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Xts Acts
提交 bc3b4444 编写于 作者: P principal87
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更新模型、修改Promise和Callback调用方式、处理告警 

Signed-off-by: Nprincipal87 <nierong3@huawei.com>
上级 0c1985c8
隐藏空白更改
内联 并排
Showing with 1426 addition and 1652 deletion
ai/mindspore/mindsporectest/src/ohos_common.cpp
浏览文件 @ bc3b4444
......@@ -165,11 +165,12 @@ bool allclose(float *a, float *b, uint64_t count, float rtol = 1e-05,
if (i == count - 1) {
printf(" ……\n");
printf("\n *** Total fail_count: %u\n", fail_count);
printf("\n fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
tol / fail_count);
printf("\n fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
printf("\n fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
if (fail_count != 0) {
printf("\n fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
tol / fail_count);
printf("\n fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
printf("\n fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
}
c = c + count;
printf("\n avg : %f\n", sum / count);
printf("\n min : %f\n", minimum);
......@@ -254,10 +255,12 @@ bool allclose_int8(uint8_t *a, uint8_t *b, uint64_t count, float rtol = 1e-05,
if (i == count - 1) {
printf(" ……\n");
printf("\n *** Total fail_count: %u\n", fail_count);
printf("\n fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
tol / fail_count);
printf("\n fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
printf("\n fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
if (fail_count != 0) {
printf("\n fabs(a[i] - b[i])/(fabs(b[i])+1) : %f\n",
tol / fail_count);
printf("\n fabs(a[i] - b[i]) : %f\n", tol1 / fail_count);
printf("\n fabs(a[i] - b[i])/fabs(b[i]) : %f\n", tol2 / fail_count);
}
c = c + count;
printf("\n avg : %f\n", sum / count);
......
ai/mindspore/mindsporejstest/src/main/ets/test/Ability.test.ets
浏览文件 @ bc3b4444
......@@ -69,7 +69,7 @@ export default function abilityTest() {
console.log("[rawfile_copy_to_sandbox] ml_ocr_cn.ms is copy success");
}
});
} catch (error) {
console.info("[rawfile_copy_to_sandbox] getRawFileDescriptor api run failed" + error);
}
......@@ -80,7 +80,7 @@ export default function abilityTest() {
})
beforeEach(async function () {
let dir = globalThis.abilityContext.filesDir + "/";
let ml_face_model = dir + "ml_face_isface.ms";
await fs.access(ml_face_model).then(async (res) => {
if (res) {
......@@ -90,7 +90,7 @@ export default function abilityTest() {
console.info("ml_face_isface.ms file does not exists! access failed with error message: " +
err.message + ", error code: " + err.code);
});
let mnet_caffemodel_bin_file = dir + "ml_face_isface_0.input";
await fs.access(mnet_caffemodel_bin_file).then(async (res) => {
if (res) {
......@@ -132,63 +132,53 @@ export default function abilityTest() {
let modelName = 'ml_face_isface.ms';
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
let context: mindSporeLite.Context = {};
context.target = ["cpu", "nnrt"];
context.nnrt = {};
context.cpu = {};
context.cpu.threadNum = 1;
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
let context: mindSporeLite.Context = {};
context.target = ["cpu", "nnrt"];
context.nnrt = {};
context.cpu = {};
context.cpu.threadNum = 1;
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context)
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -199,55 +189,46 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
await mindSporeLite.loadModelFromFd(file.fd).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
let msliteModel = await mindSporeLite.loadModelFromFd(file.fd);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
// 正常场景：ModelBuild，调用loadModelFromFile方法，正常推理
......@@ -256,62 +237,54 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu", "nnrt"];
context.nnrt = {};
context.cpu = {};
context.cpu.threadNum = 1;
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log('=========MSLITE loadModel start=====');
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu", "nnrt"];
context.nnrt = {};
context.cpu = {};
context.cpu.threadNum = 1;
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log('=========MSLITE loadModel start=====');
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -321,58 +294,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -382,58 +347,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 2,
}
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 2,
}
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
//正常场景：Context设置CPU，1线程
......@@ -442,58 +399,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 1,
}
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 1,
}
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -503,58 +452,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 0,
