def loadPreTrained(model: BaseNet, path: str, logger: HtmlLogger) -> dict:
optimizerStateDict = None
if path is not None:
if exists(path):
# load checkpoint
checkpoint = loadModel(
path, map_location=lambda storage, loc: storage.cuda())
# load weights
model.loadPreTrained(checkpoint['state_dict'])
# # load optimizer state dict
# optimizerStateDict = checkpoint['optimizer']
# add info rows about checkpoint
loggerRows = []
loggerRows.append(['Path', '{}'.format(path)])
validationAccRows = [[
'Ratio', 'Accuracy'
]] + HtmlLogger.dictToRows(checkpoint['best_prec1'],
nElementPerRow=1)
loggerRows.append(['Validation accuracy', validationAccRows])
# set optimizer table row
optimizerRow = HtmlLogger.dictToRows(
optimizerStateDict, nElementPerRow=3
) if optimizerStateDict else optimizerStateDict
loggerRows.append(['Optimizer', optimizerRow])
logger.addInfoTable('Pre-trained model', loggerRows)
else:
raise ValueError(
'Failed to load pre-trained from [{}], path does not exists'
.format(path))
return optimizerStateDict
def logParameters(logger, args, model):
if not logger:
return
# log command line
logger.addInfoTable(
title='Command line',
rows=[[' '.join(argv)], ['PID', getpid()], ['Hostname',
gethostname()],
['CUDA_VISIBLE_DEVICES',
environ.get('CUDA_VISIBLE_DEVICES')]])
# # calc number of permutations
# permutationStr = model.nPerms
# for p in [12, 9, 6, 3]:
# v = model.nPerms / (10 ** p)
# if v > 1:
# permutationStr = '{:.3f} * 10<sup>{}</sup>'.format(v, p)
# break
#
# # log other parameters
# logger.addInfoTable('Parameters', HtmlLogger.dictToRows(
# {
# 'Learnable params': len([param for param in model.parameters() if param.requires_grad]),
# 'Widths per layer': [layer.nWidths() for layer in model.layersList()],
# 'Permutations': permutationStr
# }, nElementPerRow=2))
# init args dict sorting function
sortFuncsDict = {k: lambda kv: kv[-1] for k in BaseNet.keysToSortByValue()}
# transform args to dictionary
argsDict = vars(args)
# emit model flops list from args dict
modelFlopsKey = BaseNet.modelFlopsKey()
modelFlops = argsDict[modelFlopsKey]
del argsDict[modelFlopsKey]
# log args to html
logger.addInfoTable(
'args',
HtmlLogger.dictToRows(argsDict, 3, lambda kv: kv[0], sortFuncsDict))
# print args
print(args)
# save to json
saveArgsToJSON(args)
# bring back model flops list
argsDict[modelFlopsKey] = modelFlops
class SearchRegime(TrainRegime):
# init train logger key
trainLoggerKey = TrainWeights.trainLoggerKey
summaryKey = TrainWeights.summaryKey
# init table columns names
archLossKey = 'Arch Loss'
pathsListKey = 'Paths list'
gradientsKey = 'Gradients'
# get keys from TrainWeights
batchNumKey = TrainWeights.batchNumKey
epochNumKey = TrainWeights.epochNumKey
forwardCountersKey = TrainWeights.forwardCountersKey
timeKey = TrainWeights.timeKey
trainLossKey = TrainWeights.trainLossKey
trainAccKey = TrainWeights.trainAccKey
validLossKey = TrainWeights.validLossKey
validAccKey = TrainWeights.validAccKey
widthKey = TrainWeights.widthKey
lrKey = TrainWeights.lrKey
validFlopsRatioKey = TrainWeights.flopsRatioKey
# init table columns
k = 2
alphasTableTitle = 'Alphas (top [{}])'.format(k)
# init table columns names
colsTrainAlphas = [
batchNumKey, archLossKey, alphasTableTitle, pathsListKey, gradientsKey
]
