kazum commented on a change in pull request #5073: [Relay][Frontend][ONNX]
operator support NonZero
URL: https://github.com/apache/incubator-tvm/pull/5073#discussion_r394006951
##########
File path: tests/python/frontend/onnx/test_forward.py
##########
@@ -30,22 +30,54 @@
import scipy
-def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None,
output_dtype='float32', opset=None):
- """ Generic function to execute and get tvm output"""
- target = 'llvm'
+def get_input_data_shape_dict(graph_def, input_data):
if isinstance(input_data, list):
input_names = {}
shape_dict = {}
- dtype_dict = {}
for i, _ in enumerate(input_data):
input_names[i] = graph_def.graph.input[i].name
shape_dict[input_names[i]] = input_data[i].shape
- dtype_dict[input_names[i]] = input_data[i].dtype
else:
input_names = graph_def.graph.input[0].name
shape_dict = {input_names: input_data.shape}
+
+ return input_names, shape_dict
+
+
+def get_input_data_dtype_dict(graph_def, input_data):
+ if isinstance(input_data, list):
+ input_names = {}
+ dtype_dict = {}
+ for i, _ in enumerate(input_data):
+ input_names[i] = graph_def.graph.input[i].name
+ dtype_dict[input_names[i]] = input_data[i].dtype
+ else:
+ input_names = graph_def.graph.input[0].name
dtype_dict = {input_names: input_data.dtype}
+ return input_names, dtype_dict
+
+
+def get_tvm_output_with_vm(graph_def, input_data, target, ctx,
+ opset=None):
+ """ Generic function to execute and get tvm output with vm executor"""
+
+ _, shape_dict = get_input_data_shape_dict(graph_def, input_data)
+
+ mod, params = relay.frontend.from_onnx(graph_def, shape_dict, opset=opset)
+
+ ex = relay.create_executor('vm', mod=mod, ctx=ctx, target=target)
+ indata = tvm.nd.array(input_data)
+ result = ex.evaluate()(indata)
+ return result.asnumpy()
+
+
+def get_tvm_output(graph_def, input_data, target, ctx, output_shape=None,
output_dtype='float32', opset=None):
Review comment:
How about using vm for all the tests?
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