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Pre Merge pull request !413 from jwx930962/st

pull/413/MERGE
jwx930962 Gitee 4 years ago
parent
a9de1a20bb cbb2ab913d
commit
56f57f733c
9 changed files with 1013 additions and 1 deletions
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  1. BIN
      tests/st/testcase/origin_models/avgpool3dgrad.pb.txt
  2. BIN
      tests/st/testcase/origin_models/conv2d.pb
  3. +26
    -0
      tests/st/testcase/origin_models/conv2d_depthwise_pb_gen.py
  4. BIN
      tests/st/testcase/origin_models/model.pb
  5. +71
    -0
      tests/st/testcase/origin_models/test.json
  6. +41
    -0
      tests/st/testcase/origin_models/test_avgpool3dgrad_pb_gen.py
  7. +27
    -0
      tests/st/testcase/origin_models/test_conv2d_pb_gen.py
  8. BIN
      tests/st/testcase/origin_models/test_depth_wise_conv2d.pb
  9. +848
    -1
      tests/st/testcase/test_tensorflow_parser.cc

BIN
tests/st/testcase/origin_models/avgpool3dgrad.pb.txt View File


BIN
tests/st/testcase/origin_models/conv2d.pb View File


+ 26
- 0
tests/st/testcase/origin_models/conv2d_depthwise_pb_gen.py View File

@@ -0,0 +1,26 @@
import tensorflow as tf
import os

pb_file_path = os.getcwd()

with tf.compat.v1.Session(graph=tf.Graph()) as sess:
# NHWC
fmap_shape = [17, 101, 101, 17]
filter_size = [5, 5, 17, 1]
dy_shape = [17, 49, 49, 17]
strideh, stridew = [2, 2]
padding = 'VALID'
tensor_x1 = tf.compat.v1.placeholder(dtype="float16", shape=fmap_shape)
tensor_x2 = tf.compat.v1.placeholder(dtype="float16", shape=fmap_shape)
tensor_x = tf.add(tensor_x1, tensor_x2)
tensor_dy1 = tf.compat.v1.placeholder(dtype="float16", shape=dy_shape)
tensor_dy2 = tf.compat.v1.placeholder(dtype="float16", shape=dy_shape)
tensor_dy = tf.add(tensor_dy1, tensor_dy2)
op = tf.nn.depthwise_conv2d_backprop_filter(tensor_x, filter_size, tensor_dy,
strides=[1, strideh, stridew, 1],
padding=padding,
data_format='NHWC',
dilations=[1,1,1,1])

tf.io.write_graph(sess.graph, logdir="./", name="test_depth_wise_conv2d.pb", as_text=False)

BIN
tests/st/testcase/origin_models/model.pb View File


+ 71
- 0
tests/st/testcase/origin_models/test.json View File

@@ -0,0 +1,71 @@
{
"node": [
{
"attr": [
{
"key": "dtype",
"value": {
"type": "DT_HALF"
}
},
{
"key": "shape",
"value": {
"shape": {
"dim": [
{
"size": 1
}
]
}
}
}
],
"name": "Placeholder",
"op": "Placeholder"
},
{
"attr": [
{
"key": "dtype",
"value": {
"type": "DT_HALF"
}
},
{
"key": "shape",
"value": {
"shape": {
"dim": [
{
"size": 1
}
]
}
}
}
],
"name": "Placeholder_1",
"op": "Placeholder"
},
{
"attr": [
{
"key": "T",
"value": {
"type": "DT_HALF"
}
}
],
"input": [
"Placeholder",
"Placeholder_1"
],
"name": "add_test_1",
"op": "Add"
}
],
"versions": {
"producer": 134
}
}

+ 41
- 0
tests/st/testcase/origin_models/test_avgpool3dgrad_pb_gen.py View File

@@ -0,0 +1,41 @@
import tensorflow as tf
import os
import numpy as np
from tensorflow.python.framework import graph_util

pb_file_path = os.getcwd()

def generate_case_1():
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
grads_1 = tf.compat.v1.placeholder(dtype="float16", shape=(1,2,2,2,1))
grads_2 = tf.compat.v1.placeholder(dtype="float16", shape=(1,2,2,2,1))
grads = tf.add(grads_1, grads_2)
orig_input_shape = tf.constant(np.array([1,3,3,3,1]).astype("int32"))
op = tf.raw_ops.AvgPool3DGrad(orig_input_shape=orig_input_shape,
grad=grads,
ksize=[1,2,2,2,1],
strides=[1,1,1,1,1],
padding="VALID",
data_format='NDHWC',
name='AvgPool3DGrad')

tf.io.write_graph(sess.graph, logdir="./", name="avgpool3dgrad_case_1.pb", as_text=False)

def generate_case_2():
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
grads_1 = tf.compat.v1.placeholder(dtype="float16", shape=(1,1,2,2,2))
grads_2 = tf.compat.v1.placeholder(dtype="float16", shape=(1,1,2,2,2))
grads = tf.add(grads_1, grads_2)
orig_input_shape = tf.constant(np.array([1,1,3,3,3]).astype("int32"), )
op = tf.raw_ops.AvgPool3DGrad(orig_input_shape=orig_input_shape,
grad=grads,
ksize=[1,1,2,2,2],
strides=[1,1,1,1,1],
padding="VALID",
data_format='NCDHW',
name='AvgPool3DGrad')

tf.io.write_graph(sess.graph, logdir="./", name="avgpool3dgrad.pb.txt", as_text=False)

if __name__=='__main__':
generate_case_2()

