Test (#578)

Summary:
Pull Request resolved: #578

Adding an End to End test for inception v4 for both fp32 and quantized models.

As with inception v3, we currently don't have avg_pool2d because of a missing op name. (We do support mean.dim(-1, -2) which is the same). this is somethign we can add after mvp

Reviewed By: digantdesai

Differential Revision: D49850148

fbshipit-source-id: 52ccf8044c9bb0e540ddacdf90fb4510aeeb1dcd

Author: mcr229

backends/xnnpack/test/TARGETS

@@ -127,6 +127,7 @@ python_unittest(
     ]),
     tags = ["long_running"],
     deps = [
+        "fbsource//third-party/pypi/timm:timm",
         "fbsource//third-party/pypi/torchsr:torchsr",  # @manual
         "//caffe2:torch",
         "//executorch/backends/xnnpack/test/tester:tester",

backends/xnnpack/test/models/inception_v4.py

@@ -0,0 +1,65 @@
+# 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
+from executorch.backends.xnnpack.test.tester import Tester
+from timm.models import inception_v4
+
+
+class TestInceptionV4(unittest.TestCase):
+    ic4 = inception_v4(pretrained=False).eval()
+    model_inputs = (torch.ones(3, 299, 299).unsqueeze(0),)
+
+    all_operators = {
+        "executorch_exir_dialects_edge__ops_aten_addmm_default",
+        # "executorch.exir.dialects.edge._ops.aten.avg_pool2d.default", Currently do not have avg_pool2d partitioned
+        "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_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_ic4(self):
+
+        (
+            Tester(self.ic4, 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_ic4(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.ic4, 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()
+        )