mirror of
https://github.com/pytorch/pytorch.git
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[codemod][lint][fbcode] Apply google-java-format
Test Plan: Sandcastle. Visual inspection. Reviewed By: scottrice Differential Revision: D19878711 fbshipit-source-id: be56f70b35825140676be511903e5274d1808f25
This commit is contained in:
Andres Suarez
committed by
Facebook Github Bot
Facebook Github Bot
parent
bf16688538
commit
b28a834813
@ -1,7 +1,5 @@
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package org.pytorch;
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import org.junit.BeforeClass;
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import java.io.IOException;
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import java.io.InputStream;
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import java.nio.file.Files;
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@ -14,7 +12,8 @@ public class PytorchHostTests extends PytorchTestBase {
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@Override
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protected String assetFilePath(String assetName) throws IOException {
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Path tempFile = Files.createTempFile("test", ".pt");
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try (InputStream resource = Objects.requireNonNull(getClass().getClassLoader().getResourceAsStream("test.pt"))) {
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try (InputStream resource =
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Objects.requireNonNull(getClass().getClassLoader().getResourceAsStream("test.pt"))) {
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Files.copy(resource, tempFile, StandardCopyOption.REPLACE_EXISTING);
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}
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return tempFile.toAbsolutePath().toString();
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@ -1,26 +1,23 @@
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package org.pytorch;
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import org.junit.Before;
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import org.junit.Test;
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import java.io.IOException;
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import java.util.HashMap;
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import java.util.Map;
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import static org.junit.Assert.assertArrayEquals;
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import static org.junit.Assert.assertEquals;
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import static org.junit.Assert.assertFalse;
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import static org.junit.Assert.assertNotNull;
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import static org.junit.Assert.assertTrue;
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import java.io.IOException;
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import java.util.HashMap;
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import java.util.Map;
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import org.junit.Test;
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public abstract class PytorchTestBase {
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private static final String TEST_MODULE_ASSET_NAME = "test.pt";
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@Test
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public void testForwardNull() throws IOException {
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final Module module = Module.load(assetFilePath(TEST_MODULE_ASSET_NAME));
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final IValue input =
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IValue.from(Tensor.fromBlob(Tensor.allocateByteBuffer(1), new long[] {1}));
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final IValue input = IValue.from(Tensor.fromBlob(Tensor.allocateByteBuffer(1), new long[] {1}));
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assertTrue(input.isTensor());
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final IValue output = module.forward(input);
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assertTrue(output.isNull());
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@ -57,17 +54,17 @@ public abstract class PytorchTestBase {
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final Module module = Module.load(assetFilePath(TEST_MODULE_ASSET_NAME));
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double[] values =
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new double[] {
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-Double.MAX_VALUE,
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Double.MAX_VALUE,
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-Double.MIN_VALUE,
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Double.MIN_VALUE,
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-Math.exp(1.d),
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-Math.sqrt(2.d),
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-3.1415f,
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3.1415f,
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-1,
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0,
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1,
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-Double.MAX_VALUE,
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Double.MAX_VALUE,
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-Double.MIN_VALUE,
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Double.MIN_VALUE,
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-Math.exp(1.d),
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-Math.sqrt(2.d),
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-3.1415f,
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3.1415f,
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-1,
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0,
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1,
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};
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for (double value : values) {
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final IValue input = IValue.from(value);
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@ -242,15 +239,14 @@ public abstract class PytorchTestBase {
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tensorFloats.getDataAsByteArray();
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}
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@Test
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public void testEqString() throws IOException {
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final Module module = Module.load(assetFilePath(TEST_MODULE_ASSET_NAME));
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String[] values =
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new String[] {
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"smoketest",
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"проверка не латинских символов", // not latin symbols check
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"#@$!@#)($*!@#$)(!@*#$"
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"smoketest",
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"проверка не латинских символов", // not latin symbols check
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"#@$!@#)($*!@#$)(!@*#$"
