untest resnet (too expensive for regular test)

feiwang3311 · Oct 10, 2018 · 536e48d · 536e48d
1 parent 1d2394c
commit 536e48d
Showing 1 changed file with 61 additions and 61 deletions.
diff --git a/src/test/scala/lantern/test_onnx.scala b/src/test/scala/lantern/test_onnx.scala
@@ -508,65 +508,65 @@ class ONNXTest extends LanternFunSuite {
     runTest(training_func)
   }
 
-  test("resnet_inference") {
-
-    val model_file = model_file_all("resnet50")
-    val model_dir = model_dir_all("resnet50")
-    System.out.println(s"testing reading ONNX model using library from $model_file")
-
-    val inference_func = new LanternDriverC[String, Unit] with ONNXLib {
-
-      @virtualize
-      def snippet(a: Rep[String]): Rep[Unit] = {
-        val model = readONNX(model_file)
-        val (func, x_dims) = (model.inference_func, model.x_dims)
-
-        // get test data as TensorProto
-        val input_file = model_dir + "test_data_set_0/input_0.pb"
-        val output_file = model_dir + "test_data_set_0/output_0.pb"
-        val input = readTensor(input_file).tensor
-        val output = readTensor(output_file).tensor
-        val output1 = func(input)
-        Tensor.assertEqual(output, output1)
-      }
-    }
-    val resnet_file = new PrintWriter(new File(gene_dir + "resnet.cpp"))
-    resnet_file.println(inference_func.code)
-    resnet_file.flush()
-    runTest(inference_func)
-  }
-
-  test("resnet_training") {
-
-    val model_file = model_file_all("resnet50")
-    val model_dir = model_dir_all("resnet50")
-    System.out.println(s"testing reading ONNX model using library from $model_file for training")
-
-    val training_func = new LanternDriverC[String, Unit] with ONNXLib {
-
-      @virtualize
-      def snippet(a: Rep[String]): Rep[Unit] = {
-        val model = readONNX(model_file)
-        val (func, x_dims, y_dims) = (model.training_func, model.x_dims, model.y_dims)
-
-        // fake input and target
-        val input_file = model_dir + "test_data_set_0/input_0.pb"
-        val output_file = model_dir + "test_data_set_0/output_0.pb"
-        val input = readTensor(input_file).tensor
-        val output = readTensor(output_file).tensor
-
-        val target = NewArray[Int](x_dims(0))
-        for (i <- DataLoop(x_dims(0))) target(i) = 1
-        def lossFun(dummy: TensorR) = func(TensorR(input)).nllLossB(target).sum()
-
-        val loss = gradR_loss(lossFun)(Tensor.zeros(1))
-        println(loss.data(0))
-      }
-    }
-
-    val resnet_file = new PrintWriter(new File(gene_dir + "resnetTraining.cpp"))
-    resnet_file.println(training_func.code)
-    resnet_file.flush()
-    // runTest(training_func)
-  }
+  // test("resnet_inference") {
+
+  //   val model_file = model_file_all("resnet50")
+  //   val model_dir = model_dir_all("resnet50")
+  //   System.out.println(s"testing reading ONNX model using library from $model_file")
+
+  //   val inference_func = new LanternDriverC[String, Unit] with ONNXLib {
+
+  //     @virtualize
+  //     def snippet(a: Rep[String]): Rep[Unit] = {
+  //       val model = readONNX(model_file)
+  //       val (func, x_dims) = (model.inference_func, model.x_dims)
+
+  //       // get test data as TensorProto
+  //       val input_file = model_dir + "test_data_set_0/input_0.pb"
+  //       val output_file = model_dir + "test_data_set_0/output_0.pb"
+  //       val input = readTensor(input_file).tensor
+  //       val output = readTensor(output_file).tensor
+  //       val output1 = func(input)
+  //       Tensor.assertEqual(output, output1)
+  //     }
+  //   }
+  //   val resnet_file = new PrintWriter(new File(gene_dir + "resnet.cpp"))
+  //   resnet_file.println(inference_func.code)
+  //   resnet_file.flush()
+  //   runTest(inference_func)
+  // }
+
+  // test("resnet_training") {
+
+  //   val model_file = model_file_all("resnet50")
+  //   val model_dir = model_dir_all("resnet50")
+  //   System.out.println(s"testing reading ONNX model using library from $model_file for training")
+
+  //   val training_func = new LanternDriverC[String, Unit] with ONNXLib {
+
+  //     @virtualize
+  //     def snippet(a: Rep[String]): Rep[Unit] = {
+  //       val model = readONNX(model_file)
+  //       val (func, x_dims, y_dims) = (model.training_func, model.x_dims, model.y_dims)
+
+  //       // fake input and target
+  //       val input_file = model_dir + "test_data_set_0/input_0.pb"
+  //       val output_file = model_dir + "test_data_set_0/output_0.pb"
+  //       val input = readTensor(input_file).tensor
+  //       val output = readTensor(output_file).tensor
+
+  //       val target = NewArray[Int](x_dims(0))
+  //       for (i <- DataLoop(x_dims(0))) target(i) = 1
+  //       def lossFun(dummy: TensorR) = func(TensorR(input)).nllLossB(target).sum()
+
+  //       val loss = gradR_loss(lossFun)(Tensor.zeros(1))
+  //       println(loss.data(0))
+  //     }
+  //   }
+
+  //   val resnet_file = new PrintWriter(new File(gene_dir + "resnetTraining.cpp"))
+  //   resnet_file.println(training_func.code)
+  //   resnet_file.flush()
+  //   // runTest(training_func)
+  // }
 }