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add Histogram summary operator

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clean clang format errors and cpplint errors

add some test cases for histogram summary operator
pull/340/head
ougongchang 6 years ago
parent 40f0a4a4f4
commit 0ed6d9178e
22 changed files with 181 additions and 98 deletions
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11
mindspore/ccsrc/ir/anf.cc
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@ -103,7 +103,8 @@ std::string CNode::fullname_with_scope() {
return fullname_with_scope_;
}
if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary)) {
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";
@ -111,10 +112,12 @@ std::string CNode::fullname_with_scope() {
std::string name;
if (IsApply(prim::kPrimScalarSummary)) {
name = tag + "[:Scalar]";
} else if (IsApply(prim::kPrimTensorSummary)) {
name = tag + "[:Tensor]";
} else {
} else if (IsApply(prim::kPrimImageSummary)) {
name = tag + "[:Image]";
} else if (IsApply(prim::kPrimHistogramSummary)) {
name = tag + "[:Histogram]";
} else {
name = tag + "[:Tensor]";
}
fullname_with_scope_ = name;
} else {

1
mindspore/ccsrc/operator/ops.cc
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@ -235,6 +235,7 @@ const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDa
const PrimitivePtr kPrimScalarSummary = std::make_shared<Primitive>("ScalarSummary");
const PrimitivePtr kPrimImageSummary = std::make_shared<Primitive>("ImageSummary");
const PrimitivePtr kPrimTensorSummary = std::make_shared<Primitive>("TensorSummary");
const PrimitivePtr kPrimHistogramSummary = std::make_shared<Primitive>("HistogramSummary");
ValuePtr GetPythonOps(const std::string& op_name, const std::string& module_name) {
py::object obj = parse::python_adapter::GetPyFn(module_name, op_name);

1
mindspore/ccsrc/operator/ops.h
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@ -225,6 +225,7 @@ extern const PrimitivePtr kPrimStateSetItem;
extern const PrimitivePtr kPrimScalarSummary;
extern const PrimitivePtr kPrimImageSummary;
extern const PrimitivePtr kPrimTensorSummary;
extern const PrimitivePtr kPrimHistogramSummary;
extern const PrimitivePtr kPrimBroadcastGradientArgs;
extern const PrimitivePtr kPrimControlDepend;
extern const PrimitivePtr kPrimIs_;

2
mindspore/ccsrc/operator/prim_debug.cc
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@ -69,7 +69,7 @@ AbstractBasePtr InferImplTensorSummary(const AnalysisEnginePtr &, const Primitiv
int tensor_rank = SizeToInt(tensor_value->shape()->shape().size());
if (tensor_rank == 0) {
MS_LOG(EXCEPTION) << "Tensor/Image Summary evaluator second arg should be an tensor, but got a scalar";
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

27
mindspore/ccsrc/optimizer/irpass/branch_culling.cc
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@ -51,25 +51,14 @@ bool InConvertWhiteList(const AnfNodePtr &node, size_t index) {
// node because it is attribute or ge specific reason.
// Example : when convert CNode(kPrimReduceSum, x, axis), node of index 2 in CNode->inputs is axis which should not be
// converted to switch guarded.
std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list({{prim::kPrimApplyMomentum, {1, 2}},
{prim::kPrimMomentum, {2, 3}},
{prim::kPrimStateSetItem, {1}},
{prim::kPrimEnvGetItem, {1}},
{prim::kPrimEnvSetItem, {1}},
{prim::kPrimReduceSum, {2}},
{prim::kPrimReduceMean, {2}},
{prim::kPrimReduceAll, {2}},
{prim::kPrimCast, {2}},
{prim::kPrimTranspose, {2}},
{prim::kPrimOneHot, {2}},
{prim::kPrimGatherV2, {3}},
{prim::kPrimReshape, {2}},
{prim::kPrimAssign, {1}},
{prim::kPrimAssignAdd, {1}},
{prim::kPrimAssignSub, {1}},
{prim::kPrimTensorSummary, {1}},
{prim::kPrimImageSummary, {1}},
{prim::kPrimScalarSummary, {1}}});
std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list(
{{prim::kPrimApplyMomentum, {1, 2}}, {prim::kPrimMomentum, {2, 3}}, {prim::kPrimStateSetItem, {1}},
{prim::kPrimEnvGetItem, {1}}, {prim::kPrimEnvSetItem, {1}}, {prim::kPrimReduceSum, {2}},
{prim::kPrimReduceMean, {2}}, {prim::kPrimReduceAll, {2}}, {prim::kPrimCast, {2}},
{prim::kPrimTranspose, {2}}, {prim::kPrimOneHot, {2}}, {prim::kPrimGatherV2, {3}},
{prim::kPrimReshape, {2}}, {prim::kPrimAssign, {1}}, {prim::kPrimAssignAdd, {1}},
{prim::kPrimAssignSub, {1}}, {prim::kPrimTensorSummary, {1}}, {prim::kPrimImageSummary, {1}},
{prim::kPrimScalarSummary, {1}}, {prim::kPrimHistogramSummary, {1}}});
for (auto &item : white_list) {
auto matched = std::any_of(item.second.begin(), item.second.end(), [&item, &node, &index](size_t idx) {
return IsPrimitiveCNode(node, item.first) && idx == index;

