!1329 Put the parameter validation of the Summary operator into the python layer

Merge pull request !1329 from ougongchang/master

mindspore/ccsrc/ir/anf_extends.cc

@@ -57,9 +57,6 @@ std::string CNode::fullname_with_scope() {
   if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary) ||
       IsApply(prim::kPrimHistogramSummary)) {
     std::string tag = GetValue<std::string>(GetValueNode(input(1)));
-    if (tag == "") {
-      MS_LOG(EXCEPTION) << "The tag name is null, should be valid string";
-    }
     std::string name;
     if (IsApply(prim::kPrimScalarSummary)) {
       name = tag + "[:Scalar]";

mindspore/ccsrc/operator/prim_debug.cc

@@ -21,64 +21,5 @@
 #include "utils/symbolic.h"
 
 namespace mindspore {
-namespace abstract {
-AbstractBasePtr InferImplScalarSummary(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                       const AbstractBasePtrList &args_spec_list) {
-  // Inputs: a scalar and a tensor or scalar.
-  const std::string op_name = primitive->name();
-  CheckArgsSize(op_name, args_spec_list, 2);
-
-  // check the tag
-  AbstractScalarPtr descriptions = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
-
-  // check the value: scalar or shape = (1,)
-  auto scalar_value = dyn_cast<AbstractScalar>(args_spec_list[1]);
-  if (scalar_value == nullptr) {
-    auto tensor_value = dyn_cast<AbstractTensor>(args_spec_list[1]);
-    if (tensor_value == nullptr) {
-      MS_LOG(EXCEPTION) << "Input must be scalar or shape(1,)";
-    }
-  } else {
-    auto item_v = scalar_value->BuildValue();
-    if (item_v->isa<StringImm>()) {
-      auto value = item_v->cast<StringImmPtr>()->value();
-      if (value.empty()) {
-        MS_LOG(EXCEPTION) << "Input summary value can't be null";
-      }
-    }
-  }
-
-  // Reomve the force check to support batch set summary use 'for' loop
-  auto item_v = descriptions->BuildValue();
-  if (!item_v->isa<StringImm>()) {
-    MS_EXCEPTION(TypeError) << "Summary first parameter should be string";
-  }
-
-  return std::make_shared<AbstractScalar>(kAnyValue, kBool);
-}
-
-AbstractBasePtr InferImplTensorSummary(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                       const AbstractBasePtrList &args_spec_list) {
-  // Inputs: a scalar(tag) and a tensor(value)
-  const std::string op_name = primitive->name();
-  CheckArgsSize(op_name, args_spec_list, 2);
-
-  // check the tag
-  auto descriptions = CheckArg<AbstractScalar>(op_name, args_spec_list, 0);
-  auto tensor_value = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
-
-  int tensor_rank = SizeToInt(tensor_value->shape()->shape().size());
-  if (tensor_rank == 0) {
-    MS_LOG(EXCEPTION) << op_name << " summary evaluator second arg should be an tensor, but got a scalar, rank is 0";
-  }
-
-  // Reomve the force check to support batch set summary use 'for' loop
-  auto item_v = descriptions->BuildValue();
-  if (!item_v->isa<StringImm>()) {
-    MS_EXCEPTION(TypeError) << "Summary first parameter should be string";
-  }
-
-  return std::make_shared<AbstractScalar>(kAnyValue, std::make_shared<Bool>());
-}
-}  // namespace abstract
+namespace abstract {}  // namespace abstract
 }  // namespace mindspore

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -128,11 +128,6 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimDepend, {InferImplDepend, true}},
     {prim::kPrimBroadcastGradientArgs, {InferImplBroadcastGradientArgs, false}},
     {prim::kPrimControlDepend, {InferImplControlDepend, true}},
-    // Debug
-    {prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
-    {prim::kPrimImageSummary, {InferImplTensorSummary, true}},
-    {prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
-    {prim::kPrimHistogramSummary, {InferImplTensorSummary, true}},
   };
   return prim_eval_implement_map;
 }

mindspore/ccsrc/pipeline/static_analysis/prim.h

@@ -326,11 +326,6 @@ AbstractBasePtr InferImplBroadcastGradientArgs(const AnalysisEnginePtr &, const
                                                const AbstractBasePtrList &args_spec_list);
 AbstractBasePtr InferImplControlDepend(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                        const AbstractBasePtrList &args_spec_list);
-
-AbstractBasePtr InferImplScalarSummary(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                       const AbstractBasePtrList &args_spec_list);
-AbstractBasePtr InferImplTensorSummary(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
-                                       const AbstractBasePtrList &args_spec_list);
 }  // namespace abstract
 }  // namespace mindspore
 