colsMainLogger = [
epochNumKey, archLossKey, trainLossKey, trainAccKey, validLossKey,
validAccKey, validFlopsRatioKey, widthKey, lrKey
]
# init statistics (plots) keys template
batchLossAvgTemplate = '{}_Loss_Avg_(Batch)'
epochLossAvgTemplate = '{}_Loss_Avg_(Epoch)'
batchLossVarianceTemplate = '{}_Loss_Variance_(Batch)'
# init statistics (plots) keys
entropyKey = 'Alphas_Entropy'
batchAlphaDistributionKey = 'Alphas_Distribution_(Batch)'
epochAlphaDistributionKey = 'Alphas_Distribution_(Epoch)'
# init formats for keys
formats = {
archLossKey: lambda x: HtmlLogger.dictToRows(x, nElementPerRow=1)
}
def __init__(self, args, logger):
self.lossClass = FlopsLoss
super(SearchRegime, self).__init__(args, logger)
# init number of epochs
self.nEpochs = args.search_epochs
# init main table
logger.createDataTable('Search summary', self.colsMainLogger)
# update max table cell length
logger.setMaxTableCellLength(30)
# add TrainWeights formats to self.formats
self.formats.update(TrainWeights.getFormats())
# update epoch key format
self.formats[
self.epochNumKey] = lambda x: '{}/{}'.format(x, self.nEpochs)
# init flops loss
self.flopsLoss = FlopsLoss(
args, getattr(args, self.model.baselineFlopsKey()))
self.flopsLoss = self.flopsLoss.cuda()
# create search queue
self.search_queue = self.createSearchQueue()
# load model pre-trained weights
TrainWeights.loadPreTrained(self.model, args.pre_trained, self.logger)
# reset args.pre_trained, we don't want to load these weights anymore
args.pre_trained = None
# init model replications
self.replicator = self.initReplicator()
# create folder for jobs checkpoints
self.jobsPath = '{}/jobs'.format(args.save)
makedirs(self.jobsPath)
# init data table row keys to replace
self.rowKeysToReplace = [self.validLossKey, self.validAccKey]
def buildStatsRules(self):
_alphaDistributionMaxVal = 1.1
return {
self.batchAlphaDistributionKey: _alphaDistributionMaxVal,
self.epochAlphaDistributionKey: _alphaDistributionMaxVal
}
# apply defined format functions on dict values by keys
def _applyFormats(self, dict):
for k in dict.keys():
if k in self.formats:
dict[k] = self.formats[k](dict[k])
@abstractmethod
def initReplicator(self):
raise NotImplementedError('subclasses must override initReplicator()!')
@abstractmethod
def _pathsListToRows(self, batchLossDictsList: list) -> list:
raise NotImplementedError(
'subclasses must override _parsePathsList()!')
@abstractmethod
def _containerPerAlpha(self, model: BaseNet) -> list:
raise NotImplementedError(
'subclasses must override _containerPerAlpha()!')
@abstractmethod
def _alphaGradTitle(self, layer: SlimLayer, alphaIdx: int):
raise NotImplementedError(
'subclasses must override _alphaGradTitle()!')
@abstractmethod
def _calcAlphasDistribStats(self, model: BaseNet,
alphaDistributionKey: str):
raise NotImplementedError(
'subclasses must override _calcAlphasDistribStats()!')
# add epoch loss to statistics plots
@abstractmethod
def _updateEpochLossStats(self, epochLossDict: dict):
raise NotImplementedError(
'subclasses must override _updateEpochLossStats()!')
# updates alphas gradients
# updates statistics
@abstractmethod
def _updateAlphasGradients(self, lossDictsList: list) -> dict:
raise NotImplementedError(
'subclasses must override _updateAlphasGradients()!')