}
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 0,
}
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -564,58 +505,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = 3;
console.log("MSLITE api test: set threadAffinityMode=3.");
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = 3;
console.log("MSLITE api test: set threadAffinityMode=3.");
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -625,58 +558,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.LITTLE_CORES_FIRST;
console.log("MSLITE api test: set threadAffinityMode=2.");
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.LITTLE_CORES_FIRST;
console.log("MSLITE api test: set threadAffinityMode=2.");
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -686,58 +611,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
console.log("MSLITE api test: set threadAffinityMode=1.");
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength)
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
console.log("MSLITE api test: set threadAffinityMode=1.");
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
//正常场景：Context设置CPU，绑核设置为0，不绑核
......@@ -746,58 +663,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.NO_AFFINITIES;
console.log("MSLITE api test: set threadAffinityMode=0.");
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.NO_AFFINITIES;
console.log("MSLITE api test: set threadAffinityMode=0.");
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -808,58 +717,50 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log("MSLITE api test: set threadAffinityCoreList=[0, 1, 2, 3].");
await mindSporeLite.loadModelFromFile(model_file, context).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu={};
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log("MSLITE api test: set threadAffinityCoreList=[0, 1, 2, 3].");
let msliteModel = await mindSporeLite.loadModelFromFile(model_file, context);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
//异常场景：ModelBuild，调用model path方法，path为空
......@@ -904,77 +805,69 @@ export default function abilityTest() {
let inputName = 'ml_ocr_cn_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1, 32, 512, 1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertTrue();
console.log('=========MSLITE resize success=====');
const modelInputs2 = msliteModel.getInputs();
let input_name2 = modelInputs2[0].name;
console.log(input_name2.toString());
expect(input_name2.toString()).assertEqual("data");
let input_shape2 = modelInputs2[0].shape;
console.log(input_shape2.toString());
expect(input_shape2.toString()).assertEqual("1,32,512,1");
let input_elementNum2 = modelInputs2[0].elementNum;
console.log(input_elementNum2.toString());
expect(input_elementNum2.toString()).assertEqual("16384");
let input_dtype2 = modelInputs2[0].dtype;
console.log(input_dtype2.toString());
expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format2 = modelInputs2[0].format;
console.log(input_format2.toString());
expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize2 = modelInputs2[0].dataSize;
console.log(input_dataSize2.toString());
expect(input_dataSize2.toString()).assertEqual("65536");
modelInputs2[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs2).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1, 32, 512, 1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertTrue();
console.log('=========MSLITE resize success=====');
const modelInputs2 = msliteModel.getInputs();
let input_name2 = modelInputs2[0].name;
console.log(input_name2.toString());
expect(input_name2.toString()).assertEqual("data");
let input_shape2 = modelInputs2[0].shape;
console.log(input_shape2.toString());
expect(input_shape2.toString()).assertEqual("1,32,512,1");
let input_elementNum2 = modelInputs2[0].elementNum;
console.log(input_elementNum2.toString());
expect(input_elementNum2.toString()).assertEqual("16384");
let input_dtype2 = modelInputs2[0].dtype;
console.log(input_dtype2.toString());
expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format2 = modelInputs2[0].format;
console.log(input_format2.toString());
expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize2 = modelInputs2[0].dataSize;
console.log(input_dataSize2.toString());
expect(input_dataSize2.toString()).assertEqual("65536");
modelInputs2[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs2);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -984,77 +877,69 @@ export default function abilityTest() {
let inputName = 'ml_ocr_cn_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,64,256,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertTrue();
console.log('=========MSLITE resize success=====');
