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| # Copyright (c) 2024 Intel Corporation | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| """ | ||
| This script generates a summary table in Markdown format from an XML report generated by pytest. | ||
| Usage in GitHub workflow: | ||
| - name: Test Summary | ||
| if: ${{ !cancelled() }} | ||
| run: | | ||
| python .github/scripts/generate_examples_summary.py pytest-results.xml >> $GITHUB_STEP_SUMMARY | ||
| """ | ||
|
|
||
| import sys | ||
| import xml.etree.ElementTree as ET | ||
|
|
||
| # Load the XML report generated by pytest | ||
| xml_file = sys.argv[1] | ||
|
|
||
| try: | ||
| tree = ET.parse(xml_file) | ||
| except FileNotFoundError: | ||
| sys.exit(1) | ||
|
|
||
| root = tree.getroot() | ||
|
|
||
| # Build the summary table in Markdown format | ||
| table_lines = [] | ||
| table_lines.append("| Test Name | Status | Time | Message |") | ||
| table_lines.append("|:----------|:------:|-----:|:--------|") | ||
|
|
||
| # Iterate over test cases | ||
| for testcase in root.findall(".//testcase"): | ||
| test_name = testcase.get("name") | ||
| time_duration = float(testcase.get("time", "0")) | ||
| message = "" | ||
| if testcase.find("failure") is not None: | ||
| status = "$${\color{red}Failed}$$" | ||
| message = testcase.find("failure").get("message", "") | ||
| elif testcase.find("error") is not None: | ||
| status = "$${\color{red}Error}$$" | ||
| elif testcase.find("skipped") is not None: | ||
| status = "$${\color{orange}Skipped}$$" | ||
| message = testcase.find("skipped").get("message", "") | ||
| else: | ||
| status = "$${\color{green}Ok}$$" | ||
|
|
||
| # Append each row to the table | ||
| table_lines.append(f"| {test_name} | {status} | {time_duration:.0f} | {message} |") | ||
|
|
||
| print("\n".join(table_lines)) |
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77 changes: 77 additions & 0 deletions
77
tests/cross_fw/test_templates/test_weights_compression_backends.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,77 @@ | ||
| # Copyright (c) 2024 Intel Corporation | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| from abc import abstractmethod | ||
|
|
||
| import pytest | ||
|
|
||
| from nncf.experimental.common.tensor_statistics.collectors import HAWQAggregator | ||
| from nncf.experimental.common.tensor_statistics.collectors import MaxVarianceReducer | ||
| from nncf.experimental.common.tensor_statistics.collectors import MeanAbsMaxReducer | ||
| from nncf.experimental.common.tensor_statistics.collectors import MeanAggregator | ||
| from nncf.experimental.common.tensor_statistics.collectors import MeanVarianceReducer | ||
| from nncf.experimental.common.tensor_statistics.collectors import NoopReducer | ||
| from nncf.experimental.common.tensor_statistics.collectors import TensorCollector | ||
|
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||
|
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||
| class TemplateTestMixedPrecisionAlgoBackend: | ||
| @abstractmethod | ||
| def get_hawq_with_backend(self, subset_size: int): | ||
| """Returns a HAWQ instance of the algorithm.""" | ||
|
|
||
| @abstractmethod | ||
| def get_mean_variance_with_backend(self, subset_size: int): | ||
| """Returns a Mean Variance instance of the algorithm.""" | ||
|
|
||
| @abstractmethod | ||
| def get_max_variance_with_backend(self, subset_size: int): | ||
| """Returns a Max Variance instance of the algorithm.""" | ||
|
|
||
| @abstractmethod | ||
| def get_mean_max_with_backend(self, subset_size: int): | ||
| """Returns a Mean Max instance of the algorithm.""" | ||
|
|
||
| def check_aggregator(self, collector: TensorCollector, expected_aggregator_type, subset_size: int): | ||
| assert len(collector.aggregators) == 1, "Collector should have exactly one aggregator." | ||
| _, aggregator = collector.aggregators.popitem() | ||
| assert isinstance( | ||
| aggregator, expected_aggregator_type | ||
| ), f"Expected aggregator of type {expected_aggregator_type.__name__}, got {type(aggregator).__name__}." | ||
| assert aggregator.num_samples == subset_size, "Aggregator num_samples does not match the provided subset size." | ||
|
|
||
| def check_reducer(self, collector: TensorCollector, expected_reducer_type): | ||
| assert len(collector.reducers) == 1 | ||
| reducer = collector.reducers.pop() | ||
| assert isinstance( | ||
| reducer, expected_reducer_type | ||
| ), f"Expected reducer of type {expected_reducer_type.__name__}, got {type(reducer).__name__}." | ||
|
|
||
| @pytest.mark.parametrize("subset_size", [1, 10, None]) | ||
| @pytest.mark.parametrize( | ||
| "algo_func, aggregator_type, reducer_type", | ||
| [ | ||
| ("get_hawq_with_backend", HAWQAggregator, NoopReducer), | ||
| ("get_mean_variance_with_backend", MeanAggregator, MeanVarianceReducer), | ||
| ("get_max_variance_with_backend", MeanAggregator, MaxVarianceReducer), | ||
| ("get_mean_max_with_backend", MeanAggregator, MeanAbsMaxReducer), | ||
| ], | ||
| ) | ||
| def test_statistic_collector(self, subset_size, algo_func, aggregator_type, reducer_type): | ||
| """Test function to validate statistic collectors.""" | ||
| algo = getattr(self, algo_func)(subset_size) | ||
| collector = algo._get_statistic_collector() | ||
|
|
||
| # Verify the collector instance and properties | ||
| assert isinstance(collector, TensorCollector), "Collector is not an instance of TensorCollector." | ||
|
|
||
| # Validate the aggregator and reducer types | ||
| self.check_aggregator(collector, aggregator_type, subset_size) | ||
| self.check_reducer(collector, reducer_type) |
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