+ 27
- 0
tests/st/testcase/origin_models/test_conv2d_pb_gen.py View File

@@ -0,0 +1,27 @@
import tensorflow as tf
import os
from tensorflow.python.framework import graph_util

pb_file_path = os.getcwd()

def generate_conv2d_pb():
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
input_x = tf.compat.v1.placeholder(dtype="float32", shape=(1,56,56,64))
input_filter = tf.compat.v1.placeholder(dtype="float32", shape=(3,3,64,64))
op = tf.nn.conv2d(input_x, input_filter, strides=[1,1,1,1], padding=[[0,0],[1,1],[1,1],[0,0]],
data_format="NHWC", dilations=[1,1,1,1], name='conv2d_res')
tf.io.write_graph(sess.graph, logdir="./", name="conv2d.pb", as_text=False)


def generate_add_pb():
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
x = tf.compat.v1.placeholder(tf.int32, name='x')
y = tf.compat.v1.placeholder(tf.int32, name='y')
b = tf.Variable(1, name='b')
xy = tf.multiply(x, y)
op = tf.add(xy, b, name='op_to_store')
tf.io.write_graph(sess.graph, logdir="./", name="model.pb", as_text=False)

if __name__=='__main__':
generate_conv2d_pb()
generate_add_pb()

BIN
tests/st/testcase/origin_models/test_depth_wise_conv2d.pb View File


+ 848
- 1
tests/st/testcase/test_tensorflow_parser.cc View File

@@ -15,14 +15,34 @@
*/

#include <gtest/gtest.h>

#define protected public
#define private public
#include "parser/common/op_parser_factory.h"
#include "parser/tensorflow/tensorflow_parser.h"
#include "graph/operator_reg.h"
#include "register/op_registry.h"
#include "external/register/register.h"
#include "parser/common/register_tbe.h"
#include "external/parser/tensorflow_parser.h"
#include "st/parser_st_utils.h"
#include "tests/depends/ops_stub/ops_stub.h"
#include "parser/common/acl_graph_parser_util.h"
#include "metadef/third_party/graphengine/inc/external/ge/ge_api_types.h"
#include "omg/parser/parser_factory.h"
#include "common/pre_checker.h"
#include "common/util.h"
#include "external/parser/tensorflow_parser.h"
#undef protected
#undef private

using namespace std;
using namespace domi::tensorflow;
using namespace domi;
using namespace testing;
using namespace std;
using namespace google::protobuf;

static const string GRAPH_DEFAULT_NAME = "default";

namespace ge {
class STestTensorflowParser : public testing::Test {
@@ -55,6 +75,336 @@ void STestTensorflowParser::RegisterCustomOp() {
domi::OpRegistry::Instance()->registrationDatas.clear();
}

namespace {
NodeDef *MallocNodeDef(const string &name, const string &type) {
NodeDef* node_def = new (std::nothrow) NodeDef();
if (node_def != nullptr) {
node_def->set_name(name);
node_def->set_op(type);
}
return node_def;
}

void GenOriginNodeDef(ge::TensorFlowModelParser *tensorflow_parser, vector<string> &node_name_list) {
NodeDef* pre_node_a = MallocNodeDef("pre_node_a", "Const");
EXPECT_NE(pre_node_a, nullptr);
{
::google::protobuf::Map< ::std::string, ::tensorflow::AttrValue >* node_attr_map = pre_node_a->mutable_attr();
tensorflow::AttrValue attr_dtype;
attr_dtype.set_type(tensorflow::DT_FLOAT);
(*node_attr_map)["dtype"] = attr_dtype;
tensorflow::AttrValue attr_value;
tensorflow::TensorProto* tensor = attr_value.mutable_tensor();
tensor->add_bool_val(true);
tensor->set_dtype(tensorflow::DT_BOOL);
(*node_attr_map)["value"] = attr_value;
}
tensorflow_parser->nodedef_map_["pre_node_a"] = pre_node_a;
node_name_list.push_back("pre_node_a");

NodeDef* pre_node_ctrl_in = MallocNodeDef("pre_node_ctrl_in", "Const");
EXPECT_NE(pre_node_ctrl_in, nullptr);
{
::google::protobuf::Map< ::std::string, ::tensorflow::AttrValue >* node_attr_map = pre_node_ctrl_in->mutable_attr();
tensorflow::AttrValue attr_dtype;
attr_dtype.set_type(tensorflow::DT_FLOAT);
(*node_attr_map)["dtype"] = attr_dtype;
tensorflow::AttrValue attr_value;
tensorflow::TensorProto* tensor = attr_value.mutable_tensor();
tensor->add_bool_val(true);
tensor->set_dtype(tensorflow::DT_BOOL);
(*node_attr_map)["value"] = attr_value;
}
tensorflow_parser->nodedef_map_["pre_node_ctrl_in"] = pre_node_ctrl_in;
node_name_list.push_back("pre_node_ctrl_in");

NodeDef* post_node_b = MallocNodeDef("post_node_b", "Identity");
EXPECT_NE(post_node_b, nullptr);
tensorflow_parser->nodedef_map_["post_node_b"] = post_node_b;
node_name_list.push_back("post_node_b");

NodeDef* post_node_c = MallocNodeDef("post_node_c", "Identity");
EXPECT_NE(post_node_c, nullptr);
tensorflow_parser->nodedef_map_["post_node_c"] = post_node_c;
node_name_list.push_back("post_node_c");