mindspore/ops/operations/debug_ops.py

@@ -16,10 +16,22 @@
 """debug_ops"""
 from ..._checkparam import Validator as validator
 from ...common import dtype as mstype
-from ..primitive import Primitive, prim_attr_register, PrimitiveWithInfer
+from ..primitive import prim_attr_register, PrimitiveWithInfer
 
 
-class ScalarSummary(Primitive):
+def _check_summary_param(name, value, class_name):
+    """Check the name and value is valid for summary."""
+    n_type = name['dtype']
+    n_value = name['value']
+    validator.check_value_type('name', n_type, [type(mstype.string)], class_name)
+    if not n_value:
+        raise ValueError(f"For 'name' the value should by valid string in {class_name}, but got {n_value}.")
+
+    v_type = value['dtype']
+    validator.check_value_type('value', v_type, [type(mstype.tensor)], class_name)
+
+
+class ScalarSummary(PrimitiveWithInfer):
     """
     Output scalar to protocol buffer through scalar summary operator.
 
@@ -45,11 +57,19 @@ class ScalarSummary(Primitive):
     def __init__(self):
         """init"""
 
-    def __call__(self, *args, **kwargs):
-        pass
+    def __infer__(self, name, value):
+        _check_summary_param(name, value, self.__class__.__name__)
 
+        v_shape = value['shape']
+        # In the summary, the value whose shape is [1] is also considered as a scalar.
+        if v_shape and v_shape != [1]:
+            raise ValueError(f"For 'value' the type should be scalar, "
+                             f"shape should be [] or [1] in {self.__class__.__name__}, but got {v_shape}.")
 
-class ImageSummary(Primitive):
+        return value
+
+
+class ImageSummary(PrimitiveWithInfer):
     """
     Output image tensor to protocol buffer through image summary operator.
 
@@ -73,11 +93,20 @@ class ImageSummary(Primitive):
     def __init__(self):
         """init"""
 
-    def __call__(self, *args, **kwargs):
-        pass
+    def __infer__(self, name, value):
+        _check_summary_param(name, value, self.__class__.__name__)
+
+        # The shape dim of image should be 4.
+        v_shape = value['shape']
+        image_dim = 4
+        if len(v_shape) != image_dim:
+            raise ValueError(f"For 'value' the dim should be {image_dim} in {self.__class__.__name__},"
+                             f" but got {len(v_shape)}.")
 
+        return value
 
-class TensorSummary(Primitive):
+
+class TensorSummary(PrimitiveWithInfer):
     """
     Output tensor to protocol buffer through tensor summary operator.
 
@@ -103,11 +132,19 @@ class TensorSummary(Primitive):
     def __init__(self):
         """init"""
 
-    def __call__(self, *args, **kwargs):
-        pass
+    def __infer__(self, name, value):
+        _check_summary_param(name, value, self.__class__.__name__)
+
+        v_shape = value['shape']
+        # In the summary, the value whose shape is [] is not considered as a tensor.
+        if not v_shape:
+            raise ValueError(f"For 'value' the type should be tensor in {self.__class__.__name__}, "
+                             f"shape should not be [].")
+
+        return value
 
 
-class HistogramSummary(Primitive):
+class HistogramSummary(PrimitiveWithInfer):
     """
     Output tensor to protocol buffer through histogram summary operator.
 
@@ -133,6 +170,17 @@ class HistogramSummary(Primitive):
     def __init__(self):
         """init"""
 