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};
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for (String value : values) {
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final IValue input = IValue.from(value);
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@ -267,9 +263,9 @@ public abstract class PytorchTestBase {
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final Module module = Module.load(assetFilePath(TEST_MODULE_ASSET_NAME));
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String[] values =
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new String[] {
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"smoketest",
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"проверка не латинских символов", // not latin symbols check
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"#@$!@#)($*!@#$)(!@*#$"
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"smoketest",
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"проверка не латинских символов", // not latin symbols check
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"#@$!@#)($*!@#$)(!@*#$"
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};
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for (String value : values) {
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final IValue input = IValue.from(value);
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@ -277,7 +273,8 @@ public abstract class PytorchTestBase {
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assertTrue(value.equals(input.toStr()));
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final IValue output = module.runMethod("str3Concat", input);
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assertTrue(output.isString());
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String expectedOutput = new StringBuilder().append(value).append(value).append(value).toString();
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String expectedOutput =
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new StringBuilder().append(value).append(value).append(value).toString();
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assertTrue(expectedOutput.equals(output.toStr()));
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}
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}
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@ -286,12 +283,12 @@ public abstract class PytorchTestBase {
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public void testEmptyShape() throws IOException {
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final Module module = Module.load(assetFilePath(TEST_MODULE_ASSET_NAME));
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final long someNumber = 43;
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final IValue input = IValue.from(Tensor.fromBlob(new long[]{someNumber}, new long[]{}));
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final IValue input = IValue.from(Tensor.fromBlob(new long[] {someNumber}, new long[] {}));
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final IValue output = module.runMethod("newEmptyShapeWithItem", input);
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assertTrue(output.isTensor());
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Tensor value = output.toTensor();
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assertArrayEquals(new long[]{}, value.shape());
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assertArrayEquals(new long[]{someNumber}, value.getDataAsLongArray());
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assertArrayEquals(new long[] {}, value.shape());
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assertArrayEquals(new long[] {someNumber}, value.getDataAsLongArray());
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}
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@Test
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@ -310,8 +307,8 @@ public abstract class PytorchTestBase {
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final IValue output = module.runMethod("testNonContiguous");
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assertTrue(output.isTensor());
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Tensor value = output.toTensor();
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assertArrayEquals(new long[]{2}, value.shape());
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assertArrayEquals(new long[]{100, 300}, value.getDataAsLongArray());
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assertArrayEquals(new long[] {2}, value.shape());
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assertArrayEquals(new long[] {100, 300}, value.getDataAsLongArray());
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}
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protected abstract String assetFilePath(String assetName) throws IOException;
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@ -1,8 +1,6 @@
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package org.pytorch;
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/**
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* Codes representing tensor data types.
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*/
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/** Codes representing tensor data types. */
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public enum DType {
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// NOTE: "jniCode" must be kept in sync with pytorch_jni_common.cpp.
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// NOTE: Never serialize "jniCode", because it can change between releases.
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@ -17,7 +17,8 @@ class NativePeer implements INativePeer {
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private static native HybridData initHybrid(String moduleAbsolutePath);
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@DoNotStrip
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private static native HybridData initHybridAndroidAsset(String assetName, /* android.content.res.AssetManager */ Object androidAssetManager);
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private static native HybridData initHybridAndroidAsset(
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String assetName, /* android.content.res.AssetManager */ Object androidAssetManager);
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NativePeer(String moduleAbsolutePath) {
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mHybridData = initHybrid(moduleAbsolutePath);
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@ -1,7 +1,6 @@
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package org.pytorch;
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import android.content.res.AssetManager;
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import com.facebook.jni.annotations.DoNotStrip;
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import com.facebook.soloader.nativeloader.NativeLoader;
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import com.facebook.soloader.nativeloader.SystemDelegate;
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@ -15,13 +14,14 @@ public final class PyTorchAndroid {
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}
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/**
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* Attention:
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* This is not recommended way of loading production modules, as prepackaged assets increase apk size etc.