NodeDef* post_node_d = MallocNodeDef("post_node_d", "Identity");
EXPECT_NE(post_node_d, nullptr);
tensorflow_parser->nodedef_map_["post_node_d"] = post_node_d;
node_name_list.push_back("post_node_d");
}

void FreeNodeDefMap(ge::TensorFlowModelParser *tensorflow_parser, set<string> &malloc_node_name_list) {
for (auto &item : tensorflow_parser->nodedef_map_) {
if (item.second != nullptr && malloc_node_name_list.count(item.first) > 0) {
delete (item.second);
item.second = nullptr;
}
}
}
void GenFusionScopesResult(shared_ptr<ScopeGraph> &scope_graph, FusionScopesResult *fusion_rlt,
const string &fusion_op_name) {
if (fusion_rlt == nullptr) {
return;
}
fusion_rlt->InsertInputs("scope_node_1", {0}); // scope input 0
fusion_rlt->InsertOutputs("scope_node_m", {0}); // scope output 0
fusion_rlt->InsertOutputs("scope_node_n", {1}); // scope output 1

fusion_rlt->SetType(ge::kScopeToMultiNodes);
fusion_rlt->SetName(fusion_op_name);
fusion_rlt->SetDescription("Description for fusion node");

// Add inner nodes in sequence.
auto node1 = fusion_rlt->AddInnerNode("inner_node_1", "Unique"); // add inner node1
CHECK_INNER_NODE_CONDITION(node1 != nullptr, fusion_rlt);
auto ret = node1
->InsertInput(ge::kInputFromFusionScope, 0) // Input from 0th of boundary (a)
.InsertOutput(ge::kOutputToFusionScope, 0) // Output to 0th of boundary (b)
.InsertOutput("inner_node_2", 0) // Output to input 0th of internal node 2
.BuildInnerNode(); // Construct an internal Operator
CHECK_INNER_NODE_CONDITION(ret == ge::GRAPH_SUCCESS, fusion_rlt);
string str_val = "This is a string.";
node1->MutableOperator()->SetAttr("key1", 2); // Set integer attribute
node1->MutableOperator()->SetAttr("key2", str_val); // Set the string attribute
node1->MutableOperator()->SetAttr("key3", true); // Set boolean attribute

auto node2 = fusion_rlt->AddInnerNode("inner_node_2", "Identity"); // add inner node2
CHECK_INNER_NODE_CONDITION(node2 != nullptr, fusion_rlt);
ret = node2
->InsertInput("inner_node_1", 1) // The input comes from the 1st output of internal node 1
.InsertOutput("inner_node_3", 0) // Output to input 0th of internal node 3
.BuildInnerNode();
CHECK_INNER_NODE_CONDITION(ret == ge::GRAPH_SUCCESS, fusion_rlt);
node2->SetInputFormat("x", "NHWC");
node2->SetOutputFormat("y", "NHWC");

auto node3 = fusion_rlt->AddInnerNode("inner_node_3", "Identity"); // add inner node3
CHECK_INNER_NODE_CONDITION(node3 != nullptr, fusion_rlt);
ret = node3
->InsertInput("inner_node_2", 0) // The input comes from the 0th output of internal node 2
.InsertOutput(ge::kOutputToFusionScope, 1) // Output to 1st of boundary (c)
.BuildInnerNode();
CHECK_INNER_NODE_CONDITION(ret == ge::GRAPH_SUCCESS, fusion_rlt);

scope_graph->impl_->AddFusionScopesResult(fusion_rlt);
}

void GenOriginContext(ge::TensorFlowModelParser *tensorflow_parser, const string &fusion_op_name) {
// op_node_context for fusion op
ge::OpNodeContext op_node_context;
op_node_context.input_map["pre_node_a"].push_back({0, 0});
op_node_context.input_map["pre_node_ctrl_in"].push_back({-1, -1}); // ctrl edges
op_node_context.output_map["post_node_b"].push_back({0, 0});
op_node_context.output_map["post_node_c"].push_back({1, 0});
op_node_context.output_map["post_node_d"].push_back({-1, -1}); // ctrl edges
tensorflow_parser->op_node_context_map_[fusion_op_name] = op_node_context;
tensorflow_parser->SaveEdgesControlInfo(fusion_op_name, -1);

// op_node_context for pre_node_a
ge::OpNodeContext op_node_context_a;
op_node_context_a.output_map[fusion_op_name].push_back({0, 0});
tensorflow_parser->op_node_context_map_["pre_node_a"] = op_node_context_a;

// op_node_context for pre_node_ctrl_in
ge::OpNodeContext op_node_context_ctrl_in;
op_node_context_ctrl_in.output_map[fusion_op_name].push_back({-1, -1}); // ctrl edges
tensorflow_parser->op_node_context_map_["pre_node_ctrl_in"] = op_node_context_ctrl_in;

// op_node_context for post_node_b
ge::OpNodeContext op_node_context_b;
op_node_context_b.input_map[fusion_op_name].push_back({0, 0});
tensorflow_parser->op_node_context_map_["post_node_b"] = op_node_context_b;

// op_node_context for post_node_c
ge::OpNodeContext op_node_context_c;
op_node_context_c.input_map[fusion_op_name].push_back({1, 0});
op_node_context_c.output_map["post_node_d"].push_back({0, 0});
tensorflow_parser->op_node_context_map_["post_node_c"] = op_node_context_c;

