151 lines
5.3 KiB
Python
151 lines
5.3 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import os
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import pytest
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import numpy as np
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import ngraph as ng
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import tests_compatibility
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from pathlib import Path
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def model_path(is_fp16=False):
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base_path = os.path.dirname(__file__)
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if is_fp16:
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test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.xml")
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test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp16.bin")
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else:
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test_xml = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.xml")
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test_bin = os.path.join(base_path, "test_utils", "utils", "test_model_fp32.bin")
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return (test_xml, test_bin)
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def model_onnx_path():
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base_path = os.path.dirname(__file__)
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test_onnx = os.path.join(base_path, "test_utils", "utils", "test_model.onnx")
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return test_onnx
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def plugins_path():
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base_path = os.path.dirname(__file__)
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plugins_xml = os.path.join(base_path, "test_utils", "utils", "plugins.xml")
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plugins_win_xml = os.path.join(base_path, "test_utils", "utils", "plugins_win.xml")
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plugins_osx_xml = os.path.join(base_path, "test_utils", "utils", "plugins_apple.xml")
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return (plugins_xml, plugins_win_xml, plugins_osx_xml)
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def _get_default_model_zoo_dir():
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return Path(os.getenv("ONNX_HOME", Path.home() / ".onnx/model_zoo"))
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def pytest_addoption(parser):
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parser.addoption(
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"--backend",
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default="CPU",
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choices=["CPU", "GPU", "GNA", "HETERO", "TEMPLATE"],
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help="Select target device",
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)
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parser.addoption(
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"--model_zoo_dir",
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default=_get_default_model_zoo_dir(),
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type=str,
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help="location of the model zoo",
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)
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parser.addoption(
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"--model_zoo_xfail",
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action="store_true",
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help="treat model zoo known issues as xfails instead of failures",
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)
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def pytest_configure(config):
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backend_name = config.getvalue("backend")
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tests_compatibility.BACKEND_NAME = backend_name
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tests_compatibility.MODEL_ZOO_DIR = Path(config.getvalue("model_zoo_dir"))
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tests_compatibility.MODEL_ZOO_XFAIL = config.getvalue("model_zoo_xfail")
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# register additional markers
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config.addinivalue_line("markers", "skip_on_cpu: Skip test on CPU")
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config.addinivalue_line("markers", "skip_on_gpu: Skip test on GPU")
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config.addinivalue_line("markers", "skip_on_gna: Skip test on GNA")
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config.addinivalue_line("markers", "skip_on_hetero: Skip test on HETERO")
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config.addinivalue_line("markers", "skip_on_template: Skip test on TEMPLATE")
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config.addinivalue_line("markers", "onnx_coverage: Collect ONNX operator coverage")
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config.addinivalue_line("markers", "template_plugin")
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config.addinivalue_line("markers", "dynamic_library: Runs tests only in dynamic libraries case")
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def pytest_collection_modifyitems(config, items):
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backend_name = config.getvalue("backend")
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tests_compatibility.MODEL_ZOO_DIR = Path(config.getvalue("model_zoo_dir"))
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tests_compatibility.MODEL_ZOO_XFAIL = config.getvalue("model_zoo_xfail")
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keywords = {
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"CPU": "skip_on_cpu",
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"GPU": "skip_on_gpu",
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"GNA": "skip_on_gna",
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"HETERO": "skip_on_hetero",
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"TEMPLATE": "skip_on_template",
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}
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skip_markers = {
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"CPU": pytest.mark.skip(reason="Skipping test on the CPU backend."),
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"GPU": pytest.mark.skip(reason="Skipping test on the GPU backend."),
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"GNA": pytest.mark.skip(reason="Skipping test on the GNA backend."),
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"HETERO": pytest.mark.skip(reason="Skipping test on the HETERO backend."),
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"TEMPLATE": pytest.mark.skip(reason="Skipping test on the TEMPLATE backend."),
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}
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for item in items:
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skip_this_backend = keywords[backend_name]
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if skip_this_backend in item.keywords:
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item.add_marker(skip_markers[backend_name])
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@pytest.fixture(scope="session")
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def device():
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return os.environ.get("TEST_DEVICE") if os.environ.get("TEST_DEVICE") else "CPU"
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def create_encoder(input_shape, levels=4):
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# input
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input_node = ng.parameter(input_shape, np.float32, name="data")
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padding_begin = padding_end = [0, 0]
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strides = [1, 1]
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dilations = [1, 1]
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input_channels = [input_shape[1]]
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last_output = input_node
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# convolution layers
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for _ in range(levels):
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input_c = input_channels[-1]
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output_c = input_c * 2
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conv_w = np.random.uniform(0, 1, [output_c, input_c, 5, 5]).astype(np.float32)
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conv_node = ng.convolution(last_output, conv_w, strides, padding_begin, padding_end, dilations)
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input_channels.append(output_c)
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last_output = conv_node
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# deconvolution layers
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for _ in range(levels):
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input_c = input_channels[-2]
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output_c = input_channels.pop(-1)
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deconv_w = np.random.uniform(0, 1, [output_c, input_c, 5, 5]).astype(np.float32)
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deconv_node = ng.convolution_backprop_data(last_output, deconv_w, strides)
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last_output = deconv_node
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# result
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last_output.set_friendly_name("out")
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result_node = ng.result(last_output)
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return ng.Function(result_node, [input_node], "Encoder")
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def create_relu(input_shape):
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input_shape = ng.impl.PartialShape(input_shape)
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param = ng.parameter(input_shape, dtype=np.float32, name="data")
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result = ng.relu(param, name="out")
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function = ng.Function(result, [param], "TestFunction")
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return function
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