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* For production usage consider using loading from file on the disk {@link org.pytorch.Module#load(String)}.
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* Attention: This is not recommended way of loading production modules, as prepackaged assets
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* increase apk size etc. For production usage consider using loading from file on the disk {@link
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* org.pytorch.Module#load(String)}.
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*
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* This method is meant to use in tests and demos.
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* <p>This method is meant to use in tests and demos.
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*/
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public static Module loadModuleFromAsset(final AssetManager assetManager, final String assetName) {
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public static Module loadModuleFromAsset(
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final AssetManager assetManager, final String assetName) {
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return new Module(new NativePeer(assetName, assetManager));
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}
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@ -1,8 +1,7 @@
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package org.pytorch;
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import com.facebook.jni.annotations.DoNotStrip;
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import com.facebook.jni.HybridData;
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import com.facebook.jni.annotations.DoNotStrip;
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import java.nio.Buffer;
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import java.nio.ByteBuffer;
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import java.nio.ByteOrder;
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@ -643,7 +642,8 @@ public abstract class Tensor {
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// Called from native
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@DoNotStrip
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private static Tensor nativeNewTensor(ByteBuffer data, long[] shape, int dtype, HybridData hybridData) {
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private static Tensor nativeNewTensor(
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ByteBuffer data, long[] shape, int dtype, HybridData hybridData) {
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Tensor tensor = null;
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if (DType.FLOAT32.jniCode == dtype) {
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tensor = new Tensor_float32(data.asFloatBuffer(), shape);
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@ -1,16 +1,13 @@
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package org.pytorch.torchvision;
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import android.graphics.Bitmap;
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import static org.junit.Assert.assertArrayEquals;
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import org.junit.Before;
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import android.graphics.Bitmap;
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import androidx.test.ext.junit.runners.AndroidJUnit4;
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import org.junit.Test;
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import org.junit.runner.RunWith;
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import org.pytorch.Tensor;
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import androidx.test.ext.junit.runners.AndroidJUnit4;
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import static org.junit.Assert.assertArrayEquals;
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@RunWith(AndroidJUnit4.class)
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public class TorchVisionInstrumentedTests {
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@ -3,32 +3,30 @@ package org.pytorch.torchvision;
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import android.graphics.Bitmap;
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import android.graphics.ImageFormat;
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import android.media.Image;
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import com.facebook.soloader.nativeloader.NativeLoader;
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import com.facebook.soloader.nativeloader.SystemDelegate;
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import org.pytorch.Tensor;
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import java.nio.Buffer;
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import java.nio.ByteBuffer;
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import java.nio.FloatBuffer;
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import java.util.Locale;
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import org.pytorch.Tensor;
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/**
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* Contains utility functions for {@link org.pytorch.Tensor} creation from
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* {@link android.graphics.Bitmap} or {@link android.media.Image} source.
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* Contains utility functions for {@link org.pytorch.Tensor} creation from {@link
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* android.graphics.Bitmap} or {@link android.media.Image} source.
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*/
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public final class TensorImageUtils {
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public static float[] TORCHVISION_NORM_MEAN_RGB = new float[]{0.485f, 0.456f, 0.406f};
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public static float[] TORCHVISION_NORM_STD_RGB = new float[]{0.229f, 0.224f, 0.225f};
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public static float[] TORCHVISION_NORM_MEAN_RGB = new float[] {0.485f, 0.456f, 0.406f};
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public static float[] TORCHVISION_NORM_STD_RGB = new float[] {0.229f, 0.224f, 0.225f};
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/**
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* Creates new {@link org.pytorch.Tensor} from full {@link android.graphics.Bitmap}, normalized
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* with specified in parameters mean and std.
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*
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* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB
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* order
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*/
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public static Tensor bitmapToFloat32Tensor(
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final Bitmap bitmap, final float[] normMeanRGB, final float normStdRGB[]) {
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@ -40,17 +38,17 @@ public final class TensorImageUtils {
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}
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/**
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* Writes tensor content from specified {@link android.graphics.Bitmap},
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* normalized with specified in parameters mean and std to specified {@link java.nio.FloatBuffer}
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* with specified offset.
