[WIP] TF zoo model validation (#7052)

* TF zoo model validation - Mobilenet

* Resnet v2

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/ValidateZooModelPredictions.java

@@ -0,0 +1,148 @@
+package org.nd4j.imports.TFGraphs;
+
+import org.apache.commons.io.FileUtils;
+import org.junit.Before;
+import org.junit.ClassRule;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.TemporaryFolder;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.BaseNd4jTest;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.factory.Nd4jBackend;
+import org.nd4j.linalg.io.ClassPathResource;
+import org.nd4j.shade.jackson.core.type.TypeReference;
+import org.nd4j.shade.jackson.databind.ObjectMapper;
+
+import java.io.File;
+import java.net.URL;
+import java.nio.charset.StandardCharsets;
+import java.util.*;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+public class ValidateZooModelPredictions {
+
+    @Rule
+    public TemporaryFolder testDir = new TemporaryFolder();
+
+    @Before
+    public void before() {
+        Nd4j.create(1);
+        Nd4j.setDataType(DataType.DOUBLE);
+        Nd4j.getRandom().setSeed(123);
+    }
+
+
+    @Test
+    public void testMobilenetV1() throws Exception {
+        TFGraphTestZooModels.currentTestDir = testDir.newFolder();
+
+        //Load model
+        String path = "tf_graphs/zoo_models/mobilenet_v1_0.5_128/tf_model.txt";
+        File resource = new ClassPathResource(path).getFile();
+        SameDiff sd = TFGraphTestZooModels.LOADER.apply(resource, "mobilenet_v1_0.5_128");
+
+        //Load data
+        //Because we don't have DataVec NativeImageLoader in ND4J tests due to circular dependencies, we'll load the image previously saved...
+        File imgFile = new ClassPathResource("deeplearning4j-zoo/goldenretriever_rgb128_unnormalized_nchw_INDArray.bin").getFile();
+        INDArray img = Nd4j.readBinary(imgFile).castTo(DataType.FLOAT);
+        img = img.permute(0,2,3,1).dup();   //to NHWC
+
+        //Mobilenet V1 - not sure, but probably using inception preprocessing
+        //i.e., scale to (-1,1) range
+        //Image is originally 0 to 255
+        img.divi(255).subi(0.5).muli(2);
+
+        double min = img.minNumber().doubleValue();
+        double max = img.maxNumber().doubleValue();
+
+        assertTrue(min >= -1 && min <= -0.6);
+        assertTrue(max <= 1 && max >= 0.6);
+
+        //Perform inference
+        List<String> inputs = sd.inputs();
+        assertEquals(1, inputs.size());
+        List<String> outputs = sd.outputs();
+        assertEquals(1, outputs.size());
+
+        String out = outputs.get(0);
+        Map<String,INDArray> m = sd.exec(Collections.singletonMap(inputs.get(0), img), out);
+
+        INDArray outArr = m.get(out);
+
+
+        System.out.println("SHAPE: " + Arrays.toString(outArr.shape()));
+        System.out.println(outArr);
+
+        INDArray argmax = outArr.argMax(1);
+
+        //Load labels
+        List<String> labels = labels();
+
+        int classIdx = argmax.getInt(0);
+        String className = labels.get(classIdx);
+        String expClass = "golden retriever";
+        double prob = outArr.getDouble(classIdx);
+
+        System.out.println("Predicted class: \"" + className + "\" - probability = " + prob);
+        assertEquals(expClass, className);
+    }
+
+
+    @Test
+    public void testResnetV2() throws Exception {
+        TFGraphTestZooModels.currentTestDir = testDir.newFolder();
+
+        //Load model
+        String path = "tf_graphs/zoo_models/resnetv2_imagenet_frozen_graph/tf_model.txt";
+        File resource = new ClassPathResource(path).getFile();
+        SameDiff sd = TFGraphTestZooModels.LOADER.apply(resource, "resnetv2_imagenet_frozen_graph");
+
+        //Load data
+        //Because we don't have DataVec NativeImageLoader in ND4J tests due to circular dependencies, we'll load the image previously saved...
+        File imgFile = new ClassPathResource("deeplearning4j-zoo/goldenretriever_rgb224_unnormalized_nchw_INDArray.bin").getFile();
+        INDArray img = Nd4j.readBinary(imgFile).castTo(DataType.FLOAT);
+        img = img.permute(0,2,3,1).dup();   //to NHWC
+
+        //Resnet v2 - NO external normalization, just resize and center crop
+        // https://github.com/tensorflow/models/blob/d32d957a02f5cffb745a4da0d78f8432e2c52fd4/research/tensorrt/tensorrt.py#L70
+        // https://github.com/tensorflow/models/blob/1af55e018eebce03fb61bba9959a04672536107d/official/resnet/imagenet_preprocessing.py#L253-L256
+
+        //Perform inference
+        List<String> inputs = sd.inputs();
+        assertEquals(1, inputs.size());
+
+        String out = "softmax_tensor";
+        Map<String,INDArray> m = sd.exec(Collections.singletonMap(inputs.get(0), img), out);
+
+        INDArray outArr = m.get(out);
+
+
+        System.out.println("SHAPE: " + Arrays.toString(outArr.shape()));
+        System.out.println(outArr);
+
+        INDArray argmax = outArr.argMax(1);
+
+        //Load labels
+        List<String> labels = labels();
+
+        int classIdx = argmax.getInt(0);
+        String className = labels.get(classIdx);
+        String expClass = "golden retriever";
+        double prob = outArr.getDouble(classIdx);
+
+        System.out.println("Predicted class: " + classIdx + " - \"" + className + "\" - probability = " + prob);
+        assertEquals(expClass, className);
+    }
+
+
+    public static List<String> labels() throws Exception {
+        File labelsFile = new ClassPathResource("tf_graphs/zoo_models/labels/imagenet_labellist.txt").getFile();
+        List<String> labels = FileUtils.readLines(labelsFile, StandardCharsets.UTF_8);
+        return labels;
+    }
+}

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/BaseNd4jTest.java

@@ -23,7 +23,10 @@
 import org.junit.Before;
 import org.junit.runner.RunWith;
 import org.junit.runners.Parameterized;
+import org.nd4j.config.ND4JEnvironmentVars;
+import org.nd4j.config.ND4JSystemProperties;
 import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.environment.Nd4jEnvironment;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.factory.Nd4jBackend;
 import org.nd4j.linalg.util.ArrayUtil;
@@ -58,7 +61,7 @@ public BaseNd4jTest(String name, Nd4jBackend backend) {
         this.name = name;
 
         //Suppress ND4J initialization - don't need this logged for every test...
-        System.setProperty(Nd4j.LOG_INIT_ENV_PROPERTY, "false");
+        System.setProperty(ND4JSystemProperties.LOG_INITIALIZATION, "false");
         System.gc();
     }