def _alphaPlotTitle(self, layer: SlimLayer, alphaIdx: int) -> str:
return '{} ({})'.format(layer.widthRatioByIdx(alphaIdx),
layer.widthByIdx(alphaIdx))
def _getNextSearchQueueDataLoader(self):
if len(self.search_queue) == 0:
# create search queue again, because we iterate over all samples
self.search_queue = self.createSearchQueue()
# get next DataLoader
dataLoader = self.search_queue[0]
# remove DataLoader from search_queue list
del self.search_queue[0]
return dataLoader
def TrainWeightsClass(self):
return EpochTrainWeights
def _addValuesToStatistics(self, getListFunc: callable, templateStr: str,
valuesDict: dict):
for k, v in valuesDict.items():
self.statistics.addValue(getListFunc(templateStr.format(k)), v)
def trainAlphas(self, search_queue, optimizer, epoch, loggers):
print('*** trainAlphas() ***')
model = self.model
replicator = self.replicator
# init trainingStats instance
trainStats = AlphaTrainingStats(self.flopsLoss.lossKeys(),
useAvg=False)
def createInfoTable(dict, key, logger, rows):
dict[key] = logger.createInfoTable('Show', rows)
def createAlphasTable(k, rows):
createInfoTable(dataRow, self.alphasTableTitle, trainLogger, rows)
nBatches = len(search_queue)
# update batch num key format
self.formats[self.batchNumKey] = lambda x: '{}/{}'.format(x, nBatches)
startTime = time()
# choose nSamples paths, train them, evaluate them over search_queue
# lossDictsList is a list of lists where each list contains losses of specific batch
lossDictsList = replicator.loss(model, search_queue)
calcTime = time() - startTime
trainLogger = loggers.get(self.trainLoggerKey)
if trainLogger:
trainLogger.createDataTable(
'Alphas - Epoch:[{}] - Time:[{:.3f}]'.format(epoch, calcTime),
self.colsTrainAlphas)
for batchNum, batchLossDictsList in enumerate(lossDictsList):
# reset optimizer gradients
optimizer.zero_grad()
# update statistics and alphas gradients based on loss
lossAvgDict = self._updateAlphasGradients(batchLossDictsList)
# perform optimizer step
optimizer.step()
# update training stats
for lossName, loss in lossAvgDict.items():
trainStats.update(lossName, loss)
# save alphas to csv
model.saveAlphasCsv(data=[epoch, batchNum])
# update batch alphas distribution statistics (after optimizer step)
self._calcAlphasDistribStats(model, self.batchAlphaDistributionKey)
if trainLogger:
# parse paths list to InfoTable rows
pathsListRows = self._pathsListToRows(batchLossDictsList)
# parse alphas gradients to InfoTable rows
gradientRows = [['Layer #', self.gradientsKey]]
for layerIdx, (layer, alphas) in enumerate(
zip(model.layersList(), model.alphas())):
gradientRows.append([
layerIdx,
[[
self._alphaGradTitle(layer, idx),
'{:.5f}'.format(alphas.grad[idx])
] for idx in range(len(alphas))]
])
# init data row
dataRow = {
self.batchNumKey:
batchNum,
self.archLossKey:
trainStats.batchLoss(),
self.pathsListKey:
trainLogger.createInfoTable('Show', pathsListRows),
self.gradientsKey:
trainLogger.createInfoTable('Show', gradientRows)
}
# add alphas distribution table
model.logTopAlphas(self.k, [createAlphasTable])
# apply formats
self._applyFormats(dataRow)
# add row to data table
trainLogger.addDataRow(dataRow)
epochLossDict = trainStats.epochLoss()
# log summary row
summaryDataRow = {
self.batchNumKey: self.summaryKey,
self.archLossKey: epochLossDict
}
# delete batch num key format
del self.formats[self.batchNumKey]
# apply formats
self._applyFormats(summaryDataRow)
# add row to data table
trainLogger.addSummaryDataRow(summaryDataRow)
# update epoch alphas distribution statistics (after optimizer step)
self._calcAlphasDistribStats(model, self.epochAlphaDistributionKey)
# update epoch loss statistics
self._updateEpochLossStats(epochLossDict)
