Add tfhub url support

Fixes deepjavalibrary#1122

Change-Id: Ia1a2fafc502cb07878ed23dea66f1914b8b3159a

api/src/main/java/ai/djl/repository/RepositoryFactoryImpl.java

@@ -23,6 +23,7 @@
 import java.nio.file.Path;
 import java.nio.file.Paths;
 import java.util.Collections;
+import java.util.Locale;
 import java.util.Map;
 import java.util.ServiceLoader;
 import java.util.Set;
@@ -59,6 +60,23 @@ public Repository newInstance(String name, URI uri) {
             throw new IllegalArgumentException("Malformed URL: " + uri, e);
         }
 
+        if ("tfhub.dev".equals(uri.getHost().toLowerCase(Locale.ROOT))) {
+            // Handle tfhub case
+            String path = uri.getPath();
+            if (path.endsWith("/")) {
+                path = path.substring(0, path.length() - 1);
+            }
+            path = "/tfhub-modules" + path + ".tar.gz";
+            try {
+                uri = new URI("https", null, "storage.googleapis.com", -1, path, null, null);
+            } catch (URISyntaxException e) {
+                throw new IllegalArgumentException("Failed to append query string: " + uri, e);
+            }
+            String[] tokens = path.split("/");
+            String modelName = tokens[tokens.length - 2];
+            return new SimpleUrlRepository(name, uri, modelName);
+        }
+
         Path path = parseFilePath(uri);
         String fileName = path.toFile().getName();
         if (FilenameUtils.isArchiveFile(fileName)) {

api/src/test/java/ai/djl/repository/TfhubRepositoryTest.java

@@ -0,0 +1,28 @@
+/*
+ * Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
+ * with the License. A copy of the License is located at
+ *
+ * http://aws.amazon.com/apache2.0/
+ *
+ * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES
+ * OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
+ * and limitations under the License.
+ */
+package ai.djl.repository;
+
+import org.testng.Assert;
+import org.testng.annotations.Test;
+
+public class TfhubRepositoryTest {
+
+    @Test
+    public void testResource() {
+        Repository repo =
+                Repository.newInstance(
+                        "tfhub",
+                        "https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1/");
+        Assert.assertEquals(repo.getResources().size(), 1);
+    }
+}

extensions/benchmark/README.md

@@ -21,6 +21,7 @@ djl-bench currently support benchmark the following type of models:
 - ONNX model
 - PaddlePaddle model
 - TFLite model
+- TensorRT model
 - Neo DLR (TVM) model
 - XGBoost model
 
@@ -50,12 +51,24 @@ curl -O https://publish.djl.ai/djl-bench/0.12.0/djl-bench_0.12.0-1_all.deb
 sudo dpkg -i djl-bench_0.12.0-1_all.deb
 ```
 
+For centOS or Amazon Linux 2
+
+You can download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.12.0/benchmark-0.12.0.zip).
+
+```
+curl -O https://publish.djl.ai/djl-bench/0.12.0/benchmark-0.12.0.zip
+unzip benchmark-0.12.0.zip
+rm benchmark-0.12.0.zip
+sudo ln -s $PWD/benchmark-0.12.0/bin/benchmark /usr/bin/djl-bench
+```
+
 For Windows
 
 We are considering to create a `chocolatey` package for Windows. For the time being, you can
 download djl-bench zip file from [here](https://publish.djl.ai/djl-bench/0.12.0/benchmark-0.12.0.zip).
 
 Or you can run benchmark using gradle:
+
 ```
 cd djl
 
@@ -87,10 +100,10 @@ they have different CUDA version to support. Please check the individual Engine
 Here is a few sample benchmark script for you to refer. You can also skip this and directly follow
 the 4-step instructions for your own model.
 
-Benchmark on a Tensorflow model from http url with all-ones NDArray input for 10 times:
+Benchmark on a Tensorflow model from [tfhub](https://tfhub.dev/) url with all-zeros NDArray input for 10 times:
 
 ```
-djl-bench -e TensorFlow -u https://storage.googleapis.com/tfhub-modules/tensorflow/resnet_50/classification/1.tar.gz -c 10 -s 1,224,224,3
+djl-bench -e TensorFlow -u https://tfhub.dev/tensorflow/resnet_50/classification/1 -c 10 -s 1,224,224,3
 ```
 
 Similarly, this is for PyTorch
@@ -117,7 +130,6 @@ SSD object detection model:
 djl-bench -e PyTorch -c 2 -s 1,3,300,300 -u djl://ai.djl.pytorch/ssd/0.0.1/ssd_300_resnet50
 ```
 
-
 ## Configuration of Benchmark script
 
 To start your benchmarking, we need to make sure we provide the following information.
@@ -140,18 +152,20 @@ This will print out the possible arguments to pass in:
 
 ```
 usage: djl-bench [-p MODEL-PATH] -s INPUT-SHAPES [OPTIONS]
- -c,--iteration <ITERATION>         Number of total iterations (per thread).
- -d,--duration <DURATION>           Duration of the test in minutes.
- -e,--engine <ENGINE-NAME>          Choose an Engine for the benchmark.
- -g,--gpus <NUMBER_GPUS>            Number of GPUS to run multithreading inference.
- -h,--help                          Print this help.
- -l,--delay <DELAY>                 Delay of incremental threads.
- -n,--model-name <MODEL-NAME>       Specify model file name.
- -o,--output-dir <OUTPUT-DIR>       Directory for output logs.
- -p,--model-path <MODEL-PATH>       Model directory file path.
- -s,--input-shapes <INPUT-SHAPES>   Input data shapes for the model.
- -t,--threads <NUMBER_THREADS>      Number of inference threads.
- -u,--model-url <MODEL-URL>         Model archive file URL.
+ -c,--iteration <ITERATION>               Number of total iterations.
+ -d,--duration <DURATION>                 Duration of the test in minutes.
+ -e,--engine <ENGINE-NAME>                Choose an Engine for the benchmark.
+ -g,--gpus <NUMBER_GPUS>                  Number of GPUS to run multithreading inference.
+ -h,--help                                Print this help.
+ -l,--delay <DELAY>                       Delay of incremental threads.
+    --model-arguments <MODEL-ARGUMENTS>   Specify model loading arguments.
+    --model-options <MODEL-OPTIONS>       Specify model loading options.
+ -n,--model-name <MODEL-NAME>             Specify model file name.
+ -o,--output-dir <OUTPUT-DIR>             Directory for output logs.
+ -p,--model-path <MODEL-PATH>             Model directory file path.
+ -s,--input-shapes <INPUT-SHAPES>         Input data shapes for the model.
+ -t,--threads <NUMBER_THREADS>            Number of inference threads.
+ -u,--model-url <MODEL-URL>               Model archive file URL.
 ```
 
 ### Step 1: Pick your deep engine
@@ -165,6 +179,7 @@ By default, the above script will use MXNet as the default Engine, but you can a
 -e PaddlePaddle # PaddlePaddle
 -e OnnxRuntime # pytorch
 -e TFLite # TFLite
+-e TensorRT # TensorRT
 -e DLR # Neo DLR
 -e XGBoost # XGBoost
 ```