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* Writes tensor content from specified {@link android.graphics.Bitmap}, normalized with specified
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* in parameters mean and std to specified {@link java.nio.FloatBuffer} with specified offset.
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*
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* @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data
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* @param x - x coordinate of top left corner of bitmap's area
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* @param y - y coordinate of top left corner of bitmap's area
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* @param width - width of bitmap's area
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* @param height - height of bitmap's area
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* @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data
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* @param x - x coordinate of top left corner of bitmap's area
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* @param y - y coordinate of top left corner of bitmap's area
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* @param width - width of bitmap's area
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* @param height - height of bitmap's area
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* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB
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* order
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*/
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public static void bitmapToFloatBuffer(
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final Bitmap bitmap,
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@ -89,13 +87,14 @@ public final class TensorImageUtils {
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* Creates new {@link org.pytorch.Tensor} from specified area of {@link android.graphics.Bitmap},
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* normalized with specified in parameters mean and std.
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*
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* @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data
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* @param x - x coordinate of top left corner of bitmap's area
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* @param y - y coordinate of top left corner of bitmap's area
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* @param width - width of bitmap's area
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* @param height - height of bitmap's area
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* @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data
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* @param x - x coordinate of top left corner of bitmap's area
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* @param y - y coordinate of top left corner of bitmap's area
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* @param width - width of bitmap's area
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* @param height - height of bitmap's area
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* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB order
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB
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* order
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*/
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public static Tensor bitmapToFloat32Tensor(
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final Bitmap bitmap,
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@ -110,20 +109,21 @@ public final class TensorImageUtils {
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final FloatBuffer floatBuffer = Tensor.allocateFloatBuffer(3 * width * height);
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bitmapToFloatBuffer(bitmap, x, y, width, height, normMeanRGB, normStdRGB, floatBuffer, 0);
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return Tensor.fromBlob(floatBuffer, new long[]{1, 3, height, width});
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return Tensor.fromBlob(floatBuffer, new long[] {1, 3, height, width});
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}
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/**
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* Creates new {@link org.pytorch.Tensor} from specified area of {@link android.media.Image},
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* doing optional rotation, scaling (nearest) and center cropping.
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*
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* @param image {@link android.media.Image} as a source for Tensor data
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* @param image {@link android.media.Image} as a source for Tensor data
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* @param rotateCWDegrees Clockwise angle through which the input image needs to be rotated to be
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* upright. Range of valid values: 0, 90, 180, 270
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* @param tensorWidth return tensor width, must be positive
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* @param tensorHeight return tensor height, must be positive
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* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
|
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB order
|
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* upright. Range of valid values: 0, 90, 180, 270
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* @param tensorWidth return tensor width, must be positive
|
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* @param tensorHeight return tensor height, must be positive
|
||||
* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
|
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB
|
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* order
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*/
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public static Tensor imageYUV420CenterCropToFloat32Tensor(
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final Image image,
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@ -145,26 +145,24 @@ public final class TensorImageUtils {
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final FloatBuffer floatBuffer = Tensor.allocateFloatBuffer(3 * tensorWidth * tensorHeight);
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imageYUV420CenterCropToFloatBuffer(
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image,
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rotateCWDegrees,
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tensorWidth,
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tensorHeight,
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normMeanRGB, normStdRGB, floatBuffer, 0);
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return Tensor.fromBlob(floatBuffer, new long[]{1, 3, tensorHeight, tensorWidth});
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image, rotateCWDegrees, tensorWidth, tensorHeight, normMeanRGB, normStdRGB, floatBuffer, 0);
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return Tensor.fromBlob(floatBuffer, new long[] {1, 3, tensorHeight, tensorWidth});
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}
|
||||
|
||||
/**
|
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* Writes tensor content from specified {@link android.media.Image}, doing optional rotation,
|
||||
* scaling (nearest) and center cropping to specified {@link java.nio.FloatBuffer} with specified offset.
