Test (#577)

Summary:
Pull Request resolved: #577

Test for inception v3 model both fp32 and quantized

Currently we don't have a node visitor for avg_pool2d, so we omitt it. This shouldn't be hard to add, but seems like just a node name that we are missing. We can add this after mvp

Reviewed By: digantdesai

Differential Revision: D49850153

fbshipit-source-id: e1f22188093d282362e71f5b11a9b1eed4faff10

Author: mcr229

backends/xnnpack/test/models/inception_v3.py

@@ -0,0 +1,67 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import torch
+import torchvision.models as models
+from executorch.backends.xnnpack.test.tester import Tester
+
+
+class TestInceptionV3(unittest.TestCase):
+    # pyre-ignore
+    ic3 = models.inception_v3(weights="IMAGENET1K_V1").eval()  # noqa
+    model_inputs = (torch.ones(1, 3, 224, 224),)
+
+    all_operators = {
+        "executorch_exir_dialects_edge__ops_aten_addmm_default",
+        "executorch_exir_dialects_edge__ops_aten_add_Tensor",
+        "executorch_exir_dialects_edge__ops_aten_cat_default",
+        "executorch_exir_dialects_edge__ops_aten_convolution_default",
+        "executorch_exir_dialects_edge__ops_aten_max_pool2d_with_indices_default",
+        # "executorch.exir.dialects.edge._ops.aten.avg_pool2d.default", Currently do not have avg_pool2d partitioned
+        "executorch_exir_dialects_edge__ops_aten_mean_dim",
+        "executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
+        "executorch_exir_dialects_edge__ops_aten_permute_copy_default",
+        "executorch_exir_dialects_edge__ops_aten_relu_default",
+    }
+
+    def test_fp32_ic3(self):
+
+        (
+            Tester(self.ic3, self.model_inputs)
+            .export()
+            .to_edge()
+            .check(list(self.all_operators))
+            .partition()
+            .check(["torch.ops.executorch_call_delegate"])
+            .check_not(list(self.all_operators))
+            .to_executorch()
+            .serialize()
+            .run_method()
+            .compare_outputs()
+        )
+
+    def test_qs8_ic3(self):
+        # Quantization fuses away batchnorm, so it is no longer in the graph
+        ops_after_quantization = self.all_operators - {
+            "executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
+        }
+
+        (
+            Tester(self.ic3, self.model_inputs)
+            .quantize()
+            .export()
+            .to_edge()
+            .check(list(ops_after_quantization))
+            .partition()
+            .check(["torch.ops.executorch_call_delegate"])
+            .check_not(list(ops_after_quantization))
+            .to_executorch()
+            .serialize()
+            .run_method()
+            .compare_outputs()
+        )