// op_node_context for post_node_d
ge::OpNodeContext op_node_context_d;
op_node_context_d.input_map["post_node_c"].push_back({0, 0});
op_node_context_d.input_map[fusion_op_name].push_back({-1, -1}); // ctrl edges
tensorflow_parser->op_node_context_map_["post_node_d"] = op_node_context_d;
tensorflow_parser->SaveEdgesControlInfo("post_node_d", -1);

string fusion_op_type = ge::kScopeToMultiNodes;
string description = "fusion op description";
tensorflow_parser->fusion_op_type_map_[fusion_op_name].push_back(fusion_op_type);
tensorflow_parser->fusion_op_type_map_[fusion_op_name].push_back(description);
}
void register_tbe_op()
{
std::vector<OpRegistrationData> registrationDatas = OpRegistry::Instance()->registrationDatas;
for(OpRegistrationData reg_data : registrationDatas)
{
OpRegistrationTbe::Instance()->Finalize(reg_data);
OpRegistry::Instance()->Register(reg_data);
}
OpRegistry::Instance()->registrationDatas.clear();
}
}

namespace {
REG_OP(Data)
.INPUT(x, TensorType::ALL())
.OUTPUT(y, TensorType::ALL())
.ATTR(index, Int, 0)
.OP_END_FACTORY_REG(Data)

REG_OP(Add)
.INPUT(x1, TensorType({DT_FLOAT, DT_INT32, DT_INT64, DT_FLOAT16, DT_INT16,
DT_INT8, DT_UINT8, DT_DOUBLE, DT_COMPLEX128,
DT_COMPLEX64, DT_STRING}))
.INPUT(x2, TensorType({DT_FLOAT, DT_INT32, DT_INT64, DT_FLOAT16, DT_INT16,
DT_INT8, DT_UINT8, DT_DOUBLE, DT_COMPLEX128,
DT_COMPLEX64, DT_STRING}))
.OUTPUT(y, TensorType({DT_FLOAT, DT_INT32, DT_INT64, DT_FLOAT16, DT_INT16,
DT_INT8, DT_UINT8, DT_DOUBLE, DT_COMPLEX128,
DT_COMPLEX64, DT_STRING}))
.OP_END_FACTORY_REG(Add)
}

struct MemBuffer
{
void* data;
uint32_t size;
};

static MemBuffer* MemBufferFromFile(const char *path)
{
char path_temp[PATH_MAX + 1] = {0x00};
if(strlen(path) > PATH_MAX || nullptr == realpath(path, path_temp)) {
return nullptr;
}
FILE *fp = fopen(path_temp, "r+");
if (fp == nullptr) {
return nullptr;
}

// get model file length
if (0 != fseek(fp, 0, SEEK_END)) {
fclose(fp);
return nullptr;
}
long file_length = ftell(fp);
if (fseek(fp, 0, SEEK_SET)) {
fclose(fp);
return nullptr;
}
if (file_length <= 0) {
fclose(fp);
return nullptr;
}

// alloc model buffer
void *data = malloc((unsigned int)file_length);
if (!data) {
fclose(fp);
return nullptr;
}

// read file into memory
uint32_t read_size = (uint32_t)fread(data, 1, (unsigned int)file_length, fp);

// check if read success
if ((long)read_size != file_length) {
free(data);
data = nullptr;
fclose(fp);
return nullptr;
}

// close model file
fclose(fp);

// create an MemBuffer
MemBuffer* membuf = new MemBuffer();
if (!membuf) {
free(data);
data = nullptr;
return nullptr;
}
membuf->data = malloc((unsigned int)read_size);

// set size && data
membuf->size = (uint32_t)read_size;
memcpy((char*)membuf->data, (char*)data, read_size);
free(data);
return membuf;
}


/// placeholder0 placeholder1
/// | /\ /\ |
/// | / \/ \ |
/// | / /\ \ |
/// | | / \ | |
/// | add0 mul0 |
/// | / /c | \ |
/// mul1 --- / | add1
/// \ | |
/// \ ---- add2 |
/// | |
/// retval0 retval1

void CreateGraphDef(domi::tensorflow::GraphDef &graph_def) {
// 1. add node
auto placeholder0 = graph_def.add_node();
auto placeholder1 = graph_def.add_node();
auto add0 = graph_def.add_node();
auto add1 = graph_def.add_node();
auto mul0 = graph_def.add_node();
auto mul1 = graph_def.add_node();
auto add2 = graph_def.add_node();
auto retval0 = graph_def.add_node();
auto retval1 = graph_def.add_node();

// 2. set info
placeholder0->set_name("placeholder0");
placeholder0->set_op("PlaceHolder");
placeholder1->set_name("placeholder1");
placeholder1->set_op("PlaceHolder");

add0->set_name("add0");
add0->set_op("Add");
add1->set_name("add1");
add1->set_op("Add");
add2->set_name("add2");
add2->set_op("Add");

mul0->set_name("mul0");
mul0->set_op("Mul");
mul1->set_name("mul1");
mul1->set_op("Mul");

retval0->set_name("retval0");
retval0->set_op("_RetVal");
retval1->set_name("retval1");
retval1->set_op("_RetVal");

// 3. add edges
add0->add_input("placeholder0");
add0->add_input("placeholder1");

mul0->add_input("placeholder0");
mul0->add_input("placeholder1");

mul1->add_input("placeholder0");
mul1->add_input("add0");
mul1->add_input("^mul0");

add1->add_input("mul0");
add1->add_input("placeholder1");

add2->add_input("mul1");
add2->add_input("mul0");

retval0->add_input("add2:0");
retval1->add_input("add1:0");
}

TEST_F(STestTensorflowParser, tensorflow_parser_success) {
RegisterCustomOp();

