Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29455
- Don't need to load native library.
- Shape is now private.
Test Plan: Ran test.
Reviewed By: IvanKobzarev
Differential Revision: D18405213
fbshipit-source-id: e1d1abcf2122332317693ce391e840904b69e135
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27359
Adding methods to TensorImageUtils:
```
bitmapToFloatBuffer(..., FloatBuffer outBuffer, int outBufferOffset)
imageYUV420CenterCropToFloat32Tensor(..., FloatBuffer outBuffer, int outBufferOffset)
```
To be able to
- reuse FloatBuffer for inference
- to create batch-Tensor (contains several images/bitmaps)
As we reuse FloatBuffer for example demo app - image classification,
profiler shows less memory allocations (before that for every run we created new input tensor with newly allocated FloatBuffer) and ~-20ms on my PixelXL
Known open question:
At the moment every tensor element is written separatly calling `outBuffer.put()`, which is native call crossing lang boundaries
As an alternative - to allocation `float[]` on java side and fill it and put it in `outBuffer` with one call, reducing native calls, but increasing memory allocation on java side.
Tested locally just eyeballing durations - have not noticed big difference - decided to go with less memory allocations.
Will be good to merge into 1.3.0, but if not - demo app can use snapshot dependencies with this change.
PR with integration to demo app:
https://github.com/pytorch/android-demo-app/pull/6
Test Plan: Imported from OSS
Differential Revision: D17758621
Pulled By: IvanKobzarev
fbshipit-source-id: b4f1a068789279002d7ecc0bc680111f781bf980
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26995
Fix current setup, exclude fbjni - we can not use independently pytorch_android:package, for example for testing `gradle pytorch_android:cAT`
But for publishing it works as pytorch_android has dep on fbjni that will be also published
For other cases - we have 2 fbjni.so - one from native build (CMakeLists.txt does add_subdirectory(fbjni_dir)), and from dependency ':fbjni'
We need both of them as ':fbjni' also contains java classes
As a fix: keep excluding for publishing tasks (bintrayUpload, uploadArchives), but else - pickFirst (as we have 2 sources of fbjni.so)
# Testing
gradle cAT works, fbjni.so included
gradle bintrayUpload (dryRun==true) - no fbjni.so
Test Plan: Imported from OSS
Differential Revision: D17637775
Pulled By: IvanKobzarev
fbshipit-source-id: edda56ba555678272249fe7018c1f3a8e179947c
Summary:
- Normalization mean and std specified as parameters instead of hardcode
- imageYUV420CenterCropToFloat32Tensor before this change worked only with square tensors (width==height) - added generalization to support width != height with all rotations and scalings
- javadocs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26690
Differential Revision: D17556006
Pulled By: IvanKobzarev
fbshipit-source-id: 63f3321ea2e6b46ba5c34f9e92c48d116f7dc5ce
Summary:
After offline discussion with dzhulgakov :
- In future we will introduce creation of byte signed and byte unsigned dtype tensors, but java has only signed byte - we will have to add some separation for it in method names ( java types and tensor types can not be clearly mapped) => Returning type in method names
- fixes in error messages
- non-static method Tensor.numel()
- Change Tensor toString() to be more consistent with python
Update on Sep 16:
Type renaming on java side to uint8, int8, int32, float32, int64, float64
```
public abstract class Tensor {
public static final int DTYPE_UINT8 = 1;
public static final int DTYPE_INT8 = 2;
public static final int DTYPE_INT32 = 3;
public static final int DTYPE_FLOAT32 = 4;
public static final int DTYPE_INT64 = 5;
public static final int DTYPE_FLOAT64 = 6;
```
```
public static Tensor newUInt8Tensor(long[] shape, byte[] data)
public static Tensor newInt8Tensor(long[] shape, byte[] data)
public static Tensor newInt32Tensor(long[] shape, int[] data)
public static Tensor newFloat32Tensor(long[] shape, float[] data)
public static Tensor newInt64Tensor(long[] shape, long[] data)
public static Tensor newFloat64Tensor(long[] shape, double[] data)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26219
Differential Revision: D17406467
Pulled By: IvanKobzarev
fbshipit-source-id: a0d7d44dc8ce8a562da1a18bd873db762975b184
Summary:
Applying dzhulgakov review comments
org.pytorch.Tensor:
- dims renamed to shape
- typeCode to dtype
- numElements to numel
newFloatTensor, newIntTensor... to newTensor(...)
Add support of dtype=long, double
Resorted in code byte,int,float,long,double
For if conditions order float,int,byte,long,double as I expect that float and int branches will be used more often
Tensor.toString() does not have data, only numel (data buffer capacity)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26183
Differential Revision: D17374332
Pulled By: IvanKobzarev
fbshipit-source-id: ee93977d9c43c400b6c054b6286080321ccb81bc
Summary:
Gradle tasks for publishing to bintray and jcenter, mavencentral; snapshot buidls go to oss.sonatype.org
Those gradle changes adds tasks:
bintrayUpload - publishing on bintray, in 'facebook' org
uploadArchives - uploading to maven repos
Gradle tasks are copied from facebook open sourced libraries like https://github.com/facebook/litho, https://github.com/facebookincubator/spectrum
To do the publishing we need to provide somehow (e.g. in ~/.gradle/gradle.properties)
```
signing.keyId=
signing.password=
signing.secretKeyRingFile=
bintrayUsername=
bintrayApiKey=
bintrayGpgPassword=
SONATYPE_NEXUS_USERNAME=
SONATYPE_NEXUS_PASSWORD=
```
android/libs/fbjni is submodule, to be able to add publishing tasks to it (it needs to be published as separate maven dependency) - I created `android/libs/fbjni_local` that has only `build.gradle` with release tasks.
pytorch_android dependency for ':fbjni' changed from implementation -> api as implementation treated as 'private' dependency which is translated to scope=runtime in maven pom file, api works as 'compile'
Testing:
it's already published on bintray with version 0.0.4 and can be used in gradle files as
```
repositories {
maven {
url "https://dl.bintray.com/facebook/maven"
}
}
dependencies {
implementation 'com.facebook:pytorch_android:0.0.4'
implementation 'com.facebook:pytorch_android_torchvision:0.0.4'
}
```
It was published in com.facebook group
I requested sync to jcenter from bintray, that usually takes 2-3 days
Versioning added version suffixes to aar output files and circleCI jobs for android start failing as they expected just pytorch_android.aar pytorch_android_torchvision.aar, without any version
To avoid it - I changed circleCI android jobs to zip *.aar files and publish as single artifact with name artifacts.zip, I will add kostmo to check this part, if circleCI jobs finish ok - everything works :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25351
Reviewed By: kostmo
Differential Revision: D17135886
Pulled By: IvanKobzarev
fbshipit-source-id: 64eebac670bbccaaafa1b04eeab15760dd5ecdf9
Summary:
Initial commit of pytorch_android_torchvision that has utility methods for
android.media.Image, YUV_420_888 format (camera output) -> Tensor(Float) with torchvision format, normalized by ImageNet mean,std
Bitmap -> Tensor(Float) torchvision format
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25185
Reviewed By: dreiss
Differential Revision: D17053008
Pulled By: IvanKobzarev
fbshipit-source-id: 6bf7a39615bf876999982b06925e7444700e284b
