Better test result viz (#162)

* Fix how the y-axis of datasets are loaded. Write the model_input image to a file.

Currently works for the default selections all the way through.

* Fix loss in default job

* Cleanup

* Fix plugin name

Author: Octogonapus

plugin/src/main/kotlin/edu/wpi/axon/plugin/DatasetPlugins.kt

@@ -10,11 +10,10 @@ object DatasetPlugins {
     )
 
     val divideByTwoFiveFivePlugin = Plugin.Official(
-        "Divide Elements by 255",
+        "Divide Data Elements by 255",
         """
         |def process_dataset(x, y):
         |    newX = tf.cast(x / 255.0, tf.float32)
-        |    newY = tf.cast(y / 255.0, tf.float32)
         |    return (newX, newY)
         """.trimMargin()
     )
@@ -24,9 +23,8 @@ object DatasetPlugins {
         """
         |def process_dataset(x, y):
         |    newX = tf.cast(x / 255.0, tf.float32)
-        |    newY = tf.cast(y / 255.0, tf.float32)
         |    newX = newX[..., tf.newaxis]
-        |    newY = newY[..., tf.newaxis]
+        |    newY = tf.keras.utils.to_categorical(y)
         |    return (newX, newY)
         """.trimMargin()
     )
@@ -36,11 +34,10 @@ object DatasetPlugins {
         """
         |def process_dataset(x, y):
         |    newX = tf.cast(x / 255.0, tf.float32)
-        |    newY = tf.cast(y / 255.0, tf.float32)
         |    newX = newX[..., tf.newaxis]
         |    newX = tf.image.resize_images(newX, (224, 224), method=tf.image.ResizeMethod.BILINEAR, align_corners=True)
         |    newX = tf.image.grayscale_to_rgb(newX)
-        |    newY = newY[..., tf.newaxis]
+        |    newY = tf.keras.utils.to_categorical(y)
         |    return (newX, newY)
         """.trimMargin()
     )

plugin/src/main/kotlin/edu/wpi/axon/plugin/LoadTestDataPlugins.kt

@@ -35,6 +35,7 @@ object LoadTestDataPlugins {
         |    x_test = x_test[:1]
         |    x_test = tf.cast(x_test / 255, tf.float32)
         |    x_test = x_test[..., tf.newaxis]
+        |    y_test = tf.keras.utils.to_categorical(y_test)
         |    return (x_test, y_test[:1], 1)
         """.trimMargin()
     )

plugin/src/main/kotlin/edu/wpi/axon/plugin/ProcessTestOutputPlugins.kt

@@ -2,18 +2,25 @@ package edu.wpi.axon.plugin
 
 object ProcessTestOutputPlugins {
 
-    val serializeModelOutputPlugin = Plugin.Official(
-        "Serialize Model Output",
+    val classifyImageModelOutputPlugin = Plugin.Official(
+        "Classify Image",
         """
         |def process_model_output(model_input, expected_output, model_output):
-        |    # import json
         |    import numpy as np
+        |
         |    with open("output/expected_output.txt", "w+") as f:
-        |        # json.dump(expected_output, f)
         |        np.savetxt(f, expected_output)
+        |
         |    with open("output/model_output.txt", "w+") as f:
-        |        # json.dump(model_output, f)
         |        np.savetxt(f, model_output)
+        |
+        |    images = tf.image.encode_jpeg(tf.cast(model_input[0]*255, tf.uint8))
+        |    fwrite = tf.write_file(
+        |        tf.constant("output/model_input.jpeg"),
+        |        images
+        |    )
+        |    with tf.Session() as session:
+        |        session.run(fwrite)
         """.trimMargin()
     )
 }

tf-data-code/src/main/kotlin/edu/wpi/axon/tfdata/code/loss/DefaultLossToCode.kt

@@ -5,6 +5,7 @@ import edu.wpi.axon.tfdata.loss.Loss
 class DefaultLossToCode : LossToCode {
 
     override fun makeNewLoss(loss: Loss) = when (loss) {
+        is Loss.CategoricalCrossentropy -> "tf.keras.losses.categorical_crossentropy"
         is Loss.SparseCategoricalCrossentropy -> "tf.keras.losses.sparse_categorical_crossentropy"
         is Loss.MeanSquaredError -> "tf.keras.losses.mean_squared_error"
     }

tf-data-code/src/test/kotlin/edu/wpi/axon/tfdata/code/loss/DefaultLossToCodeTest.kt