const modelInputs2 = msliteModel.getInputs();
let input_name2 = modelInputs2[0].name;
console.log(input_name2.toString());
expect(input_name2.toString()).assertEqual("data");
let input_shape2 = modelInputs2[0].shape;
console.log(input_shape2.toString());
expect(input_shape2.toString()).assertEqual("1,64,256,1");
let input_elementNum2 = modelInputs2[0].elementNum;
console.log(input_elementNum2.toString());
expect(input_elementNum2.toString()).assertEqual("16384");
let input_dtype2 = modelInputs2[0].dtype;
console.log(input_dtype2.toString());
expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format2 = modelInputs2[0].format;
console.log(input_format2.toString());
expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize2 = modelInputs2[0].dataSize;
console.log(input_dataSize2.toString());
expect(input_dataSize2.toString()).assertEqual("65536");
modelInputs2[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs2).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,64,256,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertTrue();
console.log('=========MSLITE resize success=====');
const modelInputs2 = msliteModel.getInputs();
let input_name2 = modelInputs2[0].name;
console.log(input_name2.toString());
expect(input_name2.toString()).assertEqual("data");
let input_shape2 = modelInputs2[0].shape;
console.log(input_shape2.toString());
expect(input_shape2.toString()).assertEqual("1,64,256,1");
let input_elementNum2 = modelInputs2[0].elementNum;
console.log(input_elementNum2.toString());
expect(input_elementNum2.toString()).assertEqual("16384");
let input_dtype2 = modelInputs2[0].dtype;
console.log(input_dtype2.toString());
expect(input_dtype2).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format2 = modelInputs2[0].format;
console.log(input_format2.toString());
expect(input_format2).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize2 = modelInputs2[0].dataSize;
console.log(input_dataSize2.toString());
expect(input_dataSize2.toString()).assertEqual("65536");
modelInputs2[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs2);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -1064,42 +949,35 @@ export default function abilityTest() {
let inputName = 'ml_ocr_cn_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,32,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse;
console.log('=========MSLITE resize failed=====');
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,32,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse;
console.log('=========MSLITE resize failed=====');
})
......@@ -1109,40 +987,33 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,96,96,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse();
console.log('=========MSLITE resize failed=====');
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,96,96,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse();
console.log('=========MSLITE resize failed=====');
})
......@@ -1152,42 +1023,35 @@ export default function abilityTest() {
let inputName = 'ml_ocr_cn_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_ocr_cn.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,-32,32,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse();
console.log('=========MSLITE resize failed=====');
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,32,512,1");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("16384");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("65536");
console.log('=========MSLITE resize start=====');
let new_dim = new Array([1,-32,32,1]);
let resize_result = msliteModel.resize(modelInputs, new_dim);
expect(resize_result).assertFalse();
console.log('=========MSLITE resize failed=====');
})
......@@ -1197,62 +1061,53 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let num = 0;
for (var i = 0; i < 10; i++) {
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
console.log('=========MSLITE new Float32Array end=====');
console.log('=========i.toString()=====');
console.log(i.toString());
++num;
console.log('=========num.toString()=====');
console.log(num.toString());
}
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let num = 0;
for (var i = 0; i < 10; i++) {
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var z = 0; z < 2; z++) {
console.log(output0[z].toString());
expect(output0[z].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
console.log('=========i.toString()=====');
console.log(i.toString());
++num;
console.log('=========num.toString()=====');
console.log(num.toString());
}
})
......@@ -1262,56 +1117,48 @@ export default function abilityTest() {
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
for (var i = 0; i < 10; i++) {