+    def __infer__(self, name, value):
+        _check_summary_param(name, value, self.__class__.__name__)
+
+        v_shape = value['shape']
+        # In the summary, the histogram value should be a tensor whose shape is not [].
+        if not v_shape:
+            raise ValueError(f"For 'value' the type should be tensor in {self.__class__.__name__}, "
+                             f"shape should not be [].")
+
+        return value
+
 
 class InsertGradientOf(PrimitiveWithInfer):
     """

tests/st/summary/test_gpu_summary.py

@@ -12,11 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""Summary gpu st."""
 import os
 import random
+import tempfile
 import shutil
-import pytest
+
 import numpy as np
+import pytest
 
 import mindspore.context as context
 import mindspore.nn as nn
@@ -26,36 +29,9 @@ from mindspore.train.summary.summary_record import SummaryRecord
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
-CUR_DIR = os.getcwd()
-SUMMARY_DIR_ME = CUR_DIR + "/test_me_summary_event_file/"
-SUMMARY_DIR_ME_TEMP = CUR_DIR + "/test_me_temp_summary_event_file/"
-
-
-def clean_environment_file(srcDir):
-    if os.path.exists(srcDir):
-        ls = os.listdir(srcDir)
-        for line in ls:
-            filePath = os.path.join(srcDir, line)
-            os.remove(filePath)
-        os.removedirs(srcDir)
-
-
-def save_summary_events_file(srcDir, desDir):
-    if not os.path.exists(desDir):
-        print("-- create desDir")
-        os.makedirs(desDir)
-
-    ls = os.listdir(srcDir)
-    for line in ls:
-        filePath = os.path.join(srcDir, line)
-        if os.path.isfile(filePath):
-            print("-- move events file : {}".format(filePath))
-            shutil.copy(filePath, desDir)
-        os.remove(filePath)
-    os.removedirs(srcDir)
-
 
 class SummaryNet(nn.Cell):
+    """Summary net."""
     def __init__(self, tag_tuple=None, scalar=1):
         super(SummaryNet, self).__init__()
         self.summary_s = P.ScalarSummary()
@@ -66,8 +42,9 @@ class SummaryNet(nn.Cell):
         self.tag_tuple = tag_tuple
         self.scalar = scalar
 
-    def construct(self, x, y):
-        self.summary_i("image", x)
+    def construct(self, x, y, image):
+        """Run summary net."""
+        self.summary_i("image", image)
         self.summary_s("x1", x)
         z = self.add(x, y)
         self.summary_t("z1", z)
@@ -75,32 +52,38 @@ class SummaryNet(nn.Cell):
         return z
 
 
-def train_summary_record_scalar_for_1(test_writer, steps):
+def train_summary_record(test_writer, steps):
+    """Train and record summary."""
     net = SummaryNet()
     out_me_dict = {}
     for i in range(0, steps):
         x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
         y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
-        out_put = net(x, y)
+        image = Tensor(np.array([[[[1.2]]]]).astype(np.float32))
+        out_put = net(x, y, image)
         test_writer.record(i)
-        print("-----------------output: %s-------------\n", out_put.asnumpy())
         out_me_dict[i] = out_put.asnumpy()
     return out_me_dict
 
 
-def me_scalar_summary(steps):
-    with SummaryRecord(SUMMARY_DIR_ME_TEMP) as test_writer:
-        out_me_dict = train_summary_record_scalar_for_1(test_writer, steps)
+class TestGpuSummary:
+    """Test Gpu summary."""
+    summary_dir = tempfile.mkdtemp(suffix='_gpu_summary')
 
-        return out_me_dict
+    def setup_method(self):
+        """Run before method."""
+        if not os.path.exists(self.summary_dir):
+            os.mkdir(self.summary_dir)
 
+    def teardown_emthod(self):
+        """Run after method."""
+        if os.path.exists(self.summary_dir):
+            shutil.rmtree(self.summary_dir)
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_scalarsummary_scalar1_step10_summaryrecord1():
-    clean_environment_file(SUMMARY_DIR_ME_TEMP)
-    output_dict = me_scalar_summary(10)
-    print("test_scalarsummary_scalar1_step10_summaryrecord1 \n", output_dict)
-    save_summary_events_file(SUMMARY_DIR_ME_TEMP, SUMMARY_DIR_ME)
-    clean_environment_file(SUMMARY_DIR_ME)
+    @pytest.mark.level0
+    @pytest.mark.platform_x86_gpu_training
+    @pytest.mark.env_onecard
+    def test_summary_step10_summaryrecord1(self):
+        """Test record 10 step summary."""
+        with SummaryRecord(self.summary_dir) as test_writer:
+            train_summary_record(test_writer, steps=10)

tests/ut/python/ops/test_nn_ops.py

@@ -492,7 +492,7 @@ test_cases = [
     }),
     ('ScalarSummary', {
         'block': ScalarSummaryNet(),
-        'desc_inputs': [2.2],
+        'desc_inputs': [Tensor(2.2)],
     }),
     ('L2Normalize', {
         'block': L2NormalizeNet(),

tests/ut/python/pynative_mode/test_insert_grad_of.py

@@ -112,7 +112,7 @@ def test_InsertGradientOf_3():
     def f(x, y):
         return C.grad_all(debug_test)(x, y)
 