@@ -75,4 +425,501 @@ TEST_F(STestTensorflowParser, tensorflow_parser_success) {
ASSERT_EQ(net_out_name.size(), 1);
EXPECT_EQ(net_out_name.at(0), "add_test_1:0");
}

TEST_F(STestTensorflowParser, tensorflow_model_Failed) {
ge::Graph graph;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);

std::string modelFile = caseDir + "/origin_models/model.pb";
auto status = ge::aclgrphParseTensorFlow(modelFile.c_str(), graph);
EXPECT_EQ(status, ge::GRAPH_FAILED);

modelFile = caseDir + "/origin_models/test_depth_wise_conv2d.pb";
status = ge::aclgrphParseTensorFlow(modelFile.c_str(), graph);
EXPECT_EQ(status, ge::GRAPH_FAILED);
}

TEST_F(STestTensorflowParser, tensorflow_model_not_exist) {
ge::Graph graph;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);

// model file is not exist
std::string modelFile = caseDir + "/origin_models/conv2d_explicit1_pad.pb";

auto status = ge::aclgrphParseTensorFlow(modelFile.c_str(), graph);
EXPECT_EQ(status, ge::GRAPH_FAILED);
}

TEST_F(STestTensorflowParser, parser_tensorflow_model) {
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
const char *model_file = modelFile.c_str();
std::string op_name = "ge_ascend_irgraph";
ge::Graph graph(op_name);

std::map<ge::AscendString, ge::AscendString> parser_options = {
{ge::AscendString(ge::ir_option::INPUT_FORMAT), ge::AscendString("NHWC")},
};
auto ret_graph = ge::aclgrphParseTensorFlow(model_file, parser_options, graph);
EXPECT_EQ(ret_graph, ge::FAILED);

// parser tensorflow model out_node_size is equal to index
string graph_name;
AclGrphParseUtil acl_graph_parse_util;
std::map<AscendString, AscendString> out_nodes_with_node_and_index = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Placeholder:0;Placeholder_1:1")}};
ParerSTestsUtils::ClearParserInnerCtx();
auto ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_with_node_and_index, graph_name);
ret_graph = ge::aclgrphParseTensorFlow(model_file, graph);
EXPECT_EQ(ret_graph, domi::FAILED);

// parser tensorflow model success
modelFile = caseDir + "/origin_models/model.pb";
model_file = modelFile.c_str();
out_nodes_with_node_and_index = {{AscendString(ge::ir_option::OUT_NODES), AscendString("x:0;y:0")}};
ParerSTestsUtils::ClearParserInnerCtx();
ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_with_node_and_index, graph_name);
ret_graph = ge::aclgrphParseTensorFlow(model_file, graph);
EXPECT_EQ(ret_graph, domi::SUCCESS);
}

TEST_F(STestTensorflowParser, tensorflow_parser_to_json)
{
TensorFlowModelParser modelParser;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
std::string jsonFile = caseDir + "/origin_models/test.json";
const char *model_file = modelFile.c_str();
const char *json_file = jsonFile.c_str();
Status ret = modelParser.ToJson(model_file, json_file);
EXPECT_EQ(ret, SUCCESS);
}

TEST_F(STestTensorflowParser, tensorflow_parserfrommemory_failed)
{
TensorFlowModelParser modelParser;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
const char *data = modelFile.c_str();
uint32_t size = 1;
ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(modelFile.c_str(), parser_params, graph);
ASSERT_EQ(ret, SUCCESS);

modelFile = caseDir + "/origin_models/tf_add.pb";
parser_params = {{AscendString(ge::ir_option::OUT_NODES), AscendString("Placeholder:0;Placeholder_1:0")}};
ret = ge::aclgrphParseTensorFlow(modelFile.c_str(), parser_params, graph);
ge::ComputeGraphPtr compute_graph = ge::GraphUtils::GetComputeGraph(graph);
ret = modelParser.ParseFromMemory(data, size, compute_graph);
EXPECT_EQ(ret, INTERNAL_ERROR);
}

TEST_F(STestTensorflowParser, modelparser_parsefrommemory_success)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
const char* tmp_tf_pb_model = modelFile.c_str();
ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(modelFile.c_str(), parser_params, graph);
ASSERT_EQ(ret, SUCCESS);

ge::ComputeGraphPtr compute_graph = ge::GraphUtils::GetComputeGraph(graph);
TensorFlowModelParser modelParser;
MemBuffer* memBuffer = MemBufferFromFile(tmp_tf_pb_model);
PreChecker::Instance().HasError() == false;
ret = modelParser.ParseFromMemory((char*)memBuffer->data, memBuffer->size, compute_graph);
free(memBuffer->data);
delete memBuffer;
}

TEST_F(STestTensorflowParser, weightsparser_parsefrommemory_success)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
const char* tmp_tf_pb_model = modelFile.c_str();
ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(modelFile.c_str(), parser_params, graph);
ASSERT_EQ(ret, SUCCESS);

ge::ComputeGraphPtr compute_graph = ge::GraphUtils::GetComputeGraph(graph);
auto weights_parser = domi::WeightsParserFactory::Instance()->CreateWeightsParser(domi::TENSORFLOW);
MemBuffer* memBuffer = MemBufferFromFile(tmp_tf_pb_model);
ret = weights_parser->ParseFromMemory((char*)memBuffer->data, memBuffer->size, compute_graph);
free(memBuffer->data);
delete memBuffer;
EXPECT_EQ(SUCCESS, ret);
}

std::string getGraphCallbackV2(string subgraph_name)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
subgraph_name = caseDir + "/origin_models/tf_add.pb";
return subgraph_name;
}