# update statistics plots
self.statistics.plotData()
return epochLossDict, summaryDataRow
@staticmethod
def _getEpochRange(nEpochs: int) -> range:
return range(1, nEpochs + 1)
@staticmethod
def _generateTableValue(jobName, key) -> str:
return {BaseNet.partitionKey(): '{}_{}'.format(jobName, key)}
def _createPartitionInfoTable(self, partition):
rows = [['Layer #', self.widthKey]
] + [[layerIdx, w] for layerIdx, w in enumerate(partition)]
table = self.logger.createInfoTable('Show', rows)
return table
def _createJob(self, epoch: int, id: int,
choosePathFunc: callable) -> dict:
model = self.model
args = self.args
# clone args
job = Namespace(**vars(args))
# init job name
epochStr = epoch if epoch >= 10 else '0{}'.format(epoch)
idStr = id if id >= 10 else '0{}'.format(id)
jobName = '[{}]-[{}]-[{}]'.format(args.time, epochStr, idStr)
# create job data row
dataRow = {
k: self._generateTableValue(jobName, k)
for k in self.rowKeysToReplace
}
# sample path from alphas distribution
choosePathFunc()
# set attributes
job.partition = model.currWidthRatio()
job.epoch = epoch
job.id = id
job.jobName = jobName
job.tableKeys = dataRow
job.width = [0.25, 0.5, 0.75, 1.0]
# init model flops key
modelFlopsKey = BaseNet.modelFlopsKey()
# reset model flops dict
setattr(job, modelFlopsKey, None)
# save job
jobPath = '{}/{}.pth.tar'.format(self.jobsPath, job.jobName)
saveCheckpoint(job, jobPath)
# add flops ratio to data row
dataRow[self.validFlopsRatioKey] = model.flopsRatio()
# add path width ratio to data row
dataRow[self.widthKey] = self._createPartitionInfoTable(job.partition)
# add epoch number to data row
dataRow[self.epochNumKey] = epoch
# apply formats
self._applyFormats(dataRow)
return dataRow
def _createEpochJobs(self, epoch: int) -> list:
# init epoch data rows list
epochDataRows = []
# init model path chooser function
choosePathFunc = self.model.choosePathAlphasAsPartition
for id in self._getEpochRange(self.args.nJobs):
jobDataRow = self._createJob(epoch, id, choosePathFunc)
epochDataRows.append(jobDataRow)
# only 1st job should be based on alphas max, the rest should sample from alphas distribution
choosePathFunc = self.model.choosePathByAlphas
return epochDataRows
def train(self):
args = self.args
model = self.model
logger = self.logger
epochRange = self._getEpochRange(self.nEpochs)
# init optimizer
optimizer = SGD(model.alphas(),
args.search_learning_rate,
momentum=args.search_momentum,
weight_decay=args.search_weight_decay)
# init scheduler
scheduler = ReduceLROnPlateau(optimizer,
mode='min',
factor=0.95,
patience=args.search_patience,
min_lr=args.search_learning_rate_min)
for epoch in epochRange:
print('========== Epoch:[{}/{}] =============='.format(
epoch, self.nEpochs))
# init epoch train logger
trainLogger = HtmlLogger(self.trainFolderPath, epoch)
# set loggers dictionary
loggersDict = {self.trainLoggerKey: trainLogger}
# create epoch jobs
epochDataRows = self._createEpochJobs(epoch)
# add epoch data rows
for jobDataRow in epochDataRows:
logger.addDataRow(jobDataRow, trType='<tr bgcolor="#2CBDD6">')
# train alphas
# epochLossDict, alphasDataRow = self.trainAlphas(self._getNextSearchQueueDataLoader(), optimizer, epoch, loggersDict)
epochLossDict, alphasDataRow = self.trainAlphas(
self.valid_queue, optimizer, epoch, loggersDict)
# update scheduler
scheduler.step(epochLossDict.get(self.flopsLoss.totalKey()))
# calc model choosePathAlphasAsPartition flops ratio
model.choosePathAlphasAsPartition()
# add values to alphas data row
additionalData = {
self.epochNumKey: epoch,
self.lrKey: optimizer.param_groups[0]['lr'],
self.validFlopsRatioKey: model.flopsRatio()
}
self._applyFormats(additionalData)