|
||||
* scaling (nearest) and center cropping to specified {@link java.nio.FloatBuffer} with specified
|
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* offset.
|
||||
*
|
||||
* @param image {@link android.media.Image} as a source for Tensor data
|
||||
* @param image {@link android.media.Image} as a source for Tensor data
|
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* @param rotateCWDegrees Clockwise angle through which the input image needs to be rotated to be
|
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* upright. Range of valid values: 0, 90, 180, 270
|
||||
* @param tensorWidth return tensor width, must be positive
|
||||
* @param tensorHeight return tensor height, must be positive
|
||||
* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
|
||||
* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB order
|
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* @param outBuffer Output buffer, where tensor content will be written
|
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* upright. Range of valid values: 0, 90, 180, 270
|
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* @param tensorWidth return tensor width, must be positive
|
||||
* @param tensorHeight return tensor height, must be positive
|
||||
* @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order
|
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* @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB
|
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* order
|
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* @param outBuffer Output buffer, where tensor content will be written
|
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* @param outBufferOffset Output buffer offset with which tensor content will be written
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*/
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public static void imageYUV420CenterCropToFloatBuffer(
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@ -210,8 +208,7 @@ public final class TensorImageUtils {
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normMeanRGB,
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normStdRGB,
|
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outBuffer,
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||||
outBufferOffset
|
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);
|
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outBufferOffset);
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}
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|
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private static class NativePeer {
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@ -238,11 +235,11 @@ public final class TensorImageUtils {
|
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float[] normMeanRgb,
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float[] normStdRgb,
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Buffer outBuffer,
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int outBufferOffset
|
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);
|
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int outBufferOffset);
|
||||
}
|
||||
|
||||
private static void checkOutBufferCapacity(FloatBuffer outBuffer, int outBufferOffset, int tensorWidth, int tensorHeight) {
|
||||
private static void checkOutBufferCapacity(
|
||||
FloatBuffer outBuffer, int outBufferOffset, int tensorWidth, int tensorHeight) {
|
||||
if (outBufferOffset + 3 * tensorWidth * tensorHeight > outBuffer.capacity()) {
|
||||
throw new IllegalStateException("Buffer underflow");
|
||||
}
|
||||
|
||||
@ -12,15 +12,6 @@ import android.view.TextureView;
|
||||
import android.view.ViewStub;
|
||||
