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5 changes: 3 additions & 2 deletions monai/networks/utils.py
Original file line number Diff line number Diff line change
Expand Up @@ -712,9 +712,10 @@ def convert_to_onnx(
onnx_model = onnx.load(f)

if do_constant_folding and polygraphy_imported:
from polygraphy.backend.onnx.loader import fold_constants
from polygraphy.backend.onnx.loader import fold_constants, save_onnx

fold_constants(onnx_model, size_threshold=constant_size_threshold)
onnx_model = fold_constants(onnx_model, size_threshold=constant_size_threshold)
save_onnx(onnx_model, f)

if verify:
if isinstance(inputs, dict):
Expand Down
10 changes: 7 additions & 3 deletions tests/test_trt_compile.py
Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def tearDown(self):
if current_device != self.gpu_device:
torch.cuda.set_device(self.gpu_device)

@unittest.skipUnless(torch_trt_imported, "torch_tensorrt is required")
# @unittest.skipUnless(torch_trt_imported, "torch_tensorrt is required")
def test_handler(self):
from ignite.engine import Engine

Expand All @@ -74,7 +74,7 @@ def test_handler(self):

with tempfile.TemporaryDirectory() as tempdir:
engine = Engine(lambda e, b: None)
args = {"method": "torch_trt"}
args = {"method": "onnx", "dynamic_batchsize": [1, 4, 8]}
TrtHandler(net1, tempdir + "/trt_handler", args=args).attach(engine)
engine.run([0] * 8, max_epochs=1)
self.assertIsNotNone(net1._trt_compiler)
Expand All @@ -86,7 +86,11 @@ def test_lists(self):
model = ListAdd().cuda()

with torch.no_grad(), tempfile.TemporaryDirectory() as tmpdir:
args = {"output_lists": [[-1], [2], []], "export_args": {"dynamo": False, "verbose": True}}
args = {
"output_lists": [[-1], [2], []],
"export_args": {"dynamo": False, "verbose": True},
"dynamic_batchsize": [1, 4, 8],
}
x = torch.randn(1, 16).to("cuda")
y = torch.randn(1, 16).to("cuda")
z = torch.randn(1, 16).to("cuda")
Expand Down
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