TEST_F(STestTensorflowParser, parser_ParseProtoWithSubgraphV2)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
const std::string root_proto = caseDir + "/origin_models/tf_add.pb";
ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(root_proto.c_str(), parser_params, graph);
ASSERT_EQ(ret, SUCCESS);

ge::ComputeGraphPtr root_graph = ge::GraphUtils::GetComputeGraph(graph);
domi::GetGraphCallbackV2 callback(&getGraphCallbackV2);
TensorFlowModelParser parser;
ret = parser.ParseProtoWithSubgraph(root_proto, callback, root_graph);
}

TEST_F(STestTensorflowParser, parser_ConvertToGeDataType)
{
// convert to ge type success
const uint32_t type1 = domi::tensorflow::DataType::DT_FLOAT;
TensorFlowModelParser parser;
ge::DataType dataType = parser.ConvertToGeDataType(type1);
ASSERT_EQ(dataType, ge::DataType::DT_FLOAT);

const uint32_t type2 = 80; // invalid type
dataType = parser.ConvertToGeDataType(type2);
ASSERT_EQ(dataType, ge::DataType::DT_UNDEFINED);
}

TEST_F(STestTensorflowParser, tensorflow_ParserProto_failed)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
const std::string root_proto = caseDir + "/origin_models/avgpool3dgrad.pb.txt";
domi::tensorflow::GraphDef graphDef;
ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(root_proto.c_str(), parser_params, graph);
ASSERT_EQ(ret, FAILED);

ge::ComputeGraphPtr root_graph = ge::GraphUtils::GetComputeGraph(graph);
TensorFlowModelParser tensorflow_parser;
ret = tensorflow_parser.ParseProto(reinterpret_cast<google::protobuf::Message *>(&graphDef), root_graph);
EXPECT_EQ(PARAM_INVALID, ret);

// proto解析失败
bool protoRet = parser::ReadProtoFromText(root_proto.c_str(), &graphDef);
ASSERT_EQ(protoRet, false);
ret = tensorflow_parser.ParseProto(reinterpret_cast<google::protobuf::Message *>(&graphDef), root_graph);
ASSERT_EQ(ret, PARAM_INVALID);

// // 解析
// const std::string tensorflow_pb_txt_file = caseDir + "/origin_models/model.pb";
// ret = ge::aclgrphParseTensorFlow(tensorflow_pb_txt_file.c_str(), parser_params, graph);
// ASSERT_EQ(ret, SUCCESS);
// root_graph = ge::GraphUtils::GetComputeGraph(graph);
// ret = tensorflow_parser.ParseProto(reinterpret_cast<google::protobuf::Message *>(&graphDef), root_graph);
}

TEST_F(STestTensorflowParser, tensorflow_parserAllGraph_failed)
{
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
const std::string root_proto = caseDir + "/origin_models/conv2d.pb";
domi::tensorflow::GraphDef graphDef;
CreateGraphDef(graphDef);

auto no_op = graphDef.add_node();
no_op->set_name("no_op");
no_op->set_op("NoOp");
no_op->add_input("placeholder0");
no_op->add_input("placeholder1");

ge::Graph graph;
std::map<ge::AscendString, ge::AscendString> parser_params;
Status ret = ge::aclgrphParseTensorFlow(root_proto.c_str(), parser_params, graph);
ASSERT_EQ(ret, SUCCESS);

ge::ComputeGraphPtr root_graph = ge::GraphUtils::GetComputeGraph(graph);
TensorFlowModelParser tensorflow_parser;
ret = tensorflow_parser.ParseAllGraph(reinterpret_cast<google::protobuf::Message *>(&graphDef), root_graph);
EXPECT_EQ(INTERNAL_ERROR, ret);
}

TEST_F(STestTensorflowParser, test_parse_acl_output_nodes)
{
AclGrphParseUtil acl_graph_parse_util;
string graph_name;
// case 1: Normal with 'node and index'
ParerSTestsUtils::ClearParserInnerCtx();
GetParserContext().type = domi::ONNX;
std::map<AscendString, AscendString> out_nodes_with_node_and_index = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Out1:0;Out2:1")}};
ParerSTestsUtils::ClearParserInnerCtx();
auto ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_with_node_and_index, graph_name);
ASSERT_EQ(ret, SUCCESS);
EXPECT_EQ(ge::GetParserContext().user_out_nodes.size(), 2);
EXPECT_EQ(ge::GetParserContext().out_nodes_map.size(), 2);
EXPECT_EQ(ge::GetParserContext().user_out_tensors.size(), 0);

// case 2: Normal with 'tensor name'
ParerSTestsUtils::ClearParserInnerCtx();
GetParserContext().type = domi::ONNX;
std::map<AscendString, AscendString> out_nodes_with_tensor_name = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Out_tensor_1;Out_tensor_2")}};
ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_with_tensor_name, graph_name);
ASSERT_EQ(ret, SUCCESS);
EXPECT_EQ(ge::GetParserContext().user_out_nodes.size(), 0);
EXPECT_EQ(ge::GetParserContext().out_nodes_map.size(), 0);
EXPECT_EQ(ge::GetParserContext().user_out_tensors.size(), 2);

// case 3: Failed with 'node and index' before 'tensor name'
ParerSTestsUtils::ClearParserInnerCtx();
GetParserContext().type = domi::ONNX;
std::map<AscendString, AscendString> out_nodes_mode_mixex_pre = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Out1:0;Out2:1;Out_tensor_1;Out_tensor_2")}};
ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_mode_mixex_pre, graph_name);
ASSERT_EQ(ret, PARAM_INVALID);
EXPECT_EQ(ge::GetParserContext().user_out_nodes.size(), 2);
EXPECT_EQ(ge::GetParserContext().out_nodes_map.size(), 2);
EXPECT_EQ(ge::GetParserContext().user_out_tensors.size(), 0);

