Tensorflow.Numpy.NDArray how to Numpy.NDarray

Question

Tensorflow.Numpy.NDArray how to Numpy.NDarray

cross-hello opened this issue 3 years ago · 10 comments

I am using Tensorflow package in C# project. I decided use Numpy.NET package instead of Tensorflow.Numpy.
Sometime the return value type from Tensorflow library is Tensorflow.Numpy.NDArray, and = - = How could I transform between?

Answer 1 · 2021-11-26T11:31:36.000Z

find out how to get a c# array from tensorflow (I don't know)
Read the readme on how to create an ndarray from a c# array.

Answer 2 · 2021-11-27T13:54:42.000Z

@cross-hello You can get the tensor data handle referenced by SafeTensorHandle

Answer 3 · 2021-11-29T02:47:42.000Z

Ok
Now for future reference, The function could be like below:

        public static NDarray Tensorflow_Numpy_NDArray2Numpy_NDarray(Tensorflow.NumPy.NDArray a)
        {
            SafeTensorHandle b = a.Handle;
            IntPtr d = c_api.TF_TensorData(b);
            NDarray e = new NDarray(d, (long)a.size, d.GetDtype());
            return e;
        }

(Edit: Sorry I am not native C# developer)

Answer 4 · 2021-11-30T09:45:03.000Z

Sorry, I find the method fail at the third argument in NDarray constructor.
public NDarray(IntPtr dataPtr, long dataLength, Dtype dtype) //copy from Numpy.NET
Could I get DType from Tensorflow.Numpy.NDArray variable?

Answer 5 · 2021-12-01T09:29:48.000Z

If it doesn't work directly I guess you need to use a switch to convert from the Tensorflow DType to the Numpy.Net DType

Answer 6 · 2021-12-01T11:17:04.000Z

---------------------------------------- Dec 1, 2021 17:29:59 Meinrad Recheis ***@***.***>:

If it doesn't work directly I guess you need to use a switch to convert from the Tensorflow DType to the Numpy.Net DType — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub[#86 (comment)], or unsubscribe[https://github.com/notifications/unsubscribe-auth/AKGBAYHWKULVI6P6OCRMDYLUOXTJPANCNFSM5IZVBPIQ]. Triage notifications on the go with GitHub Mobile for iOS[https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675] or Android[https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub]. [data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEgAAABICAYAAABV7bNHAAAAAXNSR0IArs4c6QAAAARzQklUCAgICHwIZIgAAAArSURBVHic7cEBDQAAAMKg909tDjegAAAAAAAAAAAAAAAAAAAAAAAAAAA+DFFIAAEctgHwAAAAAElFTkSuQmCC###24x24:true###][Tracking image][https://github.com/notifications/beacon/AKGBAYCFCWQH5ZSQC43TFU3UOXTJPA5CNFSM5IZVBPI2YY3PNVWWK3TUL52HS4DFVREXG43VMVBW63LNMVXHJKTDN5WW2ZLOORPWSZGOHKPE4UQ.gif]

I know😭 I just want to know what method it is

Answer 7 · 2021-12-03T08:36:00.000Z

When I get .dtype.GetDtype() from Tensorflow.Numpy.NDArray variable, the exception throws:

Can not convert type of given object to dtype: Tensorflow.TF_DataType

Answer 8 · 2021-12-03T08:59:41.000Z

The only thing I could get from Tensorflow.Numpy.NDArray is this:

Tensorflow.Numpy.NDArray x = Tensorflow.NumPy.np.array(1, 2, 3);
Console.WriteLine(x.dtype);// show TF_INT32
Console.WriteLine(x.dtype.GetDtype());// throw exception

Now the problem I want to know is how to construct a Numpy.Dtype variable via this = - =

Answer 9 · 2021-12-03T09:20:10.000Z

yes, I guess you need to do some kind of switch:

switch(x.dtype.ToString()) {
    case "TF_INT32":
        // ... use Numpy.NET's  Dtype.int32 or whatever it is called, you'll find it if you look in the code
       break;
   case " ... other TF type":   
        // ... use appropriate Numpy.NET Dtype
       break;
}

