_count": stats.nan_count,
+        "neg_inf_count": stats.neg_inf_count,
+        "pos_inf_count": stats.pos_inf_count
+    }
+    return statistics
+
+
+class TensorProcessor(BaseProcessor):
+    """Tensor Processor."""
+    def get_tensors(self, train_ids, tags, step, dims, detail):
+        """
+        Get tensor data for given train_ids, tags, step, dims and detail.
+
+        Args:
+            train_ids (list): Specify list of train job ID.
+            tags (list): Specify list of tag.
+            step (int): Specify step of tag, it's necessary when detail is equal to 'data'.
+            dims (str): Specify dims of step, it's necessary when detail is equal to 'data'.
+            detail (str): Specify which data to query, available values: 'stats', 'histogram' and 'data'.
+
+        Returns:
+            dict, a dict including the `tensors`.
+
+        Raises:
+            UrlDecodeError, If unquote train id error with strict mode.
+        """
+        Validation.check_param_empty(train_id=train_ids, tag=tags)
+        for index, train_id in enumerate(train_ids):
+            try:
+                train_id = unquote(train_id, errors='strict')
+            except UnicodeDecodeError:
+                raise UrlDecodeError('Unquote train id error with strict mode')
+            else:
+                train_ids[index] = train_id
+
+        tensors = []
+        for train_id in train_ids:
+            tensors += self._get_train_tensors(train_id, tags, step, dims, detail)
+
+        return {"tensors": tensors}
+
+    def _get_train_tensors(self, train_id, tags, step, dims, detail):
+        """
+        Get tensor data for given train_id, tags, step, dims and detail.
+
+        Args:
+            train_id (str): Specify list of train job ID.
+            tags (list): Specify list of tag.
+            step (int): Specify step of tensor, it's necessary when detail is set to 'data'.
+            dims (str): Specify dims of tensor, it's necessary when detail is set to 'data'.
+            detail (str): Specify which data to query, available values: 'stats', 'histogram' and 'data'.
+
+        Returns:
+            list[dict], a list of dictionaries containing the `train_id`, `tag`, `values`.
+
+        Raises:
+            TensorNotExistError, If tensor with specific train_id and tag is not exist in cache.
+            ParamValueError, If the value of detail is not within available values:
+                            'stats', 'histogram' and 'data'.
+        """
+
+        tensors_response = []
+        for tag in tags:
+            try:
+                tensors = self._data_manager.list_tensors(train_id, tag)
+            except ParamValueError as err:
+                raise TensorNotExistError(err.message)
+
+            if tensors and not isinstance(tensors[0].value, TensorContainer):
+                raise TensorNotExistError("there is no tensor data in this tag: {}".format(tag))
+
+            if detail is None or detail == 'stats':
+                values = self._get_tensors_summary(detail, tensors)
+            elif detail == 'data':
+                Validation.check_param_empty(step=step, dims=dims)
+                step = to_int(step, "step")
+                values = self._get_tensors_data(step, dims, tensors)
+            elif detail == 'histogram':
+                values = self._get_tensors_histogram(tensors)
+            else:
+                raise ParamValueError('Can not support this value: {} of detail.'.format(detail))
+
+            tensor = {
+                "train_id": train_id,
+                "tag": tag,
+                "values": values
+            }
+            tensors_response.append(tensor)
+
+        return tensors_response
+
+    def _get_tensors_summary(self, detail, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor summary.
+
+        Args:
+            detail (str): Specify which data to query, detail value is None or 'stats' at this method.
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32"
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            } This dict is being set when detail is equal to stats.
+                        }
+                    }
+        """
+        values = []
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            value_dict = {
+                "dims": tuple(value.dims),
+                "data_type": anf_ir_pb2.DataType.Name(value.data_type)
+            }
+            if detail and detail == 'stats':
+                stats = get_statistics_dict(value, None)
+                value_dict.update({"statistics": stats})
+
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": value_dict
+            })
+
+        return values
+
+    def _get_tensors_data(self, step, dims, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor dims data.
+
+        Args:
+            step (int): Specify step of tensor.
+            dims (str): Specify dims of tensor.
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32",
+                            "data": [[0.1]]
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            }
+                        }
+                    }
+
+        Raises:
+            ResponseDataExceedMaxValueError, If the size of response data exceed max value.
+            StepTensorDataNotInCacheError, If query step is not in cache.
+        """
+        values = []
+        step_in_cache = False
+        dims = convert_array_from_str(dims, limit=2)
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            if step != tensor.step:
+                continue
+            step_in_cache = True
+            ndarray = value.get_or_calc_ndarray()
+            res_data = get_specific_dims_data(ndarray, dims, list(value.dims))
+            flatten_data = res_data.flatten().tolist()
+            if len(flatten_data) > MAX_TENSOR_RESPONSE_DATA_SIZE:
+                raise ResponseDataExceedMaxValueError("the size of response data: {} exceed max value: {}."
+                                                      .format(len(flatten_data), MAX_TENSOR_RESPONSE_DATA_SIZE))
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": {
+                    "dims": tuple(value.dims),
+                    "data_type": anf_ir_pb2.DataType.Name(value.data_type),
+                    "data": res_data.tolist(),
+                    "statistics": get_statistics_dict(value, flatten_data)
+                }
+            })
+            break
+        if not step_in_cache:
+            raise StepTensorDataNotInCacheError("this step: {} data may has been dropped.".format(step))
+
+        return values
+
+    def _get_tensors_histogram(self, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor histogram data.
+
+        Args:
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32",
+                            "histogram_buckets": [[0.1, 0.2, 3]]
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            }
+                        }
+                    }
+        """
+        values = []
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            buckets = value.buckets()
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": {
+                    "dims": tuple(value.dims),
+                    "data_type": anf_ir_pb2.DataType.Name(value.data_type),
+                    "histogram_buckets": buckets,
+                    "statistics": get_statistics_dict(value, None)
+                }
+            })
+
+        return values