// case 4: Failed with 'node and index' inserted in 'tensor name'
ParerSTestsUtils::ClearParserInnerCtx();
GetParserContext().type = domi::ONNX;
std::map<AscendString, AscendString> out_nodes_mode_mixex_mid = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Out_tensor_1;Out1:0;Out2:1;Out_tensor_2")}};
ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_mode_mixex_mid, graph_name);
ASSERT_EQ(ret, PARAM_INVALID);
EXPECT_EQ(ge::GetParserContext().user_out_nodes.size(), 0);
EXPECT_EQ(ge::GetParserContext().out_nodes_map.size(), 0);
EXPECT_EQ(ge::GetParserContext().user_out_tensors.size(), 1);

// case 5: Failed with 'node and index' after 'tensor name'
ParerSTestsUtils::ClearParserInnerCtx();
GetParserContext().type = domi::ONNX;
std::map<AscendString, AscendString> out_nodes_mode_mixex_post = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Out_tensor_1;Out_tensor_2;Out1:0;Out2:1")}};
ret = acl_graph_parse_util.ParseParamsBeforeGraph(out_nodes_mode_mixex_post, graph_name);
ASSERT_EQ(ret, PARAM_INVALID);
EXPECT_EQ(ge::GetParserContext().user_out_nodes.size(), 0);
EXPECT_EQ(ge::GetParserContext().out_nodes_map.size(), 0);
EXPECT_EQ(ge::GetParserContext().user_out_tensors.size(), 2);
}

TEST_F(STestTensorflowParser, parse_AutoMappingByOp) {
static const string KEY_STRING = "key_string";
static const string KEY_INT = "key_int";
static const string KEY_FLOAT = "key_float";
static const string KEY_BOOL = "key_bool";
static const string KEY_TYPE = "key_type";
static const string VALUE_STRING = "string";
static const int64_t VALUE_INT = 1;
static const float VALUE_FLOAT = 1.0;
static const bool VALUE_BOOL = true;
static const domi::tensorflow::DataType VALUE_TYPE = domi::tensorflow::DataType::DT_FLOAT;

std::cout << "test data_type value_type: " << (int64_t)VALUE_TYPE << std::endl;
static const string VALUE_NAME = "test_name";
ge::OpDescPtr op_desc = std::make_shared<ge::OpDesc>();
NodeDef node_def;
domi::tensorflow::AttrValue value;
ge::Operator op = ge::OpDescUtils::CreateOperatorFromOpDesc(op_desc);

node_def.set_name(VALUE_NAME);
value.set_s(VALUE_STRING);
TensorFlowUtil::AddNodeAttr(KEY_STRING, value, &node_def);
value.set_i(VALUE_INT);
TensorFlowUtil::AddNodeAttr(KEY_INT, value, &node_def);
value.set_f(VALUE_FLOAT);
TensorFlowUtil::AddNodeAttr(KEY_FLOAT, value, &node_def);
value.set_b(VALUE_BOOL);
TensorFlowUtil::AddNodeAttr(KEY_BOOL, value, &node_def);
value.set_type(VALUE_TYPE);
TensorFlowUtil::AddNodeAttr(KEY_TYPE, value, &node_def);

domi::Status status = domi::AutoMappingFn(reinterpret_cast<google::protobuf::Message *>(&node_def), op);
EXPECT_EQ(domi::SUCCESS, status);
EXPECT_EQ(VALUE_NAME, op_desc->GetName());

string value_string = "";
ge::AttrUtils::GetStr(op_desc, KEY_STRING, value_string);
EXPECT_EQ(VALUE_STRING, value_string);

int64_t value_int = 0;
ge::AttrUtils::GetInt(op_desc, KEY_INT, value_int);
EXPECT_EQ(VALUE_INT, value_int);

float value_float = 0.0;
ge::AttrUtils::GetFloat(op_desc, KEY_FLOAT, value_float);
EXPECT_EQ(VALUE_FLOAT, value_float);

bool value_bool = false;
ge::AttrUtils::GetBool(op_desc, KEY_BOOL, value_bool);
EXPECT_EQ(VALUE_BOOL, value_bool);

ge::DataType data_type = ge::DT_UNDEFINED;
ge::AttrUtils::GetDataType(op_desc, KEY_TYPE, data_type);
EXPECT_EQ(ge::DT_FLOAT, data_type);

// test AutoMappingByOpFn
ge::OpDescPtr op_desc_dest = std::make_shared<ge::OpDesc>();
ge::Operator op_dest = ge::OpDescUtils::CreateOperatorFromOpDesc(op_desc_dest);

status = domi::AutoMappingByOpFn(op, op_dest);
EXPECT_EQ(domi::SUCCESS, status);
EXPECT_EQ(VALUE_NAME, op_dest.GetName());

value_string = "";
ge::AttrUtils::GetStr(op_desc_dest, KEY_STRING, value_string);
EXPECT_EQ(VALUE_STRING, value_string);

value_int = 0;
ge::AttrUtils::GetInt(op_desc_dest, KEY_INT, value_int);
EXPECT_EQ(VALUE_INT, value_int);

value_float = 0.0;
ge::AttrUtils::GetFloat(op_desc_dest, KEY_FLOAT, value_float);
EXPECT_EQ(VALUE_FLOAT, value_float);

value_bool = false;
ge::AttrUtils::GetBool(op_desc_dest, KEY_BOOL, value_bool);
EXPECT_EQ(VALUE_BOOL, value_bool);

data_type = ge::DT_UNDEFINED;
ge::AttrUtils::GetDataType(op_desc_dest, KEY_TYPE, data_type);
EXPECT_EQ(ge::DT_FLOAT, data_type);
}