@@ -21,6 +21,10 @@ internal class DefaultLossToCodeTest {
         @JvmStatic
         @Suppress("unused")
         fun lossSource() = listOf(
+            Arguments.of(
+                Loss.CategoricalCrossentropy,
+                """tf.keras.losses.categorical_crossentropy"""
+            ),
             Arguments.of(
                 Loss.SparseCategoricalCrossentropy,
                 """tf.keras.losses.sparse_categorical_crossentropy"""

tf-data/src/main/kotlin/edu/wpi/axon/tfdata/loss/Loss.kt

@@ -7,6 +7,9 @@ import kotlinx.serialization.json.JsonConfiguration
 @Serializable
 sealed class Loss {
 
+    @Serializable
+    object CategoricalCrossentropy : Loss()
+
     @Serializable
     object SparseCategoricalCrossentropy : Loss()
 

ui-javafx/src/main/kotlin/edu/wpi/axon/ui/main/FrontendKoinModules.kt

@@ -19,7 +19,7 @@ import edu.wpi.axon.plugin.LoadTestDataPlugins.loadExampleDatasetPlugin
 import edu.wpi.axon.plugin.LocalPluginManager
 import edu.wpi.axon.plugin.Plugin
 import edu.wpi.axon.plugin.PluginManager
-import edu.wpi.axon.plugin.ProcessTestOutputPlugins.serializeModelOutputPlugin
+import edu.wpi.axon.plugin.ProcessTestOutputPlugins.classifyImageModelOutputPlugin
 import edu.wpi.axon.ui.JobLifecycleManager
 import edu.wpi.axon.ui.JobRunner
 import edu.wpi.axon.ui.ModelManager
@@ -122,7 +122,7 @@ fun defaultFrontendModule() = module {
     single(named(processTestOutputPluginManagerName)) {
         bindPluginManager(
             setOf(
-                serializeModelOutputPlugin
+                classifyImageModelOutputPlugin
             ),
             "axon-process-test-output-plugins",
             "process_test_output_plugin_cache.json"

ui-javafx/src/main/kotlin/edu/wpi/axon/ui/view/jobeditor/LossFragment.kt

@@ -20,6 +20,13 @@ class LossFragment : Fragment() {
             println("Loaded with loss type: ${job.lossType.value}")
             println("Loaded with loss: ${job.userLoss.value}")
             model = when (val loss = job.userLoss.value) {
+                is Loss.CategoricalCrossentropy -> {
+                    field {
+                        label("CategoricalCrossentropy has no data.")
+                    }
+                    object : ItemViewModel<Unit>() {}
+                }
+
                 is Loss.SparseCategoricalCrossentropy -> {
                     field {
                         label("SparseCategoricalCrossentropy has no data.")

ui-javafx/src/main/kotlin/edu/wpi/axon/ui/view/joblist/JobList.kt

@@ -135,7 +135,7 @@ class JobList : View() {
             userOldModelPath = modelSource,
             userDataset = Dataset.ExampleDataset.FashionMnist,
             userOptimizer = Optimizer.Adam(),
-            userLoss = Loss.SparseCategoricalCrossentropy,
+            userLoss = Loss.CategoricalCrossentropy,
             userMetrics = setOf("accuracy"),
             userEpochs = 1,
             userNewModel = modelManager.loadModel(modelSource),