trevor-m commented on a change in pull request #6955:
URL: https://github.com/apache/tvm/pull/6955#discussion_r532764867
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File path: tests/python/contrib/test_tensorrt.py
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@@ -1034,5 +1039,119 @@ def set_func_attr(func, compile_name, symbol_name):
tvm.ir.assert_structural_equal(mod_trt, mod_exp, map_free_vars=True)
+def convert_traced_model_to_vm_trt(
+ traced_module: torch.jit.TopLevelTracedModule, np_sample_input:
np.ndarray, target: str
+) -> tvm.runtime.vm.Executable:
+ """
+ This function converts a traced pytorch model to VM + TRT.
+ """
+ input_shape = np_sample_input.shape
+ input_name = "input0"
+ shape_list = [(input_name, input_shape)]
+ mod, params = relay.frontend.from_pytorch(traced_module, shape_list)
+ mod, config = tensorrt.partition_for_tensorrt(mod, params,
remove_no_mac_subgraphs=True)
+ with tvm.transform.PassContext(opt_level=3,
disabled_pass=["FoldScaleAxis"]):
+ vm_trt_exec = relay.vm.compile(mod, target=target, params=params)
+
+ return vm_trt_exec
+
+
+def test_maskrcnn_resnet50() -> None:
Review comment:
What properties of this particular model are we trying to test here? Can
we make a simple test case which replicates the same behavior instead - i.e.
single conv2d op with dynamic batch size in TRT.
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