1
mindspore/ccsrc/parallel/node_check.cc
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@ -66,6 +66,7 @@ const std::set<std::string> BLACK_LIST = {TUPLE_GETITEM,
SCALARSUMMARY,
IMAGESUMMARY,
TENSORSUMMARY,
HISTOGRAMSUMMARY,
COL2IMV1,
RESOLVE,
BROADCASTGRADIENTARGS,

1
mindspore/ccsrc/parallel/ops_info/ops_utils.h
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@ -246,6 +246,7 @@ constexpr char STATESETITEM[] = "state_setitem";
constexpr char SCALARSUMMARY[] = "ScalarSummary";
constexpr char IMAGESUMMARY[] = "ImageSummary";
constexpr char TENSORSUMMARY[] = "TensorSummary";
constexpr char HISTOGRAMSUMMARY[] = "HistogramSummary";
constexpr char BROADCASTGRADIENTARGS[] = "BroadcastGradientArgs";
constexpr char INVERTPERMUTATION[] = "InvertPermutation";
constexpr char CONTROLDEPEND[] = "ControlDepend";

1
mindspore/ccsrc/pipeline/static_analysis/prim.cc
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@ -131,6 +131,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
{prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
{prim::kPrimImageSummary, {InferImplTensorSummary, true}},
{prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
{prim::kPrimHistogramSummary, {InferImplTensorSummary, true}},
};
return prim_eval_implement_map;
}

3
mindspore/ccsrc/session/anf_runtime_algorithm.cc
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@ -714,7 +714,8 @@ bool AnfRuntimeAlgorithm::IsRealKernel(const AnfNodePtr &node) {
}
auto input = cnode->inputs()[0];
bool is_virtual_node = IsPrimitive(input, prim::kPrimImageSummary) || IsPrimitive(input, prim::kPrimScalarSummary) ||
IsPrimitive(input, prim::kPrimTensorSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
IsPrimitive(input, prim::kPrimTensorSummary) ||
IsPrimitive(input, prim::kPrimHistogramSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
IsPrimitive(input, prim::kPrimStateSetItem) || IsPrimitive(input, prim::kPrimDepend) ||
IsPrimitive(input, prim::kPrimTupleGetItem) || IsPrimitive(input, prim::kPrimControlDepend) ||
IsPrimitive(input, prim::kPrimReturn);

4
mindspore/ccsrc/session/session_basic.cc
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@ -45,7 +45,7 @@ void GetSummaryNodes(const KernelGraph *graph, std::unordered_map<std::string, s
for (auto &n : apply_list) {
MS_EXCEPTION_IF_NULL(n);
if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
int index = 0;
auto cnode = n->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
@ -83,7 +83,7 @@ bool ExistSummaryNode(const KernelGraph *graph) {
auto all_nodes = DeepLinkedGraphSearch(ret);
for (auto &n : all_nodes) {
if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
return true;
}
}

1
mindspore/ccsrc/transform/convert.cc
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@ -353,6 +353,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{prim::kPrimScalarSummary->name(), ADPT_DESC(Summary)},
{prim::kPrimImageSummary->name(), ADPT_DESC(Summary)},
{prim::kPrimTensorSummary->name(), ADPT_DESC(Summary)},
{prim::kPrimHistogramSummary->name(), ADPT_DESC(Summary)},
{prim::kPrimTensorAdd->name(),
std::make_shared<OpAdapterDesc>(std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})),
std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})))},

