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# Owner(s): ["module: onnx"]
from __future__ import annotations
import onnx
import onnx.inliner
import pytorch_test_common
import torch
from torch.testing._internal import common_utils
def assert_op_in_onnx_model(model: onnx.ModelProto, op_type: str):
inlined = onnx.inliner.inline_local_functions(model)
for node in inlined.graph.node:
if node.op_type == op_type:
return
raise AssertionError(f"Op {op_type} not found in model")
class TestDynamoExportDecompSkip(pytorch_test_common.ExportTestCase):
def test_upsample_bilinear2d(self):
class TestModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.upsample = torch.nn.Upsample(scale_factor=2, mode="bilinear")
def forward(self, x):
return self.upsample(x)
onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2))
# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
def test_upsample_bilinear2d_output_size(self):
def func(x: torch.Tensor):
return torch.nn.functional.interpolate(x, size=(4, 4), mode="bilinear")
onnx_program = torch.onnx.dynamo_export(func, torch.randn(1, 1, 2, 2))
# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
def test_upsample_trilinear3d(self):
class TestModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.upsample = torch.nn.Upsample(scale_factor=2, mode="trilinear")
def forward(self, x):
return self.upsample(x)
onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2, 3))
# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
def test_upsample_trilinear3d_output_size(self):
def func(x: torch.Tensor):
return torch.nn.functional.interpolate(x, size=(4, 4, 4), mode="trilinear")
onnx_program = torch.onnx.dynamo_export(func, torch.randn(1, 1, 2, 2, 3))
# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
def test_instance_norm(self):
class TestModel(torch.nn.Module):
def forward(self, x):
return torch.nn.functional.instance_norm(x)
onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2))
# If decomposition is skipped, the model will contain an InstanceNormalization op
# instead of BatchNormalization op w/ training=True.
assert_op_in_onnx_model(onnx_program.model_proto, "InstanceNormalization")
if __name__ == "__main__":
common_utils.run_tests()
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