Fix torchscript_resnet18_e2e.py and resnet_inference.ipynb

Fix the tests to run with refbackend.

examples/torchscript_resnet18_e2e.py

@@ -7,17 +7,15 @@ import requests
 import torch
 import torchvision.models as models
 from torchvision import transforms
-import typing
 
-import torch_mlir
+from torch_mlir.dialects.torch.importer.jit_ir import ClassAnnotator, ModuleBuilder
 
 import npcomp
 from npcomp.passmanager import PassManager
 from npcomp.compiler.pytorch.backend import refbackend
 from npcomp.compiler.utils import logging
 
-mb = torch_mlir.ModuleBuilder()
-
+mb = ModuleBuilder()
 
 def load_and_preprocess_image(url: str):
     headers = {
@@ -58,7 +56,7 @@ def predictions(torch_func, jit_func, img, labels):
     golden_prediction = top3_possibilities(torch_func(img))
     print("PyTorch prediction")
     print(golden_prediction)
-    prediction = top3_possibilities(torch.from_numpy(jit_func(img)))
+    prediction = top3_possibilities(torch.from_numpy(jit_func(img.numpy())))
     print("NPCOMP prediction")
     print(prediction)
 
@@ -92,7 +90,7 @@ img = load_and_preprocess_image(image_url)
 labels = load_labels()
 
 test_module = TestModule()
-class_annotator = torch_mlir.ClassAnnotator()
+class_annotator = ClassAnnotator()
 recursivescriptmodule = torch.jit.script(test_module)
 torch.jit.save(recursivescriptmodule, "/tmp/foo.pt")
 
@@ -110,8 +108,13 @@ class_annotator.annotateArgs(
 mb.import_module(recursivescriptmodule._c, class_annotator)
 
 backend = refbackend.RefBackendNpcompBackend()
-PassManager.parse("torchscript-to-npcomp-backend-pipeline").run(mb.module)
-compiled = backend.compile(mb.module)
+with npcomp.ir.Context() as ctx:
+    npcomp.register_all_dialects(ctx)
+    lowered_mlir_module = npcomp.ir.Module.parse(str(mb.module))
+    pm = PassManager.parse('torchscript-to-npcomp-backend-pipeline')
+    pm.run(lowered_mlir_module)
+
+compiled = backend.compile(lowered_mlir_module)
 jit_module = backend.load(compiled)
 
 predictions(test_module.forward, jit_module.forward, img, labels)