2
mindspore/ccsrc/utils/callbacks_ge.cc
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@ -131,7 +131,7 @@ static TensorPtr GetMeTensorForSummary(const std::string& name, const std::share
auto shape = std::vector<int>({ONE_SHAPE});
return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape);
}
if (tname == "[:Tensor]") {
if (tname == "[:Tensor]" || tname == "[:Histogram]") {
MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor";
// process the tensor summary
// Now we can't get the real shape, so we keep same shape with GE

9
mindspore/ops/_grad/grad_debug_ops.py
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@ -49,6 +49,15 @@ def get_bprop_image_summary(self):
return bprop
@bprop_getters.register(P.HistogramSummary)
def get_bprop_histogram_summary(self):
"""Generate bprop for HistogramSummary"""
def bprop(tag, x, out, dout):
return tag, zeros_like(x)
return bprop
@bprop_getters.register(P.InsertGradientOf)
def get_bprop_insert_gradient_of(self):
"""Generate bprop for InsertGradientOf"""

3
mindspore/ops/operations/__init__.py
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@ -34,7 +34,7 @@ from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast
_MirrorOperator, ReduceOp, _VirtualDataset,
_VirtualDiv, _GetTensorSlice)
from .debug_ops import (ImageSummary, InsertGradientOf, ScalarSummary,
TensorSummary, Print)
TensorSummary, HistogramSummary, Print)
from .control_ops import ControlDepend, GeSwitch, Merge
from .inner_ops import ScalarCast
from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
@ -148,6 +148,7 @@ __all__ = [
'ScalarSummary',
'ImageSummary',
'TensorSummary',
'HistogramSummary',
"Print",
'InsertGradientOf',
'InvertPermutation',

27
mindspore/ops/operations/debug_ops.py
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@ -98,6 +98,33 @@ class TensorSummary(Primitive):
"""init"""
class HistogramSummary(Primitive):
"""
Output tensor to protocol buffer through histogram summary operator.
Inputs:
- **name** (str) - The name of the input variable.
- **value** (Tensor) - The value of tensor, and the rank of tensor should be greater than 0.
Examples:
>>> class SummaryDemo(nn.Cell):
>>> def __init__(self,):
>>> super(SummaryDemo, self).__init__()
>>> self.summary = P.HistogramSummary()
>>> self.add = P.TensorAdd()
>>>
>>> def construct(self, x, y):
>>> x = self.add(x, y)
>>> name = "x"
>>> self.summary(name, x)
>>> return x
"""
@prim_attr_register
def __init__(self):
"""init"""
class InsertGradientOf(PrimitiveWithInfer):
"""
Attach callback to graph node that will be invoked on the node's gradient.

57
tests/st/summary/test_gpu_summary.py
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@ -24,17 +24,6 @@ from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.train.summary.summary_record import SummaryRecord
'''
This testcase is used for save summary data only. You need install MindData first and uncomment the commented
packages to analyse summary data.
Using "minddata start --datalog='./test_me_summary_event_file/' --host=0.0.0.0" to make data visible.
'''
# from minddata.datavisual.data_transform.data_manager import DataManager
# from minddata.datavisual.visual.train_visual.train_task_manager import TrainTaskManager
# from minddata.datavisual.visual.train_visual.scalars_processor import ScalarsProcessor
# from minddata.datavisual.common.enums import PluginNameEnum
# from minddata.datavisual.common.enums import DataManagerStatus
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
@ -43,6 +32,7 @@ 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)
@ -50,6 +40,8 @@ def clean_environment_file(srcDir):
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")
@ -64,12 +56,14 @@ def save_summary_events_file(srcDir, desDir):
os.remove(filePath)
os.removedirs(srcDir)
class SummaryNet(nn.Cell):
def __init__(self, tag_tuple=None, scalar=1):
super(SummaryNet, self).__init__()
self.summary_s = P.ScalarSummary()
self.summary_i = P.ImageSummary()
self.summary_t = P.TensorSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.tag_tuple = tag_tuple
self.scalar = scalar
@ -79,8 +73,10 @@ class SummaryNet(nn.Cell):
self.summary_s("x1", x)
z = self.add(x, y)
self.summary_t("z1", z)
self.histogram_summary("histogram", z)
return z
def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
net = SummaryNet()
out_me_dict = {}
@ -93,6 +89,7 @@ def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
out_me_dict[i] = out_put.asnumpy()
return out_me_dict
def me_scalar_summary(steps, tag=None, value=None):
test_writer = SummaryRecord(SUMMARY_DIR_ME_TEMP)
@ -104,44 +101,6 @@ def me_scalar_summary(steps, tag=None, value=None):
test_writer.close()
return out_me_dict
def print_scalar_data():
print("============start print_scalar_data\n")
data_manager = DataManager()
data_manager.start_load_data(path=SUMMARY_DIR_ME)
while data_manager.get_status() != DataManagerStatus.DONE:
time.sleep(0.1)
task_manager = TrainTaskManager(data_manager)
train_jobs = task_manager.get_all_train_tasks(PluginNameEnum.scalar)
print(train_jobs)
"""
train_jobs
['train_jobs': {
'id': '12-123',
'name': 'train_job_name',
'tags': ['x1', 'y1']
}]
"""
scalar_processor = ScalarsProcessor(data_manager)
metadata = scalar_processor.get_metadata_list(train_job_ids=train_jobs['train_jobs'][0]['id'], tag=train_jobs['train_jobs'][0]['tags'][0])
print(metadata)
'''
metadata
{
'scalars' : [
{
'train_job_id' : '12-12',
'metadatas' : [
{
'wall_time' : 0.1,
'step' : 1,
'value' : 0.1
}
]
}
]
}
'''
print("============end print_scalar_data\n")
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training

