From 00e266bb682d1361947541a593b11d9a7b2e7d33 Mon Sep 17 00:00:00 2001 From: jin-xiulang Date: Wed, 25 Oct 2023 11:31:34 +0800 Subject: [PATCH] fix a bug of summary --- .jenkins/test/config/dependent_packages.yaml | 2 +- mindarmour/fuzz_testing/model_coverage_metrics.py | 9 +++++++++ .../fuzz_testing/sensitivity_convergence_coverage.py | 3 +++ .../concept_drift/concept_drift_check_images.py | 2 ++ 4 files changed, 15 insertions(+), 1 deletion(-) diff --git a/.jenkins/test/config/dependent_packages.yaml b/.jenkins/test/config/dependent_packages.yaml index d44e582..5be4ef7 100644 --- a/.jenkins/test/config/dependent_packages.yaml +++ b/.jenkins/test/config/dependent_packages.yaml @@ -1,2 +1,2 @@ mindspore: - 'https://repo.mindspore.cn/mindspore/mindspore/version/202308/20230819/master_20230819182346_9f3ee1b5baa5d721a359fc1f57b7bf1b2d1db440/' + 'https://repo.mindspore.cn/mindspore/mindspore/version/202310/20231015/r2.2_20231015170323_9390851d80579584b8af398adcc0d3ceb0fb0950/' diff --git a/mindarmour/fuzz_testing/model_coverage_metrics.py b/mindarmour/fuzz_testing/model_coverage_metrics.py index c77a11d..f80ab10 100644 --- a/mindarmour/fuzz_testing/model_coverage_metrics.py +++ b/mindarmour/fuzz_testing/model_coverage_metrics.py @@ -17,6 +17,7 @@ Model-Test Coverage Metrics. from abc import abstractmethod from collections import defaultdict import math +import time import numpy as np from mindspore import Tensor @@ -84,6 +85,7 @@ class CoverageMetrics: dict, return a activate_table. """ self._model.predict(Tensor(data)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() if not layer_out: msg = 'User must use TensorSummary() operation to specify the middle layer of the model participating in ' \ @@ -113,6 +115,7 @@ class CoverageMetrics: for i in range(batches): inputs = train_dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() @@ -224,6 +227,7 @@ class NeuronCoverage(CoverageMetrics): for i in range(batches): inputs = dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() @@ -314,6 +318,7 @@ class TopKNeuronCoverage(CoverageMetrics): for i in range(batches): inputs = dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() @@ -407,6 +412,7 @@ class SuperNeuronActivateCoverage(CoverageMetrics): for i in range(batches): inputs = dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() @@ -499,6 +505,7 @@ class NeuronBoundsCoverage(SuperNeuronActivateCoverage): for i in range(batches): inputs = dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() @@ -535,6 +542,7 @@ class KMultisectionNeuronCoverage(SuperNeuronActivateCoverage): def _init_k_multisection_table(self, data): """ Initial the activate table.""" self._model.predict(Tensor(data)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() activate_section_table = defaultdict() for layer, value in layer_out.items(): @@ -606,6 +614,7 @@ class KMultisectionNeuronCoverage(SuperNeuronActivateCoverage): for i in range(batches): inputs = dataset[i * self.batch_size: (i + 1) * self.batch_size] self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): value = tensor.asnumpy() diff --git a/mindarmour/fuzz_testing/sensitivity_convergence_coverage.py b/mindarmour/fuzz_testing/sensitivity_convergence_coverage.py index 9f904d8..4582e48 100644 --- a/mindarmour/fuzz_testing/sensitivity_convergence_coverage.py +++ b/mindarmour/fuzz_testing/sensitivity_convergence_coverage.py @@ -14,6 +14,7 @@ """ Source code of SensitivityConvergenceCoverage class. """ +import time import numpy as np from mindspore import Tensor @@ -120,6 +121,7 @@ class SensitivityConvergenceCoverage(CoverageMetrics): if not self.sensitive_neuron_idx: self._get_sensitive_neruon_idx(dataset) self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): @@ -144,6 +146,7 @@ class SensitivityConvergenceCoverage(CoverageMetrics): inputs = check_numpy_param('dataset', dataset) self._model.predict(Tensor(inputs)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() for layer, tensor in layer_out.items(): tensor = tensor.asnumpy().reshape(tensor.shape[0], -1) diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_images.py b/mindarmour/reliability/concept_drift/concept_drift_check_images.py index 45581aa..e49f6aa 100644 --- a/mindarmour/reliability/concept_drift/concept_drift_check_images.py +++ b/mindarmour/reliability/concept_drift/concept_drift_check_images.py @@ -16,6 +16,7 @@ Out-of-Distribution detection module for images. """ import heapq +import time import numpy as np from sklearn.cluster import KMeans @@ -55,6 +56,7 @@ class OodDetector: numpy.ndarray, the data feature extracted by a certain neural layer. """ model.predict(Tensor(data)) + time.sleep(0.01) layer_out = _get_summary_tensor_data() return layer_out[layer].asnumpy() -- Gitee