import android.widget.TextView;
|
||||
import android.widget.Toast;
|
||||
|
||||
import org.pytorch.IValue;
|
||||
import org.pytorch.Module;
|
||||
import org.pytorch.PyTorchAndroid;
|
||||
import org.pytorch.Tensor;
|
||||
import org.pytorch.torchvision.TensorImageUtils;
|
||||
|
||||
import java.nio.FloatBuffer;
|
||||
|
||||
import androidx.annotation.Nullable;
|
||||
import androidx.annotation.UiThread;
|
||||
import androidx.annotation.WorkerThread;
|
||||
@ -32,6 +23,12 @@ import androidx.camera.core.ImageProxy;
|
||||
import androidx.camera.core.Preview;
|
||||
import androidx.camera.core.PreviewConfig;
|
||||
import androidx.core.app.ActivityCompat;
|
||||
import java.nio.FloatBuffer;
|
||||
import org.pytorch.IValue;
|
||||
import org.pytorch.Module;
|
||||
import org.pytorch.PyTorchAndroid;
|
||||
import org.pytorch.Tensor;
|
||||
import org.pytorch.torchvision.TensorImageUtils;
|
||||
|
||||
public class CameraActivity extends AppCompatActivity {
|
||||
private static final String TAG = BuildConfig.LOGCAT_TAG;
|
||||
@ -59,10 +56,7 @@ public class CameraActivity extends AppCompatActivity {
|
||||
|
||||
if (ActivityCompat.checkSelfPermission(this, Manifest.permission.CAMERA)
|
||||
!= PackageManager.PERMISSION_GRANTED) {
|
||||
ActivityCompat.requestPermissions(
|
||||
this,
|
||||
PERMISSIONS,
|
||||
REQUEST_CODE_CAMERA_PERMISSION);
|
||||
ActivityCompat.requestPermissions(this, PERMISSIONS, REQUEST_CODE_CAMERA_PERMISSION);
|
||||
} else {
|
||||
setupCameraX();
|
||||
}
|
||||
@ -103,9 +97,9 @@ public class CameraActivity extends AppCompatActivity {
|
||||
if (requestCode == REQUEST_CODE_CAMERA_PERMISSION) {
|
||||
if (grantResults[0] == PackageManager.PERMISSION_DENIED) {
|
||||
Toast.makeText(
|
||||
this,
|
||||
"You can't use image classification example without granting CAMERA permission",
|
||||
Toast.LENGTH_LONG)
|
||||
this,
|
||||
"You can't use image classification example without granting CAMERA permission",
|
||||
Toast.LENGTH_LONG)
|
||||
.show();
|
||||
finish();
|
||||
} else {
|
||||
@ -118,17 +112,19 @@ public class CameraActivity extends AppCompatActivity {
|
||||
private static final int TENSOR_HEIGHT = 224;
|
||||
|
||||
private void setupCameraX() {
|
||||
final TextureView textureView = ((ViewStub) findViewById(R.id.camera_texture_view_stub))
|
||||
.inflate()
|
||||
.findViewById(R.id.texture_view);
|
||||
final TextureView textureView =
|
||||
((ViewStub) findViewById(R.id.camera_texture_view_stub))
|
||||
.inflate()
|
||||
.findViewById(R.id.texture_view);
|
||||
final PreviewConfig previewConfig = new PreviewConfig.Builder().build();
|
||||
final Preview preview = new Preview(previewConfig);
|
||||
preview.setOnPreviewOutputUpdateListener(new Preview.OnPreviewOutputUpdateListener() {
|
||||
@Override
|
||||
public void onUpdated(Preview.PreviewOutput output) {
|
||||
textureView.setSurfaceTexture(output.getSurfaceTexture());
|
||||
}
|
||||
});
|
||||
preview.setOnPreviewOutputUpdateListener(
|
||||
new Preview.OnPreviewOutputUpdateListener() {
|
||||
@Override
|
||||
public void onUpdated(Preview.PreviewOutput output) {
|
||||
textureView.setSurfaceTexture(output.getSurfaceTexture());
|
||||
}
|
||||
});
|
||||
|
||||
final ImageAnalysisConfig imageAnalysisConfig =
|
||||
new ImageAnalysisConfig.Builder()
|
||||
@ -149,12 +145,13 @@ public class CameraActivity extends AppCompatActivity {
|
||||
|
||||
if (result != null) {
|
||||
mLastAnalysisResultTime = SystemClock.elapsedRealtime();
|
||||
CameraActivity.this.runOnUiThread(new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
CameraActivity.this.handleResult(result);
|
||||
}
|
||||
});
|
||||
CameraActivity.this.runOnUiThread(
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