-    print("debug_gradient:", f(1, 2))
+    print("debug_gradient:", f(Tensor(1.0), Tensor(2.0)))
 
 
 def test_print_shape_type():

tests/ut/python/train/summary/test_summary_ops_params_valid_check.py

@@ -12,260 +12,139 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""
-@File  : test_summary.py
-@Author:
-@Date  : 2019-08-5
-@Desc  : test summary function of ops params valid check
-"""
-import logging
-import numpy as np
+"""Test summary function of ops params valid check."""
 import os
+import tempfile
+import shutil
+from enum import Enum
+
+import numpy as np
 import pytest
-import random
 
 import mindspore.nn as nn
 from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
 from mindspore.train.summary.summary_record import SummaryRecord
 
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-
-class SummaryDemoTag(nn.Cell):
-    """ SummaryDemoTag definition """
-
-    def __init__(self, tag1, tag2, tag3):
-        super(SummaryDemoTag, self).__init__()
-        self.s = P.ScalarSummary()
-        self.histogram_summary = P.HistogramSummary()
-        self.add = P.TensorAdd()
-        self.tag1 = tag1
-        self.tag2 = tag2
-        self.tag3 = tag3
-
-    def construct(self, x, y):
-        self.s(self.tag1, x)
-        z = self.add(x, y)
-        self.s(self.tag2, z)
-        self.s(self.tag3, y)
-        self.histogram_summary(self.tag1, x)
-        return z
 
+class SummaryEnum(Enum):
+    """Summary enum."""
+    IMAGE = P.ImageSummary.__name__
+    SCALAR = P.ScalarSummary.__name__
+    TENSOR = P.TensorSummary.__name__
+    HISTOGRAM = P.HistogramSummary.__name__
 
-class SummaryDemoTagForSet(nn.Cell):
-    """ SummaryDemoTagForSet definition """
 
-    def __init__(self, tag_tuple):
-        super(SummaryDemoTagForSet, self).__init__()
-        self.s = P.ScalarSummary()
-        self.histogram_summary = P.HistogramSummary()
+class SummaryNet(nn.Cell):
+    """Summary net definition."""
+    def __init__(self, summary_type, tag, data):
+        super(SummaryNet, self).__init__()
+        self.tag = tag
+        self.data = data
+        self.summary_fn = getattr(P, summary_type)()
+        self.one = Tensor(np.array([1]).astype(np.float32))
         self.add = P.TensorAdd()
-        self.tag_tuple = tag_tuple
-
-    def construct(self, x, y):
-        z = self.add(x, y)
-        for tag in self.tag_tuple:
-            self.s(tag, x)
-            self.histogram_summary(tag, x)
-        return z
-
-
-class SummaryDemoValue(nn.Cell):
-    """ SummaryDemoValue definition """
 
-    def __init__(self, value):
-        super(SummaryDemoValue, self).__init__()
-        self.s = P.ScalarSummary()
-        self.add = P.TensorAdd()
-        self.v = value
-
-    def construct(self, x, y):
-        self.s("x", self.v)
-        z = self.add(x, y)
-        self.s("z", self.v)
-        self.s("y", self.v)
-        return z
-
-
-class SummaryDemoValueForSet(nn.Cell):
-    """ SummaryDemoValueForSet definition """
-
-    def __init__(self, value, tag_tuple):
-        super(SummaryDemoValueForSet, self).__init__()
-        self.s = P.ScalarSummary()
-        self.add = P.TensorAdd()
-        self.tag_tuple = tag_tuple
-        self.v = value
-
-    def construct(self, x, y):
-        z = self.add(x, y)
-        for tag in self.tag_tuple:
-            self.s(tag, self.v)
-        return z
-
-
-class HistogramSummaryNet(nn.Cell):
-    "HistogramSummaryNet definition"
-
-    def __init__(self, value):
-        self.histogram_summary = P.HistogramSummary()
-        self.add = P.TensorAdd()
-        self.value = value
+    def construct(self):
+        self.summary_fn(self.tag, self.data)
+        return self.add(self.one, self.one)
 