# add alphas data row
alphasDataRow.update(additionalData)
logger.addDataRow(alphasDataRow)
# save checkpoint
save_checkpoint(self.trainFolderPath, model, optimizer,
epochLossDict)
class TrainWeights:
# init train logger key
trainLoggerKey = 'train'
summaryKey = 'Summary'
# init tables keys
trainLossKey = 'Training loss'
trainAccKey = 'Training acc'
validLossKey = 'Validation loss'
validAccKey = 'Validation acc'
flopsRatioKey = 'Flops ratio'
epochNumKey = 'Epoch #'
batchNumKey = 'Batch #'
timeKey = 'Time'
lrKey = 'Optimizer lr'
widthKey = 'Width'
forwardCountersKey = 'Forward counters'
# init formats for keys
formats = {
timeKey: lambda x: '{:.3f}'.format(x),
lrKey: lambda x: '{:.8f}'.format(x),
trainLossKey: lambda x: HtmlLogger.dictToRows(x, nElementPerRow=1),
trainAccKey: lambda x: HtmlLogger.dictToRows(x, nElementPerRow=1),
validLossKey: lambda x: HtmlLogger.dictToRows(x, nElementPerRow=1),
validAccKey: lambda x: HtmlLogger.dictToRows(x, nElementPerRow=1),
flopsRatioKey: lambda x: '{:.3f}'.format(x)
}
# init tables columns
colsTrainWeights = [batchNumKey, trainLossKey, trainAccKey, timeKey]
colsValidation = [batchNumKey, validLossKey, validAccKey, timeKey]
# def __init__(self, regime):
def __init__(self, getModel, getModelParallel, getArgs, getLogger,
getTrainQueue, getValidQueue, getTrainFolderPath):
# init functions
self.getModel = getModel
self.getModelParallel = getModelParallel
self.getArgs = getArgs
self.getLogger = getLogger
self.getTrainQueue = getTrainQueue
self.getValidQueue = getValidQueue
self.getTrainFolderPath = getTrainFolderPath
# self.regime = regime
# init cross entropy loss
self.cross_entropy = CrossEntropyLoss().cuda()
# load pre-trained model & optimizer
self.optimizerStateDict = self.loadPreTrained(
self.getModel(),
self.getArgs().pre_trained, self.getLogger())
# apply defined format functions on dict values by keys
def _applyFormats(self, dict):
for k in dict.keys():
if k in self.formats:
dict[k] = self.formats[k](dict[k])
@staticmethod
def getFormats():
return TrainWeights.formats
@abstractmethod
def stopCondition(self, epoch):
raise NotImplementedError('subclasses must override stopCondition()!')
@abstractmethod
# returns (widthRatio, idxList) list or generator
def widthList(self):
raise NotImplementedError('subclasses must override widthList()!')
@abstractmethod
def schedulerMetric(self, validLoss):
raise NotImplementedError(
'subclasses must override schedulerMetric()!')
@abstractmethod
def postEpoch(self, epoch, optimizer, trainData: EpochData,
validData: EpochData):
raise NotImplementedError('subclasses must override postEpoch()!')
@abstractmethod
def postTrain(self):
raise NotImplementedError('subclasses must override postTrain()!')
# generic epoch flow
def _genericEpoch(self, forwardFunc, data_queue, loggers, lossKey, accKey,
tableTitle, tableCols,
forwardCountersTitle) -> EpochData:
trainStats = TrainingStats([k for k, v in self.widthList()])
trainLogger = loggers.get(self.trainLoggerKey)
if trainLogger:
trainLogger.createDataTable(tableTitle, tableCols)
nBatches = len(data_queue)
for batchNum, (input, target) in enumerate(data_queue):
startTime = time()
input = input.cuda().clone().detach().requires_grad_(False)
target = target.cuda(
async=True).clone().detach().requires_grad_(False)
# do forward
forwardFunc(input, target, trainStats)
endTime = time()
if trainLogger:
dataRow = {
self.batchNumKey: '{}/{}'.format(batchNum, nBatches),
self.timeKey: (endTime - startTime),
lossKey: trainStats.batchLoss(),
accKey: trainStats.prec1()
}
# apply formats
self._applyFormats(dataRow)
# add row to data table
trainLogger.addDataRow(dataRow)
epochLossDict = trainStats.epochLoss()
epochAccDict = trainStats.top1()
