def runAllModels(
inputTest,
questionTypeArray,
modelSpecs,
resultsFolder,
dataset,
dataFolder):
allOutputs = []
for modelSpec in modelSpecs:
if modelSpec['isClassEnsemble']:
print 'Running test data on ensemble model %s...' \
% modelSpec['name']
models = loadEnsemble(modelSpec['id'].split(','), resultsFolder)
classDataFolders = getClassDataFolders(dataset, dataFolder)
if modelSpec['runPrior']:
outputTest = runEnsemblePrior(
inputTest,
models,
dataFolder,
classDataFolders,
questionTypeArray)
else:
outputTest = runEnsemble(
inputTest,
models,
dataFolder,
classDataFolders,
questionTypeArray)
elif modelSpec['isAverageEnsemble']:
modelOutputs = []
for modelId in modelSpec['id'].split(','):
model = it.loadModel(modelId, resultsFolder)
modelOutputs.append(nn.test(model, inputTest))
outputTest = np.zeros(modelOutputs[0].shape)
for output in modelOutputs:
shape0 = min(outputTest.shape[0], output.shape[0])
shape1 = min(outputTest.shape[1], output.shape[1])
outputTest[:shape0, :shape1] += output[:shape0, :shape1] / \
float(len(modelOutputs))
else:
print 'Running test data on model %s...' \
% modelSpec['name']
model = it.loadModel(modelSpec['id'], resultsFolder)
outputTest = nn.test(model, inputTest)
allOutputs.append(outputTest)
return allOutputs
dataset = 'cocoqa'
for i, flag in enumerate(sys.argv):
if flag == '-m' or flag == '-model':
modelId = sys.argv[i + 1]
elif flag == '-d' or flag == '-data':
dataFolder = sys.argv[i + 1]
elif flag == '-td' or flag == '-tdata':
testDataFolder = sys.argv[i + 1]
elif flag == '-reindex':
needReindex = True
elif flag == '-r' or flag == '-results':
resultsFolder = sys.argv[i + 1]
elif flag == '-dataset':
dataset = sys.argv[i + 1]
model = it.loadModel(modelId, resultsFolder)
data = it.loadDataset(dataFolder)
testdata = it.loadDataset(testDataFolder)
if needReindex:
testQuestions, testAnswers = reindexDataset(
testdata['testData'][0],
testdata['testData'][1],
testdata['questionIdict'],
data['questionDict'],
testdata['ansIdict'],
data['ansDict'])
else:
testQuestions = testdata['testData'][0]
testAnswers = testdata['testData'][1]
outputTest = nn.test(model, testQuestions)
rate, correct, total = nn.calcRate(model, outputTest, testAnswers)
modelIds.append(sys.argv[i + 1])
elif flag == '-vm' or flag == '-vmodel':
validModelIds.append(sys.argv[i + 1])
elif flag == '-r' or flag == '-results':
resultsFolder = sys.argv[i + 1]
elif flag == '-d' or flag == '-data':
dataFolder = sys.argv[i + 1]
elif flag == '-o' or flag == '-output':
outputFolder = sys.argv[i + 1]
data = it.loadDataset(dataFolder)
models = []
validModels = []
for modelId in modelIds:
print 'Loading model %s' % modelId
models.append(it.loadModel(modelId, resultsFolder))
for modelId in validModelIds:
print 'Loading model %s' % modelId
validModels.append(it.loadModel(modelId, resultsFolder))
modelOutputs = []
validModelOutputs = []
# for modelId, model in zip(validModelIds, validModels):
# print 'Running model %s' % modelId
# modelOutput = nn.test(model, data['validData'][0])
# validModelOutputs.append(modelOutput)
#
# mixRatios = np.arange(0, 11) * 0.1
# bestMixRatio = validAvg(validModelOutputs, mixRatios, data['validData'][1])
# print 'Best ratio found: %.4f' % bestMixRatio
bestMixRatio = 0.5
dataset = 'cocoqa'
for i, flag in enumerate(sys.argv):
if flag == '-m' or flag == '-model':
modelId = sys.argv[i + 1]
elif flag == '-d' or flag == '-data':
dataFolder = sys.argv[i + 1]
elif flag == '-td' or flag == '-tdata':
testDataFolder = sys.argv[i + 1]
elif flag == '-reindex':
needReindex = True
elif flag == '-r' or flag == '-results':
resultsFolder = sys.argv[i + 1]
elif flag == '-dataset':
dataset = sys.argv[i + 1]
model = it.loadModel(modelId, resultsFolder)
data = it.loadDataset(dataFolder)
testdata = it.loadDataset(testDataFolder)
if needReindex:
testQuestions, testAnswers = reindexDataset(testdata['testData'][0],
testdata['testData'][1],
testdata['questionIdict'],
data['questionDict'],
testdata['ansIdict'],
data['ansDict'])
else:
testQuestions = testdata['testData'][0]
testAnswers = testdata['testData'][1]
outputTest = nn.test(model, testQuestions)
rate, correct, total = nn.calcRate(model, outputTest, testAnswers)
print 'rate: %.4f' % rate
visDataFolder = sys.argv[i + 1]
elif flag == "-md" or flag == "-mdata":
mainDataFolder = sys.argv[i + 1]
elif flag == "-r" or flag == "-results":
resultsFolder = sys.argv[i + 1]
elif flag == "-qtype":
questionType = sys.argv[i + 1]
elif flag == "-o" or flag == "-outweights":
outputWeightsFolder = sys.argv[i + 1]
data = it.loadDataset(visDataFolder)
testInput = data["testData"][0]
testTarget = data["testData"][1]
deltas = [0.000001, 0.000005, 0.00001, 0.00005, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0]
preVisModel = it.loadModel(preVisModelId, resultsFolder)
print "Num answer", len(data["ansIdict"])
bestDelta = validDelta(
data["trainData"],
data["validData"],
preVisModel,
data["questionDict"],
data["questionIdict"],
len(data["ansIdict"]),
deltas,
questionType,
)
trainDataAll = combineTrainValid(data["trainData"], data["validData"])
visModel = it.loadModel(visModelId, resultsFolder)
deltas = \
[0.000001,
0.000005,
0.00001,
0.00005,
0.0001,
0.0005,
0.001,
0.005,
0.01,
0.05,
0.1,
0.5,
1.0]
preVisModel = it.loadModel(preVisModelId, resultsFolder)
print 'Num answer', len(data['ansIdict'])
bestDelta = validDelta(
data['trainData'],
data['validData'],
preVisModel,
data['questionDict'],
data['questionIdict'],
len(data['ansIdict']),
deltas,
questionType)
trainDataAll = combineTrainValid(data['trainData'], data['validData'])
visModel = it.loadModel(visModelId, resultsFolder)
visTestOutput = runVisPrior(trainDataAll,