TEST_F(STestTensorflowParser, parse_ParseNodeDef)
{
NodeDef * node_def = new NodeDef();
node_def->set_name("test_name");
node_def->set_op("PlaceholderWithDefault");

bool isDatasetInit = true;
TensorFlowModelParser model_parser;
Status ret = model_parser.AdaptOpType(node_def, isDatasetInit);
EXPECT_EQ(domi::SUCCESS, ret);

node_def->set_op("Add");
ret = model_parser.AdaptOpType(node_def, isDatasetInit);
EXPECT_EQ(domi::SUCCESS, ret);
delete node_def;
}

TEST_F(STestTensorflowParser, parse_AddFmkNode)
{
TensorFlowModelParser modelParser;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
ge::Graph graph;
string graph_name;
AclGrphParseUtil acl_graph_parse_util;
std::map<ge::AscendString, ge::AscendString> parser_options = {{AscendString(ge::ir_option::OUT_NODES), AscendString("Placeholder:0;Placeholder_1:0")}};
ParerSTestsUtils::ClearParserInnerCtx();
Status ret = acl_graph_parse_util.ParseParamsBeforeGraph(parser_options, graph_name);
ret = aclgrphParseTensorFlow(modelFile.c_str(), parser_options, graph);
ASSERT_EQ(ret, SUCCESS);

ge::ComputeGraphPtr compute_graph = std::make_shared<ge::ComputeGraph>(GRAPH_DEFAULT_NAME);
tensorflow::GraphDef *graphDef = new (std::nothrow) tensorflow::GraphDef();
ScopePassManager pass_manager;
std::shared_ptr<ScopeGraph> scope_graph = pass_manager.BuildScopeGraph(graphDef);

std::string fusion_op_name = "fusion_op_name";
FusionScopesResult *fusion_rlt = new (std::nothrow) FusionScopesResult();
EXPECT_NE(fusion_rlt, nullptr);
fusion_rlt->Init();
GenFusionScopesResult(scope_graph, fusion_rlt, fusion_op_name);
GenOriginContext(&modelParser, fusion_op_name);

// origin inner node def
NodeDef* node_def = MallocNodeDef("scope_node_1", "Add");
EXPECT_NE(node_def, nullptr);
modelParser.fusion_op_nodedef_map_[fusion_op_name].push_back(node_def);

bool train_flag_backup = ge::GetParserContext().train_flag;
ge::GetParserContext().train_flag = true;

REGISTER_CUSTOM_OP("Identity")
.FrameworkType(domi::TENSORFLOW)
.OriginOpType("Identity")
.ParseParamsFn(ParseParams)
.ImplyType(ImplyType::TVM);
REGISTER_CUSTOM_OP("Constant")
.FrameworkType(domi::TENSORFLOW)
.OriginOpType("Const")
.ParseParamsFn(ParseParams)
.ImplyType(ImplyType::TVM);

register_tbe_op();

std::vector<std::string> node_name_list;
GenOriginNodeDef(&modelParser, node_name_list);
std::set<std::string> malloc_node_name_list(node_name_list.begin(), node_name_list.end());
node_name_list.push_back(fusion_op_name);

ret = modelParser.AddFmkNode(compute_graph, scope_graph, node_name_list, false);
EXPECT_EQ(ret, PARAM_INVALID);
EXPECT_EQ(modelParser.scope_inner_node_map_.size(), 0);
EXPECT_EQ(modelParser.nodedef_map_.size(), 5);

ret = modelParser.AddEdges(compute_graph);
EXPECT_EQ(ret, SUCCESS);

// release resource
delete graphDef;
delete node_def;
modelParser.DeleteFuisonNodeDef();
FreeNodeDefMap(&modelParser, malloc_node_name_list);
ge::GetParserContext().train_flag = train_flag_backup;
}

TEST_F(STestTensorflowParser, parse_AddScopeInnerNode)
{
TensorFlowModelParser modelParser;
std::string caseDir = __FILE__;
std::size_t idx = caseDir.find_last_of("/");
caseDir = caseDir.substr(0, idx);
std::string modelFile = caseDir + "/origin_models/tf_add.pb";
std::string op_name = "ge_ascend_irgraph";
ge::Graph graph(op_name);
ge::ComputeGraphPtr compute_graph = ge::GraphUtils::GetComputeGraph(graph);
std::map<ge::AscendString, ge::AscendString> parser_params = {
{AscendString(ge::ir_option::OUT_NODES), AscendString("Placeholder:0;Placeholder_1:0")}};
Status ret = ge::aclgrphParseTensorFlow(modelFile.c_str(), parser_params, graph);
EXPECT_EQ(ret, SUCCESS);

std::mutex graph_mutex;
tensorflow::NodeDef *node_def = new NodeDef();
node_def->set_name("FastrcnnPredictions");
node_def->set_op("FastrcnnPredictions");
// can't find in scope_inner_node_map
ret = modelParser.AddScopeInnerNode(&modelParser, compute_graph, &graph_mutex, node_def);
EXPECT_EQ(ret, PARAM_INVALID);

delete node_def;
}

} // namespace ge

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