mindinsight/utils/constant.py

@@ -71,6 +71,9 @@ class DataVisualErrors(Enum):
     HISTOGRAM_NOT_EXIST = 15
     TRAIN_JOB_DETAIL_NOT_IN_CACHE = 16
     QUERY_STRING_CONTAINS_NULL_BYTE = 17
+    TENSOR_NOT_EXIST = 18
+    MAX_RESPONSE_DATA_EXCEEDED_ERROR = 19
+    STEP_TENSOR_DATA_NOT_IN_CACHE = 20
 
 
 class ScriptConverterErrors(Enum):

tests/ut/datavisual/data_transform/test_histogram_container.py

@@ -29,9 +29,9 @@ class TestHistogram:
         mocked_bucket.width = 1
         mocked_bucket.count = 1
         mocked_input.buckets = [mocked_bucket]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=1, min_val=0, bins=1)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=1, min_val=0, bins=1)
+        buckets = histogram_container.buckets()
         assert buckets == ((0.0, 1.0, 1),)
 
     def test_re_sample_buckets_split_original(self):
@@ -42,9 +42,9 @@ class TestHistogram:
         mocked_bucket.width = 1
         mocked_bucket.count = 1
         mocked_input.buckets = [mocked_bucket]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=1, min_val=0, bins=3)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=1, min_val=0, bins=3)
+        buckets = histogram_container.buckets()
         assert buckets == ((0.0, 0.3333333333333333, 1), (0.3333333333333333, 0.3333333333333333, 1),
                            (0.6666666666666666, 0.3333333333333333, 1))
 
@@ -60,9 +60,9 @@ class TestHistogram:
         mocked_bucket2.width = 1
         mocked_bucket2.count = 2
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=3, min_val=-1, bins=4)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=3, min_val=-1, bins=4)
+        buckets = histogram_container.buckets()
         assert buckets == ((-1.0, 1.0, 0), (0.0, 1.0, 1), (1.0, 1.0, 2), (2.0, 1.0, 0))
 
     def test_re_sample_buckets_merge_bucket(self):
@@ -77,9 +77,9 @@ class TestHistogram:
         mocked_bucket2.width = 1
         mocked_bucket2.count = 10
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=3, min_val=-1, bins=5)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=3, min_val=-1, bins=5)
+        buckets = histogram_container.buckets()
         assert buckets == (
             (-1.0, 0.8, 0), (-0.19999999999999996, 0.8, 1), (0.6000000000000001, 0.8, 5), (1.4000000000000004, 0.8, 6),
             (2.2, 0.8, 0))
@@ -96,9 +96,9 @@ class TestHistogram:
         mocked_bucket2.width = 0
         mocked_bucket2.count = 2
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=2, min_val=0, bins=3)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=2, min_val=0, bins=3)
+        buckets = histogram_container.buckets()
         assert buckets == (
             (0.0, 0.6666666666666666, 1),
             (0.6666666666666666, 0.6666666666666666, 3),

