def postProcess(experiment):
if not isinstance(experiment, Experiment):
experiment = Experiment(experiment)
inferenceDir = experiment.getInferenceDirectory()
# Use level1 rather than level1_sp because the latter
# is still in learning mode
# use "all" rather than level1 because level1_sp is not created by default
experiment.loadNetwork("all")
net = experiment.network
# Analyze the trained network and extract the auxiliary data structures
netInfo = _analyzeTrainedNet(net=net, options=dict())
# ------------------------------------------------------------------------
# Get the list of tests, and the list of baselines required
testNames = [test['name'] for test in experiment.description['infer']]
testOptions = [test.get('ppOptions', dict()) for test in experiment.description['infer']]
standaloneTests = list(testNames)
options = dict(zip(testNames, testOptions))
baselineTestNames = set()
for testName in testNames:
(datasetName, testVariant) = testName.split('_')
if testVariant == 'baseline':
baselineTestNames.add(testName)
# ------------------------------------------------------------------------
# For each baseline, train the classifier, and then run each variant that uses
# that baseline
for baselineTestName in baselineTestNames:
(baseDatasetName, baseVariantName) = baselineTestName.split('_')
assert(baseVariantName == 'baseline')
standaloneTests.remove(baselineTestName)
baseline = _analyzeTestRun(experiment, netInfo=netInfo,
options=options[baselineTestName],
datasetName=baseDatasetName, testVariantName=baseVariantName,
baseline=None)
# Now, run each variant that uses this baseline
for testName in testNames:
(datasetName, variantName) = testName.split('_')
if datasetName != baseDatasetName or variantName == 'baseline':
continue
standaloneTests.remove(testName)
_analyzeTestRun(experiment, netInfo=netInfo, options=options[testName],
datasetName=datasetName, testVariantName=variantName,
baseline=baseline)
# Run each standalone test
for testName in standaloneTests:
(datasetName, variantName) = testName.split('_')
_analyzeTestRun(experiment, netInfo=netInfo, options=options[testName],
datasetName=datasetName, testVariantName=variantName,
baseline=None)
def postProcess(experiment):
if not isinstance(experiment, Experiment):
experiment = Experiment(experiment)
inferenceDir = experiment.getInferenceDirectory()
# Use level1 rather than level1_sp because the latter
# is still in learning mode
# use "all" rather than level1 because level1_sp is not created by default
experiment.loadNetwork("all")
net = experiment.network
# Analyze the trained network and extract the auxiliary data structures
netInfo = _analyzeTrainedNet(net=net, options=dict())
# ------------------------------------------------------------------------
# Get the list of tests, and the list of baselines required
testNames = [test['name'] for test in experiment.description['infer']]
testOptions = [
test.get('ppOptions', dict())
for test in experiment.description['infer']
]
standaloneTests = list(testNames)
options = dict(zip(testNames, testOptions))
baselineTestNames = set()
for testName in testNames:
(datasetName, testVariant) = testName.split('_')
if testVariant == 'baseline':
baselineTestNames.add(testName)
# ------------------------------------------------------------------------
# For each baseline, train the classifier, and then run each variant that uses
# that baseline
for baselineTestName in baselineTestNames:
(baseDatasetName, baseVariantName) = baselineTestName.split('_')
assert (baseVariantName == 'baseline')
standaloneTests.remove(baselineTestName)
baseline = _analyzeTestRun(experiment,
netInfo=netInfo,
options=options[baselineTestName],
datasetName=baseDatasetName,
testVariantName=baseVariantName,
baseline=None)
# Now, run each variant that uses this baseline
for testName in testNames:
(datasetName, variantName) = testName.split('_')
if datasetName != baseDatasetName or variantName == 'baseline':
continue
standaloneTests.remove(testName)
_analyzeTestRun(experiment,
netInfo=netInfo,
options=options[testName],
datasetName=datasetName,
testVariantName=variantName,
baseline=baseline)
# Run each standalone test
for testName in standaloneTests:
(datasetName, variantName) = testName.split('_')
_analyzeTestRun(experiment,
netInfo=netInfo,
options=options[testName],
datasetName=datasetName,
testVariantName=variantName,
baseline=None)