mindSporeLite.loadModelFromFile(model_file);
}
await mindSporeLite.loadModelFromFile(model_file).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
for (var i = 0; i < 10; i++) {
mindSporeLite.loadModelFromFile(model_file);
}
let msliteModel = await mindSporeLite.loadModelFromFile(model_file);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -1321,58 +1168,48 @@ export default function abilityTest() {
let modelName = 'ml_face_isface.ms';
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,48,48,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
})
}
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -1383,76 +1220,62 @@ export default function abilityTest() {
let inputName01 = 'ml_video_edit_face_cutout_portraitSeg_deconv_0.input';
let inputName02 = 'ml_video_edit_face_cutout_portraitSeg_deconv_1.input';
let syscontext = globalThis.context;
await syscontext.resourceManager.getRawFileContent(inputName01).then(async (buffer) => {
let inputBuffer01 = buffer;
await syscontext.resourceManager.getRawFileContent(inputName02).then(async (buffer) => {
let inputBuffer02 = buffer;
console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("a");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,512,512,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("786432");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("3145728");
console.log(modelInputs[1].name);
expect(modelInputs[1].name.toString()).assertEqual("b");
console.log(modelInputs[1].shape.toString());
expect(modelInputs[1].shape.toString()).assertEqual("1,512,512,1");
console.log(modelInputs[1].elementNum.toString());
expect(modelInputs[1].elementNum.toString()).assertEqual("262144");
console.log(modelInputs[1].dtype.toString());
expect(modelInputs[1].dtype.toString()).assertEqual("43");
console.log(modelInputs[1].format.toString());
expect(modelInputs[1].format.toString()).assertEqual("1");
console.log(modelInputs[1].dataSize.toString());
expect(modelInputs[1].dataSize.toString()).assertEqual("1048576");
modelInputs[0].setData(inputBuffer01.buffer);
modelInputs[1].setData(inputBuffer02.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
})
let inputBuffer01 = await syscontext.resourceManager.getRawFileContent(inputName01);
let inputBuffer02 = await syscontext.resourceManager.getRawFileContent(inputName02);
console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("a");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,512,512,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("786432");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("3145728");
console.log(modelInputs[1].name);
expect(modelInputs[1].name.toString()).assertEqual("b");
console.log(modelInputs[1].shape.toString());
expect(modelInputs[1].shape.toString()).assertEqual("1,512,512,1");
console.log(modelInputs[1].elementNum.toString());
expect(modelInputs[1].elementNum.toString()).assertEqual("262144");
console.log(modelInputs[1].dtype.toString());
expect(modelInputs[1].dtype.toString()).assertEqual("43");
console.log(modelInputs[1].format.toString());
expect(modelInputs[1].format.toString()).assertEqual("1");
console.log(modelInputs[1].dataSize.toString());
expect(modelInputs[1].dataSize.toString()).assertEqual("1048576");
modelInputs[0].setData(inputBuffer01.buffer);
modelInputs[1].setData(inputBuffer02.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -1463,53 +1286,43 @@ export default function abilityTest() {
let modelName = 'aiy_vision_classifier_plants_V1_3.ms';
let inputName = 'aiy_vision_classifier_plants_V1_3_0.input';
let syscontext = globalThis.context;
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("module/hub_input/images_uint8");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,224,224,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("150528");
console.log(modelInputs[0].dtype.toString());
expect(modelInputs[0].dtype.toString()).assertEqual("37");
console.log(modelInputs[0].format.toString());
expect(modelInputs[0].format.toString()).assertEqual("1");
console.log(modelInputs[0].dataSize.toString());
expect(modelInputs[0].dataSize.toString()).assertEqual("150528");
modelInputs[0].setData(inputBuffer.buffer);
let Inputs2 = new Uint8Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Uint8Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("module/hub_input/images_uint8");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,224,224,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("150528");
console.log(modelInputs[0].dtype.toString());
expect(modelInputs[0].dtype.toString()).assertEqual("37");
console.log(modelInputs[0].format.toString());
expect(modelInputs[0].format.toString()).assertEqual("1");
console.log(modelInputs[0].dataSize.toString());