6
tests/ut/cpp/transform/convert_test.cc
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@ -621,6 +621,12 @@ TEST_F(TestConvert, TestTensorSummaryOps) {
ASSERT_TRUE(ret);
}
TEST_F(TestConvert, TestHistogramSummaryOps) {
auto prim = prim::kPrimHistogramSummary;
bool ret = MakeDfGraph(prim, 2);
ASSERT_TRUE(ret);
}
TEST_F(TestConvert, TestGreaterOps) {
auto prim = std::make_shared<Primitive>("Greater");
bool ret = MakeDfGraph(prim, 2);

3
tests/ut/cpp/transform/transform_base_test.cc
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@ -73,7 +73,8 @@ FuncGraphPtr MakeFuncGraph(const PrimitivePtr prim, unsigned int nparam) {
std::vector<AnfNodePtr> inputs;
inputs.push_back(NewValueNode(prim));
for (unsigned int i = 0; i < nparam; i++) {
if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" || prim->name() == "ImageSummary") &&
if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" ||
prim->name() == "ImageSummary" || prim->name() == "HistogramSummary") &&
i == 0) {
auto input = NewValueNode("testSummary");
inputs.push_back(input);

17
tests/ut/python/ops/test_nn_ops.py
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@ -198,6 +198,19 @@ class ScalarSummaryNet(nn.Cell):
return out
class HistogramSummaryNet(nn.Cell):
"""HistogramSummaryNet definition"""
def __init__(self):
super(HistogramSummaryNet, self).__init__()
self.summary = P.HistogramSummary()
def construct(self, tensor):
string_in = "wight_value"
out = self.summary(string_in, tensor)
return out
class FusedBatchNormGrad(nn.Cell):
""" FusedBatchNormGrad definition """
@ -443,6 +456,10 @@ test_cases = [
'block': ScalarSummaryNet(),
'desc_inputs': [2.2],
}),
('HistogramSummary', {
'block': HistogramSummaryNet(),
'desc_inputs': [[1,2,3]],
}),
('FusedBatchNormGrad', {
'block': FusedBatchNormGrad(nn.BatchNorm2d(num_features=512, eps=1e-5, momentum=0.1)),
'desc_inputs': [[64, 512, 7, 7], [64, 512, 7, 7]],

19
tests/ut/python/ops/test_ops.py
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@ -160,6 +160,19 @@ class SummaryNet(nn.Cell):
return self.add(x, y)
class HistogramSummaryNet(nn.Cell):
def __init__(self,):
super(HistogramSummaryNet, self).__init__()
self.summary = P.HistogramSummary()
self.add = P.TensorAdd()
def construct(self, x, y):
out = self.add(x, y)
string_in = "out"
self.summary(string_in, out)
return out
test_case_math_ops = [
('Neg', {
'block': P.Neg(),
@ -1104,6 +1117,12 @@ test_case_other_ops = [
'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
Tensor(np.array([1.2]).astype(np.float32))],
'skip': ['backward']}),
('HistogramSummary', {
'block': HistogramSummaryNet(),
'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
Tensor(np.array([1.2]).astype(np.float32))],
'skip': ['backward']}),
]
test_case_lists = [test_case_nn_ops, test_case_math_ops, test_case_array_ops, test_case_other_ops]

