Add mobilenet_v2 and resnet50 to the examples

backends/cadence/aot/functions.yaml

@@ -77,6 +77,16 @@
     - arg_meta: null
       kernel_name: torch::executor::gelu_out
 
+- op: hardtanh.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::hardtanh_out
+
+- op: max_pool2d_with_indices.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::max_pool2d_with_indices_out
+
 - op: mean.out
   kernels:
     - arg_meta: null

backends/cadence/aot/functions_hifi.yaml

@@ -62,11 +62,26 @@
     - arg_meta: null
       kernel_name: torch::executor::full_out
 
+- op: gelu.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::gelu_out
+
+- op: hardtanh.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::hardtanh_out
+
+- op: max_pool2d_with_indices.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::max_pool2d_with_indices_out
+
 - op: mean.out
   kernels:
     - arg_meta: null
-      kernel_name: cadence::impl::HiFi::mean_dim_out   
-      
+      kernel_name: cadence::impl::HiFi::mean_dim_out
+
 - op: mul.out
   kernels:
     - arg_meta: null

backends/cadence/hifi/operators/CMakeLists.txt

@@ -33,6 +33,9 @@ set(_aten_ops__srcs
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_clone.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_embedding.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_full.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_gelu.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_hardtanh.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_max_pool2d_with_indices.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_permute_copy.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_slice_copy.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_softmax.cpp"

backends/cadence/reference/operators/CMakeLists.txt

@@ -39,6 +39,8 @@ set(_aten_ops__srcs
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_cat.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_clone.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_div.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_hardtanh.cpp"
+    "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_max_pool2d_with_indices.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_mean.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_mul.cpp"
     "${EXECUTORCH_ROOT}/kernels/portable/cpu/op_permute_copy.cpp"

examples/cadence/models/mobilenet_v2.py

@@ -0,0 +1,30 @@
+# 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.
+
+# Example script for exporting simple models to flatbuffer
+
+import logging
+
+import torch
+
+from executorch.backends.cadence.aot.ops_registrations import *  # noqa
+
+
+from executorch.backends.cadence.aot.export_example import export_model
+from torchvision.models import mobilenet_v2, MobileNet_V2_Weights
+
+
+FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+
+
+if __name__ == "__main__":
+
+    model = mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT)
+    model.eval()
+    example_inputs = (torch.randn(1, 3, 64, 64),)
+
+    export_model(model, example_inputs)

examples/cadence/models/resnet50.py

@@ -0,0 +1,30 @@
+# 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.
+
+# Example script for exporting simple models to flatbuffer
+
+import logging
+
+import torch
+
+from executorch.backends.cadence.aot.ops_registrations import *  # noqa
+
+
+from executorch.backends.cadence.aot.export_example import export_model
+from torchvision.models import resnet50, ResNet50_Weights
+
+
+FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+
+
+if __name__ == "__main__":
+
+    model = resnet50(weights=ResNet50_Weights.DEFAULT)
+    model.eval()
+    example_inputs = (torch.randn(1, 3, 64, 64),)
+
+    export_model(model, example_inputs)