expect(modelInputs[0].dataSize.toString()).assertEqual("150528");
modelInputs[0].setData(inputBuffer.buffer);
let Inputs2 = new Uint8Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Uint8Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
......@@ -1521,187 +1334,240 @@ export default function abilityTest() {
let inputName02 = 'ml_headpose_pb2tflite_1.input';
let inputName03 = 'ml_headpose_pb2tflite_2.input';
let syscontext = globalThis.context
await syscontext.resourceManager.getRawFileContent(inputName01).then(async (buffer) => {
let inputBuffer01 = buffer;
await syscontext.resourceManager.getRawFileContent(inputName02).then(async (buffer) => {
let inputBuffer02 = buffer;
await syscontext.resourceManager.getRawFileContent(inputName03).then(async (buffer) => {
let inputBuffer03 = buffer;
console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
console.log('=========MSLITE success, input03 bin bytelength: ' + inputBuffer03.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer).then(async (msliteModel) => {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("input_1");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,64,64,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("12288");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("49152");
console.log(modelInputs[1].name);
expect(modelInputs[1].name.toString()).assertEqual("batch_normalization_8/batchnorm/add");
console.log(modelInputs[1].shape.toString());
expect(modelInputs[1].shape.toString()).assertEqual("16");
console.log(modelInputs[1].elementNum.toString());
expect(modelInputs[1].elementNum.toString()).assertEqual("16");
console.log(modelInputs[1].dtype.toString());
expect(modelInputs[1].dtype.toString()).assertEqual("43");
console.log(modelInputs[1].format.toString());
expect(modelInputs[1].format.toString()).assertEqual("1");
console.log(modelInputs[1].dataSize.toString());
expect(modelInputs[1].dataSize.toString()).assertEqual("64");
console.log(modelInputs[2].name);
expect(modelInputs[2].name.toString()).assertEqual("batch_normalization_1/batchnorm/add");
console.log(modelInputs[2].shape.toString());
expect(modelInputs[2].shape.toString()).assertEqual("16");
console.log(modelInputs[2].elementNum.toString());
expect(modelInputs[2].elementNum.toString()).assertEqual("16");
console.log(modelInputs[2].dtype.toString());
expect(modelInputs[2].dtype.toString()).assertEqual("43");
console.log(modelInputs[2].format.toString());
expect(modelInputs[2].format.toString()).assertEqual("1");
console.log(modelInputs[2].dataSize.toString());
expect(modelInputs[2].dataSize.toString()).assertEqual("64");
modelInputs[0].setData(inputBuffer01.buffer);
modelInputs[1].setData(inputBuffer02.buffer);
modelInputs[2].setData(inputBuffer03.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
await msliteModel.predict(modelInputs).then((modelOutputs) => {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
}).catch(err => {
console.log("predict catch: ", err);
})
}).catch(err => {
console.log("loadModel catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
})
})
let inputBuffer01 = await syscontext.resourceManager.getRawFileContent(inputName01);
let inputBuffer02 = await syscontext.resourceManager.getRawFileContent(inputName02);
let inputBuffer03 = await syscontext.resourceManager.getRawFileContent(inputName03);
console.log('=========MSLITE success, input01 bin bytelength: ' + inputBuffer01.byteLength);
console.log('=========MSLITE success, input02 bin bytelength: ' + inputBuffer02.byteLength);
console.log('=========MSLITE success, input03 bin bytelength: ' + inputBuffer03.byteLength);
let modelBuffer = await syscontext.resourceManager.getRawFileContent(modelName);
let msliteModel = await mindSporeLite.loadModelFromBuffer(modelBuffer.buffer);
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
console.log(modelInputs[0].name);
expect(modelInputs[0].name.toString()).assertEqual("input_1");
console.log(modelInputs[0].shape.toString());
expect(modelInputs[0].shape.toString()).assertEqual("1,64,64,3");
console.log(modelInputs[0].elementNum.toString());
expect(modelInputs[0].elementNum.toString()).assertEqual("12288");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("49152");
console.log(modelInputs[1].name);
expect(modelInputs[1].name.toString()).assertEqual("batch_normalization_8/batchnorm/add");
console.log(modelInputs[1].shape.toString());
expect(modelInputs[1].shape.toString()).assertEqual("16");
console.log(modelInputs[1].elementNum.toString());
expect(modelInputs[1].elementNum.toString()).assertEqual("16");
console.log(modelInputs[1].dtype.toString());
expect(modelInputs[1].dtype.toString()).assertEqual("43");
console.log(modelInputs[1].format.toString());
