wehub-resource-sync
106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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import pytest
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import torch
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from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnx
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from polygraphy.exception import PolygraphyException
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from polygraphy.logger import G_LOGGER
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from tests.models.meta import ONNX_MODELS
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class TestLoggerCallbacks:
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@pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys())
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def test_set_severity(self, sev):
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G_LOGGER.module_severity = sev
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class TestOnnxrtRunner:
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def test_can_name_runner(self):
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NAME = "runner"
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runner = OnnxrtRunner(None, name=NAME)
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assert runner.name == NAME
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def test_basic(self):
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model = ONNX_MODELS["identity"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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assert runner.is_active
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model.check_runner(runner)
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assert runner.last_inference_time() is not None
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assert not runner.is_active
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def test_torch_tensors(self):
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model = ONNX_MODELS["identity"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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arr = torch.ones((1, 1, 2, 2), dtype=torch.float32)
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outputs = runner.infer({"x": arr})
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assert isinstance(outputs["y"], torch.Tensor)
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assert torch.equal(outputs["y"], arr)
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@pytest.mark.serial
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def test_warn_if_impl_methods_called(self, check_warnings_on_runner_impl_methods):
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model = ONNX_MODELS["identity"]
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runner = OnnxrtRunner(SessionFromOnnx(model.loader))
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check_warnings_on_runner_impl_methods(runner)
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def test_shape_output(self):
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model = ONNX_MODELS["reshape"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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model.check_runner(runner)
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def test_dim_param_preserved(self):
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model = ONNX_MODELS["dim_param"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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input_meta = runner.get_input_metadata(use_numpy_dtypes=False)
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# In Polygraphy, we only use None to indicate a dynamic input dimension - not strings.
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assert len(input_meta) == 1
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for _, (_, shape) in input_meta.items():
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assert shape == ["dim0", 16, 128]
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@pytest.mark.parametrize(
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"names, err",
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[
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(["fake-input", "x"], "Extra inputs in"),
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(["fake-input"], "The following inputs were not found"),
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([], "The following inputs were not found"),
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],
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)
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def test_error_on_wrong_name_feed_dict(self, names, err):
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model = ONNX_MODELS["identity"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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with pytest.raises(PolygraphyException, match=err):
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runner.infer(
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{
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name: np.ones(shape=(1, 1, 2, 2), dtype=np.float32)
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for name in names
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}
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)
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def test_error_on_wrong_dtype_feed_dict(self):
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model = ONNX_MODELS["identity"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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with pytest.raises(PolygraphyException, match="unexpected dtype."):
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runner.infer({"x": np.ones(shape=(1, 1, 2, 2), dtype=np.int32)})
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def test_error_on_wrong_shape_feed_dict(self):
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model = ONNX_MODELS["identity"]
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with OnnxrtRunner(SessionFromOnnx(model.loader)) as runner:
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with pytest.raises(PolygraphyException, match="incompatible shape."):
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runner.infer({"x": np.ones(shape=(1, 1, 3, 2), dtype=np.float32)})
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