CameraActivity.this.handleResult(result);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
@ -174,17 +171,20 @@ public class CameraActivity extends AppCompatActivity {
|
||||
Log.i(TAG, "Loading module from asset '" + BuildConfig.MODULE_ASSET_NAME + "'");
|
||||
mModule = PyTorchAndroid.loadModuleFromAsset(getAssets(), BuildConfig.MODULE_ASSET_NAME);
|
||||
mInputTensorBuffer = Tensor.allocateFloatBuffer(3 * TENSOR_WIDTH * TENSOR_HEIGHT);
|
||||
mInputTensor = Tensor.fromBlob(mInputTensorBuffer, new long[]{1, 3, TENSOR_WIDTH,
|
||||
TENSOR_HEIGHT});
|
||||
mInputTensor =
|
||||
Tensor.fromBlob(mInputTensorBuffer, new long[] {1, 3, TENSOR_WIDTH, TENSOR_HEIGHT});
|
||||
}
|
||||
|
||||
final long startTime = SystemClock.elapsedRealtime();
|
||||
TensorImageUtils.imageYUV420CenterCropToFloatBuffer(
|
||||
image.getImage(), rotationDegrees,
|
||||
TENSOR_WIDTH, TENSOR_HEIGHT,
|
||||
image.getImage(),
|
||||
rotationDegrees,
|
||||
TENSOR_WIDTH,
|
||||
TENSOR_HEIGHT,
|
||||
TensorImageUtils.TORCHVISION_NORM_MEAN_RGB,
|
||||
TensorImageUtils.TORCHVISION_NORM_STD_RGB,
|
||||
mInputTensorBuffer, 0);
|
||||
mInputTensorBuffer,
|
||||
0);
|
||||
final long moduleForwardStartTime = SystemClock.elapsedRealtime();
|
||||
final Tensor outputTensor = mModule.forward(IValue.from(mInputTensor)).toTensor();
|
||||
final long moduleForwardDuration = SystemClock.elapsedRealtime() - moduleForwardStartTime;
|
||||
@ -198,9 +198,10 @@ public class CameraActivity extends AppCompatActivity {
|
||||
@UiThread
|
||||
protected void handleResult(Result result) {
|
||||
int ixs[] = Utils.topK(result.scores, 1);
|
||||
String message = String.format("forwardDuration:%d class:%s",
|
||||
result.moduleForwardDuration,
|
||||
Constants.IMAGENET_CLASSES[ixs[0]]);
|
||||
String message =
|
||||
String.format(
|
||||
"forwardDuration:%d class:%s",
|
||||
result.moduleForwardDuration, Constants.IMAGENET_CLASSES[ixs[0]]);
|
||||
Log.i(TAG, message);
|
||||
mTextViewStringBuilder.insert(0, '\n').insert(0, message);
|
||||
if (mTextViewStringBuilder.length() > TEXT_TRIM_SIZE) {
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -10,13 +10,12 @@ import androidx.annotation.Nullable;
|
||||
import androidx.annotation.UiThread;
|
||||
import androidx.annotation.WorkerThread;
|
||||
import androidx.appcompat.app.AppCompatActivity;
|
||||
import java.nio.FloatBuffer;
|
||||
import org.pytorch.IValue;
|
||||
import org.pytorch.Module;
|
||||
import org.pytorch.PyTorchAndroid;
|
||||
import org.pytorch.Tensor;
|
||||
|
||||
import java.nio.FloatBuffer;
|
||||
|
||||
public class MainActivity extends AppCompatActivity {
|
||||
|
||||
private static final String TAG = BuildConfig.LOGCAT_TAG;
|
||||
@ -31,21 +30,23 @@ public class MainActivity extends AppCompatActivity {
|
||||
private Tensor mInputTensor;
|
||||
private StringBuilder mTextViewStringBuilder = new StringBuilder();
|
||||
|
||||
private final Runnable mModuleForwardRunnable = new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
final Result result = doModuleForward();
|
||||
runOnUiThread(new Runnable() {
|
||||
private final Runnable mModuleForwardRunnable =
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
handleResult(result);
|
||||
if (mBackgroundHandler != null) {
|
||||
mBackgroundHandler.post(mModuleForwardRunnable);
|
||||
}
|
||||
final Result result = doModuleForward();
|
||||
runOnUiThread(
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
handleResult(result);
|
||||
if (mBackgroundHandler != null) {
|
||||
mBackgroundHandler.post(mModuleForwardRunnable);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
};
|
||||
|
||||
@Override
|
||||
protected void onCreate(Bundle savedInstanceState) {
|
||||
|
||||
Reference in New Issue
Block a user