expect(modelInputs[1].format.toString()).assertEqual("1");
console.log(modelInputs[1].dataSize.toString());
expect(modelInputs[1].dataSize.toString()).assertEqual("64");
console.log(modelInputs[2].name);
expect(modelInputs[2].name.toString()).assertEqual("batch_normalization_1/batchnorm/add");
console.log(modelInputs[2].shape.toString());
expect(modelInputs[2].shape.toString()).assertEqual("16");
console.log(modelInputs[2].elementNum.toString());
expect(modelInputs[2].elementNum.toString()).assertEqual("16");
console.log(modelInputs[2].dtype.toString());
expect(modelInputs[2].dtype.toString()).assertEqual("43");
console.log(modelInputs[2].format.toString());
expect(modelInputs[2].format.toString()).assertEqual("1");
console.log(modelInputs[2].dataSize.toString());
expect(modelInputs[2].dataSize.toString()).assertEqual("64");
modelInputs[0].setData(inputBuffer01.buffer);
modelInputs[1].setData(inputBuffer02.buffer);
modelInputs[2].setData(inputBuffer03.buffer);
let Inputs2 = new Float32Array(modelInputs[0].getData());
for (var i = 0; i < 5; i++) {
console.log(Inputs2[i].toString());
}
console.log('=========MSLITE predict start=====');
let modelOutputs = await msliteModel.predict(modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
})
// 正常场景：调用loadModelFromFile callback接口设置context
it('Test_load_model_param_model_path_callback', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_path_callback");
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromFile(model_file, context, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
function load_model_from_file() {
return new Promise((resolve) => {
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
let context: mindSporeLite.Context = {};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
mindSporeLite.loadModelFromFile(model_file, context, (msliteModel) => {
resolve(msliteModel);
})
})
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_file();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：调用loadModelFromBuffer callback接口设置context
it('Test_load_model_param_model_buffer_callback', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_buffer_callback");
function load_model_from_buffer() {
return new Promise((resolve) => {
let modelName = 'ml_face_isface.ms';
syscontext.resourceManager.getRawFileContent(modelName).then((model_buffer) => {
let modelBuffer = model_buffer
let context: mindSporeLite.Context = {};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (msliteModel) => {
resolve(msliteModel);
})
})
})
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let modelName = 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_buffer();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：调用loadModelFromFd callback接口设置context
it('Test_load_model_param_model_fd_callback', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_fd_callback");
function load_model_from_fd() {
return new Promise((resolve) => {
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
......@@ -1710,357 +1576,262 @@ export default function abilityTest() {
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, context, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
}
mindSporeLite.loadModelFromFd(file.fd, context, (msliteModel) => {
resolve(msliteModel);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
})
// 正常场景：调用loadModelFromFd callback接口设置context
it('Test_load_model_param_model_fd_callback', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_fd_callback");
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
let context:mindSporeLite.Context={};
context.target = ["cpu"];
context.cpu = {
"threadNum": 4,
}
context.cpu.threadAffinityMode = mindSporeLite.ThreadAffinityMode.BIG_CORES_FIRST;
context.cpu.precisionMode = "preferred_fp16";
context.cpu.threadAffinityCoreList = [0, 1, 2, 3];
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromFd(file.fd, context, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
}
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_fd();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：调用loadModelFromFile callback接口未设置context
it('Test_load_model_param_model_path_callback_no_context', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_path_callback_no_context");
function load_model_from_file() {
return new Promise((resolve) => {
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
mindSporeLite.loadModelFromFile(model_file, (msliteModel) => {
resolve(msliteModel);
})
})
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromFile(model_file, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
}
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_file();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：调用loadModelFromBuffer callback接口未设置context
