96 lines
2.3 KiB
Python
96 lines
2.3 KiB
Python
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import os
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import onnx
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import pytest
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import torch
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from mmdeploy.apis.utils import attribute_to_dict
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from mmdeploy.core import mark
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output_file = 'test_mark.onnx'
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@pytest.fixture(autouse=True)
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def clear_work_dir_after_test():
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# clear tmp output before test
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if os.path.exists(output_file):
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os.remove(output_file)
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yield
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# clear tmp output after test
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if os.path.exists(output_file):
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os.remove(output_file)
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def test_mark():
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@mark('add', inputs=['a', 'b'], outputs='c')
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def add(x, y):
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return torch.add(x, y)
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class TestModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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return add(x, y)
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model = TestModel().eval()
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# dummy input
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x = torch.rand(2, 3, 4)
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y = torch.rand(2, 3, 4)
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torch.onnx.export(model, (x, y), output_file)
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onnx_model = onnx.load(output_file)
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nodes = onnx_model.graph.node
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assert nodes[0].op_type == 'Mark'
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assert nodes[0].domain == 'mmcv'
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assert attribute_to_dict(nodes[0].attribute) == dict(
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func='add', id=0, type='input', name='a')
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assert nodes[1].op_type == 'Mark'
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assert nodes[1].domain == 'mmcv'
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assert attribute_to_dict(nodes[1].attribute) == dict(
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func='add', id=1, type='input', name='b')
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assert nodes[2].op_type == 'Add'
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assert nodes[3].op_type == 'Mark'
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assert nodes[3].domain == 'mmcv'
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assert attribute_to_dict(nodes[3].attribute) == dict(
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func='add', id=0, type='output', name='c')
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def test_extract():
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from mmdeploy.apis import extract_model
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@mark('add', outputs='z')
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def add(x, y):
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return torch.add(x, y)
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class TestModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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return add(x, y)
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model = TestModel().eval()
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# dummy input
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x = torch.rand(2, 3, 4)
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y = torch.rand(2, 3, 4)
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torch.onnx.export(model, (x, y), output_file)
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onnx_model = onnx.load(output_file)
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extracted = extract_model(onnx_model, 'add:input', 'add:output')
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assert extracted.graph.input[0].name == 'x'
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assert extracted.graph.input[1].name == 'y'
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assert extracted.graph.output[0].name == 'z'
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assert extracted.graph.node[0].op_type == 'Add'
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