-    def construct(self, tensors1, tensor2):
-        self.histogram_summary("value", self.value)
-        return self.add(tensors1, tensor2)
 
+class TestSummaryOps:
+    """Test summary operators."""
+    summary_dir = ''
 
-def run_case(net):
-    """ run_case """
-    # step 0: create the thread
-    with SummaryRecord(SUMMARY_DIR) as test_writer:
-        # step 1: create the network for summary
-        x = Tensor(np.array([1.1]).astype(np.float32))
-        y = Tensor(np.array([1.2]).astype(np.float32))
+    @classmethod
+    def run_case(cls, net):
+        """ run_case """
         net.set_train()
-
-        # step 2: create the Event
-        steps = 100
-        for i in range(1, steps):
-            x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
-            y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
-            net(x, y)
-            test_writer.record(i)
-
-
-# Test 1: use the repeat tag
-def test_summary_use_repeat_tag():
-    log.debug("begin test_summary_use_repeat_tag")
-    net = SummaryDemoTag("x", "x", "x")
-    try:
-        run_case(net)
-    except:
-        assert False
-    else:
-        assert True
-    log.debug("finished test_summary_use_repeat_tag")
-
-
-# Test 2: repeat tag use for set summary
-def test_summary_use_repeat_tag_for_set():
-    log.debug("begin test_summary_use_repeat_tag_for_set")
-    net = SummaryDemoTagForSet(("x", "x", "x"))
-    try:
-        run_case(net)
-    except:
-        assert False
-    else:
-        assert True
-    log.debug("finished test_summary_use_repeat_tag_for_set")
-
-
-# Test3: test with invalid tag(None, bool, "", int)
-def test_summary_use_invalid_tag_None():
-    log.debug("begin test_summary_use_invalid_tag_None")
-    net = SummaryDemoTag(None, None, None)
-    try:
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_summary_use_invalid_tag_None")
-
-
-# Test4: test with invalid tag(None, bool, "", int)
-def test_summary_use_invalid_tag_Bool():
-    log.debug("begin test_summary_use_invalid_tag_Bool")
-    net = SummaryDemoTag(True, True, True)
-    with pytest.raises(TypeError):
-        run_case(net)
-    log.debug("finished test_summary_use_invalid_tag_Bool")
-
-
-# Test5: test with invalid tag(None, bool, "", int)
-def test_summary_use_invalid_tag_null():
-    log.debug("begin test_summary_use_invalid_tag_null")
-    net = SummaryDemoTag("", "", "")
-    run_case(net)
-    log.debug("finished test_summary_use_invalid_tag_null")
-
-
-# Test6: test with invalid tag(None, bool, "", int)
-def test_summary_use_invalid_tag_Int():
-    log.debug("begin test_summary_use_invalid_tag_Int")
-    net = SummaryDemoTag(1, 2, 3)
-    with pytest.raises(TypeError):
-        run_case(net)
-    log.debug("finished test_summary_use_invalid_tag_Int")
-
-
-# Test7: test with invalid value(None, "")
-def test_scalar_summary_use_invalid_value_None():
-    log.debug("begin test_scalar_summary_use_invalid_tag_Int")
-    net = SummaryDemoValue(None)
-    try:
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_scalar_summary_use_invalid_tag_Int")
-
-
-# Test8: test with invalid value(None, "")
-def test_scalar_summary_use_invalid_value_None_ForSet():
-    log.debug("begin test_scalar_summary_use_invalid_value_None_ForSet")
-    try:
-        net = SummaryDemoValueForSet(None, ("x1", "x2"))
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_scalar_summary_use_invalid_value_None_ForSet")
-
-
-# Test9: test with invalid value(None, "")
-def test_scalar_summary_use_invalid_value_null():
-    log.debug("begin test_scalar_summary_use_invalid_value_null")
-    try:
-        net = SummaryDemoValue("")
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_scalar_summary_use_invalid_value_null")
-
-
-def test_histogram_summary_use_valid_value():
-    """Test histogram summary with valid value"""
-    log.debug("Begin test_histogram_summary_use_valid_value")
-    try:
-        net = HistogramSummaryNet(Tensor(np.array([1, 2, 3])))
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("Finished test_histogram_summary_use_valid_value")
-
-
-def test_histogram_summary_use_scalar_value():
-    """Test histogram summary use scalar value"""