Answer 10 · 2021-12-03T09:59:56.000Z

Thank you, this time it pass my test in int:

       public static NDarray Tensorflow_Numpy_NDArray2Numpy_NDarray(Tensorflow.NumPy.NDArray a)
        {
            SafeTensorHandle b = a.Handle;
            IntPtr d = c_api.TF_TensorData(b);
            NDarray e;
            switch(a.dtype.ToString())
            {
                //case "TF_INT32":  e= new NDarray(d, (long)a.shape.size, np.int32);
                //case "TF_INT32":  e= new NDarray(d, (long)a.size, np.int32);
                //case "TF_INT32":  e= new NDarray(d, (long)a.bytesize, np.int32); break;
                case "TF_FLOAT":  e= new NDarray(d, (long)a.bytesize, np.float_); break;
                case "TF_DOUBLE":  e= new NDarray(d, (long)a.bytesize, np.float64); break;
                case "TF_INT32":  e= new NDarray(d, (long)a.bytesize, np.int32); break;
                case "TF_UINT8":  e= new NDarray(d, (long)a.bytesize, np.uint8); break;
                case "TF_INT16":  e= new NDarray(d, (long)a.bytesize, np.int16); break;
                case "TF_INT8":  e= new NDarray(d, (long)a.bytesize, np.int8); break;
                case "TF_COMPLEX64":  e= new NDarray(d, (long)a.bytesize, np.complex64); break;
                case "TF_COMPLEX":  e= new NDarray(d, (long)a.bytesize, np.complex_); break;
                case "TF_INT64":  e= new NDarray(d, (long)a.bytesize, np.int64  ); break;
                case "TF_BOOL":  e= new NDarray(d, (long)a.bytesize, np.bool_); break;
                case "TF_QINT8":  e= new NDarray(d, (long)a.bytesize, np.int64 ); break;
                case "TF_QUINT8":  e= new NDarray(d, (long)a.bytesize, np.uint8); break;
                case "TF_QINT32":  e= new NDarray(d, (long)a.bytesize, np.int32  ); break;
                case "TF_BFLOAT16":  e= new NDarray(d, (long)a.bytesize, np.float16 ); break;
                case "TF_QINT16":  e= new NDarray(d, (long)a.bytesize, np.int16  ); break;
                case "TF_QUINT16":  e= new NDarray(d, (long)a.bytesize, np.uint16  ); break;
                case "TF_UINT16":  e= new NDarray(d, (long)a.bytesize, np.uint16  ); break;
                case "TF_COMPLEX128":  e= new NDarray(d, (long)a.bytesize, np.complex128  ); break;
                case "TF_HALF":  e= new NDarray(d, (long)a.bytesize, np.half); break;
                case "TF_UINT64":  e= new NDarray(d, (long)a.bytesize, np.uint64  ); break;
                case "DtFloatRef":  e= new NDarray(d, (long)a.bytesize, np.float32  ); break;
                case "DtDoubleRef":  e= new NDarray(d, (long)a.bytesize, np.float64  ); break;
                case "DtInt32Ref":  e= new NDarray(d, (long)a.bytesize, np.int32  ); break;
                case "DtUint8Ref":  e= new NDarray(d, (long)a.bytesize, np.uint8  ); break;
                case "DtInt16Ref":  e= new NDarray(d, (long)a.bytesize, np.int16  ); break;
                case "DtInt8Ref":  e= new NDarray(d, (long)a.bytesize, np.int8  ); break;
                case "DtComplex64Ref":  e= new NDarray(d, (long)a.bytesize, np.complex64 ); break;
                case "DtInt64Ref":  e= new NDarray(d, (long)a.bytesize, np.int64  ); break;
                case "DtBoolRef":  e= new NDarray(d, (long)a.bytesize, np.bool_  ); break;
                case "DtQint8Ref":  e= new NDarray(d, (long)a.bytesize, np.int8  ); break;
                case "DtQuint8Ref":  e= new NDarray(d, (long)a.bytesize, np.int8  ); break;
                case "DtQint32Ref":  e= new NDarray(d, (long)a.bytesize, np.int32  ); break;
                case "DtBfloat16Ref":  e= new NDarray(d, (long)a.bytesize, np.float16  ); break;
                case "DtQint16Ref":  e= new NDarray(d, (long)a.bytesize, np.int16  ); break;
                case "DtQuint16Ref":  e= new NDarray(d, (long)a.bytesize, np.int16  ); break;
                case "DtUint16Ref":  e= new NDarray(d, (long)a.bytesize, np.uint16  ); break;
                case "DtComplex128Ref":  e= new NDarray(d, (long)a.bytesize, np.complex128  ); break;
                case "DtHalfRef":  e= new NDarray(d, (long)a.bytesize, np.half  ); break;
                case "DtUint32Ref":  e= new NDarray(d, (long)a.bytesize, np.uint32  ); break;
                case "DtUint64Ref":  e= new NDarray(d, (long)a.bytesize, np.uint64  ); break;
                default: e= new NDarray(d, （long)a.bytesize, np.int32); break;
            }
            return e;
        }