it('Test_load_model_param_model_buffer_callback_no_context', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_buffer_callback_no_context");
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let modelName = 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
await syscontext.resourceManager.getRawFileContent(modelName).then(async (model_buffer) => {
let modelBuffer = model_buffer;
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
}
function load_model_from_buffer() {
return new Promise((resolve) => {
let modelName = 'ml_face_isface.ms';
syscontext.resourceManager.getRawFileContent(modelName).then((model_buffer) => {
let modelBuffer = model_buffer
mindSporeLite.loadModelFromBuffer(modelBuffer.buffer, (msliteModel) => {
resolve(msliteModel);
})
})
})
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_buffer();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3")
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：调用loadModelFromFd callback接口未设置context
it('Test_load_model_param_model_fd_callback_no_context', 0, async function (done) {
console.log("MSLITE api test: loadModel param model file.Test_load_model_param_model_fd_callback_no_context");
function load_model_from_fd() {
return new Promise((resolve) => {
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
mindSporeLite.loadModelFromFd(file.fd, (msliteModel) => {
resolve(msliteModel);
})
})
}
function case_predict(msliteModel, modelInputs) {
return new Promise((resolve) => {
msliteModel.predict(modelInputs, (modelOutputs) => {
resolve(modelOutputs);
})
})
}
let inputName = 'ml_face_isface_0.input';
let syscontext = globalThis.context;
let model_file = globalThis.abilityContext.filesDir + '/' + 'ml_face_isface.ms';
await syscontext.resourceManager.getRawFileContent(inputName).then(async (buffer) => {
let inputBuffer = buffer;
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
let file = fs.openSync(model_file, fs.OpenMode.READ_ONLY);
console.log('=========MSLITE loadModel start=====');
mindSporeLite.loadModelFromFd(file.fd, (msliteModel) => {
try {
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
msliteModel.predict(modelInputs, (modelOutputs) => {
try {
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
console.log('=========MSLITE new Float32Array end=====');
done();
} catch (error) {
console.info("predict catch: " + error);
done();
}
})
for (var j = 0; j < 10000; j++){
console.log("wait predict end:" + j.toString());
}
} catch (error) {
console.info("loadModel catch: " + error);
done();
}
})
}).catch(err => {
console.log("getRawFileContent catch: ", err);
done();
})
let inputBuffer = await syscontext.resourceManager.getRawFileContent(inputName);
console.log('=========MSLITE success, input bin bytelength: ' + inputBuffer.byteLength);
console.log('=========MSLITE loadModel start=====');
let msliteModel = null;
msliteModel = await load_model_from_fd();
expect(msliteModel !== null).assertTrue();
console.log('=========MSLITE loadModel end=====');
const modelInputs = msliteModel.getInputs();
let input_name = modelInputs[0].name;
console.log(input_name.toString());
expect(input_name.toString()).assertEqual("data");
let input_shape = modelInputs[0].shape;
console.log(input_shape.toString());
expect(input_shape.toString()).assertEqual("1,48,48,3");
let input_elementNum = modelInputs[0].elementNum;
console.log(input_elementNum.toString());
expect(input_elementNum.toString()).assertEqual("6912");
let input_dtype = modelInputs[0].dtype;
console.log(input_dtype.toString());
expect(input_dtype).assertEqual(mindSporeLite.DataType.NUMBER_TYPE_FLOAT32);
let input_format = modelInputs[0].format;
console.log(input_format.toString());
expect(input_format).assertEqual(mindSporeLite.Format.NHWC);
let input_dataSize = modelInputs[0].dataSize;
console.log(input_dataSize.toString());
expect(input_dataSize.toString()).assertEqual("27648");
modelInputs[0].setData(inputBuffer.buffer);
console.log('=========MSLITE predict start=====');
let modelOutputs = await case_predict(msliteModel, modelInputs);
expect(modelOutputs !== null).assertTrue();
console.log('=========MSLITE new Float32Array start=====');
let output0 = new Float32Array(modelOutputs[0].getData());
expect(output0.length).assertLarger(0);
console.log('output0.length:' + output0.length);
for (var i = 0; i < 2; i++) {
console.log(output0[i].toString());
expect(output0[i].toString() !== null).assertTrue();
}
const modelInputs0 = msliteModel.getInputs();
console.log(modelInputs0[0].name.toString());
console.log('=========MSLITE new Float32Array end=====');
done();
})
// 正常场景：头文件枚举值测试
......
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