-    log.debug("Begin test_histogram_summary_use_scalar_value")
-    try:
-        scalar = Tensor(1)
-        net = HistogramSummaryNet(scalar)
-        run_case(net)
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("Finished test_histogram_summary_use_scalar_value")
+        steps = 10
+        with SummaryRecord(cls.summary_dir) as test_writer:
+            for i in range(1, steps):
+                net()
+                test_writer.record(i)
+
+    @classmethod
+    def setup_class(cls):
+        """Run before class."""
+        if not os.path.exists(cls.summary_dir):
+            cls.summary_dir = tempfile.mkdtemp(suffix='_summary')
+
+    @classmethod
+    def teardown_class(cls):
+        """Run after class."""
+        if os.path.exists(cls.summary_dir):
+            shutil.rmtree(cls.summary_dir)
+
+    @pytest.mark.parametrize(
+        "summary_type, value",
+        [
+            (SummaryEnum.SCALAR.value, Tensor(1)),
+            (SummaryEnum.SCALAR.value, Tensor(np.array([1]))),
+            (SummaryEnum.IMAGE.value, Tensor(np.array([[[[1], [2], [3], [4]]]]))),
+            (SummaryEnum.TENSOR.value, Tensor(np.array([[1], [2], [3], [4]]))),
+            (SummaryEnum.HISTOGRAM.value, Tensor(np.array([[1], [2], [3], [4]]))),
+        ])
+    def test_summary_success(self, summary_type, value):
+        """Test summary success with valid tag and valid data."""
+        net = SummaryNet(summary_type, tag='tag', data=value)
+        TestSummaryOps.run_case(net)
+
+    @pytest.mark.parametrize(
+        "summary_type",
+        [
+            SummaryEnum.SCALAR.value,
+            SummaryEnum.IMAGE.value,
+            SummaryEnum.HISTOGRAM.value,
+            SummaryEnum.TENSOR.value
+        ])
+    def test_summary_tag_is_none(self, summary_type):
+        """Test summary tag is None, all summary operator validation rules are consistent."""
+        net = SummaryNet(summary_type, tag=None, data=Tensor(0))
+        with pytest.raises(TypeError):
+            TestSummaryOps.run_case(net)
+
+
+    @pytest.mark.parametrize(
+        "summary_type",
+        [
+            SummaryEnum.SCALAR.value,
+            SummaryEnum.IMAGE.value,
+            SummaryEnum.HISTOGRAM.value,
+            SummaryEnum.TENSOR.value
+        ])
+    def test_summary_tag_is_empty_string(self, summary_type):
+        """Test summary tag is a empty string, all summary operator validation rules are consistent."""
+        net = SummaryNet(summary_type, tag='', data=Tensor(0))
+        with pytest.raises(ValueError):
+            TestSummaryOps.run_case(net)
+
+    @pytest.mark.parametrize("tag", [123, True, Tensor(0)])
+    def test_summary_tag_is_not_string(self, tag):
+        """Test summary tag is not a string, all summary operator validation rules are consistent."""
+        # All summary operator validation rules are consistent, so we only test scalar summary.
+        net = SummaryNet(SummaryEnum.SCALAR.value, tag=tag, data=Tensor(0))
+        with pytest.raises(TypeError):
+            TestSummaryOps.run_case(net)
+
+    @pytest.mark.parametrize("value", [123, True, 'data'])
+    def test_summary_value_type_invalid(self, value):
+        """Test the type of summary value is invalid, all summary operator validation rules are consistent."""
+        # All summary operator validation rules are consistent, so we only test scalar summary.
+        net = SummaryNet(SummaryEnum.SCALAR.value, tag='tag', data=value)
+        with pytest.raises(TypeError):
+            TestSummaryOps.run_case(net)
+
+    @pytest.mark.parametrize(
+        "summary_type, value",
+        [
+            (SummaryEnum.IMAGE.value, Tensor(np.array([1, 2]))),
+            (SummaryEnum.SCALAR.value, Tensor(np.array([1, 2]))),
+            (SummaryEnum.TENSOR.value, Tensor(0)),
+            (SummaryEnum.HISTOGRAM.value, Tensor(0))
+        ])
+    def test_value_shape_invalid(self, summary_type, value):
+        """Test invalid shape of every summary operators."""
+        net = SummaryNet(summary_type, tag='tag', data=value)
+        with pytest.raises(ValueError):
+            TestSummaryOps.run_case(net)