2
tests/ut/python/train/summary/test_summary.py
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@ -132,6 +132,7 @@ class SummaryDemo(nn.Cell):
def __init__(self,):
super(SummaryDemo, self).__init__()
self.s = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
def construct(self, x, y):
@ -139,6 +140,7 @@ class SummaryDemo(nn.Cell):
z = self.add(x, y)
self.s("z1", z)
self.s("y1", y)
self.histogram_summary("histogram", z)
return z

81
tests/ut/python/train/summary/test_summary_ops_params_valid_check.py
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@ -40,6 +40,7 @@ class SummaryDemoTag(nn.Cell):
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
@ -50,6 +51,7 @@ class SummaryDemoTag(nn.Cell):
z = self.add(x, y)
self.s(self.tag2, z)
self.s(self.tag3, y)
self.histogram_summary(self.tag1, x)
return z
@ -58,6 +60,7 @@ class SummaryDemoTagForSet(nn.Cell):
def __init__(self, tag_tuple):
super(SummaryDemoTagForSet, self).__init__()
self.s = P.ScalarSummary()
self.histogram_summary = P.HistogramSummary()
self.add = P.TensorAdd()
self.tag_tuple = tag_tuple
@ -65,6 +68,7 @@ class SummaryDemoTagForSet(nn.Cell):
z = self.add(x, y)
for tag in self.tag_tuple:
self.s(tag, x)
self.histogram_summary(tag, x)
return z
@ -98,6 +102,19 @@ class SummaryDemoValueForSet(nn.Cell):
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, tensors1, tensor2):
self.histogram_summary("value", self.value)
return self.add(tensors1, tensor2)
def run_case(net):
""" run_case """
# step 0: create the thread
@ -121,8 +138,8 @@ def run_case(net):
# Test 1: use the repeat tag
def test_scalar_summary_use_repeat_tag():
log.debug("begin test_scalar_summary_use_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)
@ -130,12 +147,12 @@ def test_scalar_summary_use_repeat_tag():
assert False
else:
assert True
log.debug("finished test_scalar_summary_use_repeat_tag")
log.debug("finished test_summary_use_repeat_tag")
# Test 2: repeat tag use for set summary
def test_scalar_summary_use_repeat_tag_for_set():
log.debug("begin test_scalar_summary_use_repeat_tag_for_set")
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)
@ -143,12 +160,12 @@ def test_scalar_summary_use_repeat_tag_for_set():
assert False
else:
assert True
log.debug("finished test_scalar_summary_use_repeat_tag_for_set")
log.debug("finished test_summary_use_repeat_tag_for_set")
# Test3: test with invalid tag(None, bool, "", int)
def test_scalar_summary_use_invalid_tag_None():
log.debug("begin test_scalar_summary_use_invalid_tag_None")
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)
@ -156,31 +173,31 @@ def test_scalar_summary_use_invalid_tag_None():
assert True
else:
assert False
log.debug("finished test_scalar_summary_use_invalid_tag_None")
log.debug("finished test_summary_use_invalid_tag_None")
# Test4: test with invalid tag(None, bool, "", int)
def test_scalar_summary_use_invalid_tag_Bool():
log.debug("begin test_scalar_summary_use_invalid_tag_Bool")
def test_summary_use_invalid_tag_Bool():
log.debug("begin test_summary_use_invalid_tag_Bool")
net = SummaryDemoTag(True, True, True)
run_case(net)
log.debug("finished test_scalar_summary_use_invalid_tag_Bool")
log.debug("finished test_summary_use_invalid_tag_Bool")
# Test5: test with invalid tag(None, bool, "", int)
def test_scalar_summary_use_invalid_tag_null():
log.debug("begin test_scalar_summary_use_invalid_tag_null")
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_scalar_summary_use_invalid_tag_null")
log.debug("finished test_summary_use_invalid_tag_null")
# Test6: test with invalid tag(None, bool, "", int)
def test_scalar_summary_use_invalid_tag_Int():
log.debug("begin test_scalar_summary_use_invalid_tag_Int")
def test_summary_use_invalid_tag_Int():
log.debug("begin test_summary_use_invalid_tag_Int")
net = SummaryDemoTag(1, 2, 3)
run_case(net)
log.debug("finished test_scalar_summary_use_invalid_tag_Int")
log.debug("finished test_summary_use_invalid_tag_Int")
# Test7: test with invalid value(None, "")
@ -196,7 +213,6 @@ def test_scalar_summary_use_invalid_value_None():
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")
@ -221,3 +237,30 @@ def test_scalar_summary_use_invalid_value_null():
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")

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