tests/ut/datavisual/processors/test_train_task_manager.py

@@ -69,7 +69,7 @@ class TestTrainTaskManager:
     def load_data(self):
         """Load data."""
         log_operation = LogOperations()
-        self._plugins_id_map = {'image': [], 'scalar': [], 'graph': [], 'histogram': []}
+        self._plugins_id_map = {'image': [], 'scalar': [], 'graph': [], 'histogram': [], 'tensor': []}
         self._events_names = []
         self._train_id_list = []
 

tests/utils/log_generators/tensor_log_generator.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Log generator for tensor data."""
+import time
+
+from operator import mul
+from functools import reduce
+import numpy as np
+from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
+from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
+
+from .log_generator import LogGenerator
+
+
+class TensorLogGenerator(LogGenerator):
+    """
+    Log generator for tensor data.
+
+    This is a log generator writing tensor data. User can use it to generate fake
+    summary logs about tensor.
+    """
+
+    def generate_event(self, values):
+        """
+        Method for generating tensor event.
+
+        Args:
+            values (dict): A dict contains:
+                {
+                    wall_time (float): Timestamp.
+                    step (int): Train step.
+                    value (float): Tensor value.
+                    tag (str): Tag name.
+                }
+
+       Returns:
+            summary_pb2.Event.
+
+        """
+        tensor_event = summary_pb2.Event()
+        tensor_event.wall_time = values.get('wall_time')
+        tensor_event.step = values.get('step')
+
+        value = tensor_event.summary.value.add()
+        value.tag = values.get('tag')
+        tensor = values.get('value')
+
+        value.tensor.dims[:] = tensor.get('dims')
+        value.tensor.data_type = tensor.get('data_type')
+        value.tensor.float_data[:] = tensor.get('float_data')
+        print(tensor.get('float_data'))
+
+        return tensor_event
+
+    def generate_log(self, file_path, steps_list, tag_name):
+        """
+        Generate log for external calls.
+
+        Args:
+            file_path (str): Path to write logs.
+            steps_list (list): A list consists of step.
+            tag_name (str): Tag name.
+
+        Returns:
+            list[dict], generated tensor metadata.
+            list, generated tensors.
+
+        """
+        tensor_metadata = []
+        tensor_values = dict()
+        for step in steps_list:
+            tensor = dict()
+
+            wall_time = time.time()
+            tensor.update({'wall_time': wall_time})
+            tensor.update({'step': step})
+            tensor.update({'tag': tag_name})
+            dims = list(np.random.randint(1, 10, 4))
+            mul_value = reduce(mul, dims)
+            tensor.update({'value': {
+                "dims": dims,
+                "data_type": anf_ir_pb2.DataType.DT_FLOAT32,
+                "float_data": np.random.randn(mul_value)
+            }})
+            tensor_metadata.append(tensor)
+            tensor_values.update({step: tensor})
+
+            self._write_log_one_step(file_path, tensor)
+
+        return tensor_metadata, tensor_values
+
+
+if __name__ == "__main__":
+    tensor_log_generator = TensorLogGenerator()
+    test_file_name = '%s.%s.%s' % ('tensor', 'summary', str(time.time()))
+    test_steps = [1, 3, 5]
+    test_tag = "test_tensor_tag_name"
+    tensor_log_generator.generate_log(test_file_name, test_steps, test_tag)

tests/utils/log_operations.py

@@ -25,12 +25,14 @@ from .log_generators.graph_log_generator import GraphLogGenerator
 from .log_generators.images_log_generator import ImagesLogGenerator
 from .log_generators.scalars_log_generator import ScalarsLogGenerator
 from .log_generators.histogram_log_generator import HistogramLogGenerator
+from .log_generators.tensor_log_generator import TensorLogGenerator
 
 log_generators = {
     PluginNameEnum.GRAPH.value: GraphLogGenerator(),
     PluginNameEnum.IMAGE.value: ImagesLogGenerator(),
     PluginNameEnum.SCALAR.value: ScalarsLogGenerator(),
-    PluginNameEnum.HISTOGRAM.value: HistogramLogGenerator()
+    PluginNameEnum.HISTOGRAM.value: HistogramLogGenerator(),
+    PluginNameEnum.TENSOR.value: TensorLogGenerator()
 }