# # add epoch data to statistics plots
# self.statistics.addBatchData(epochLossDict, epochAccDict)
# log accuracy, loss, etc.
summaryData = {
lossKey: epochLossDict,
accKey: epochAccDict,
self.batchNumKey: self.summaryKey
}
# apply formats
self._applyFormats(summaryData)
for logger in loggers.values():
if logger:
logger.addSummaryDataRow(summaryData)
# log forward counters. if loggerFuncs==[] then it is just resets counters
func = [
lambda rows: trainLogger.addInfoTable(title=forwardCountersTitle,
rows=rows)
] if trainLogger else []
self.getModel().logForwardCounters(loggerFuncs=func)
return EpochData(epochLossDict, epochAccDict, summaryData)
def _slimForward(self, input, target, trainStats):
model = self.getModel()
modelParallel = self.getModelParallel()
crit = self.cross_entropy
# init loss list
lossList = []
# iterate & forward widths
for widthRatio, idxList in self.widthList():
# set model layers current width index
model.setCurrWidthIdx(idxList)
# forward
logits = modelParallel(input)
# calc loss
loss = crit(logits, target)
# add to loss list
lossList.append(loss)
# update training stats
trainStats.update(widthRatio, logits, target, loss)
return lossList
# performs single epoch of model weights training
def weightsEpoch(self, optimizer, epoch, loggers) -> EpochData:
# print('*** weightsEpoch() ***')
model = self.getModel()
modelParallel = self.getModelParallel()
modelParallel.train()
assert (model.training is True)
def forwardFunc(input, target, trainStats):
# optimize model weights
optimizer.zero_grad()
# forward
lossList = self._slimForward(input, target, trainStats)
# back propagate
for loss in lossList:
loss.backward()
# update weights
optimizer.step()
tableTitle = 'Epoch:[{}] - Training weights'.format(epoch)
forwardCountersTitle = '{} - Training'.format(self.forwardCountersKey)
return self._genericEpoch(forwardFunc, self.getTrainQueue(), loggers,
self.trainLossKey, self.trainAccKey,
tableTitle, self.colsTrainWeights,
forwardCountersTitle)
# performs single epoch of model inference
def inferEpoch(self, nEpoch, loggers) -> EpochData:
print('*** inferEpoch() ***')
model = self.getModel()
modelParallel = self.getModelParallel()
modelParallel.eval()
assert (model.training is False)
def forwardFunc(input, target, trainStats):
with no_grad():
self._slimForward(input, target, trainStats)
tableTitle = 'Epoch:[{}] - Validation'.format(nEpoch)
forwardCountersTitle = '{} - Validation'.format(
self.forwardCountersKey)
return self._genericEpoch(forwardFunc, self.getValidQueue(), loggers,
self.validLossKey, self.validAccKey,
tableTitle, self.colsValidation,
forwardCountersTitle)
def _initOptimizer(self):
modelParallel = self.getModelParallel()
args = self.getArgs()
optimizer = SGD(modelParallel.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
# load optimizer pre-trained state dict if exists
if self.optimizerStateDict:
optimizer.load_state_dict(self.optimizerStateDict)
return optimizer
def train(self, trainFolderName='init_weights_train'):
args = self.getArgs()
# create train folder
folderPath = '{}/{}'.format(self.getTrainFolderPath(), trainFolderName)
if not exists(folderPath):
makedirs(folderPath)
# init optimizer
optimizer = self._initOptimizer()
# init scheduler
scheduler = ReduceLROnPlateau(optimizer,
mode='min',
factor=0.95,
patience=args.weights_patience,
min_lr=args.learning_rate_min)
epoch = 0
trainLoggerFlag = True
while not self.stopCondition(epoch):
# update epoch number
epoch += 1
# init train logger
trainLogger = None
if trainLoggerFlag:
trainLogger = HtmlLogger(folderPath, epoch)
trainLogger.addInfoTable('Learning rates', [[
'optimizer_lr', self.formats[self.lrKey](
optimizer.param_groups[0]['lr'])
]])
# update train logger condition for next epoch
trainLoggerFlag = ((epoch + 1) % args.logInterval) == 0
# set loggers dictionary
loggersDict = {self.trainLoggerKey: trainLogger}
print('========== Epoch:[{}] =============='.format(epoch))
# train
trainData = self.weightsEpoch(optimizer, epoch, loggersDict)
# validation
validData = self.inferEpoch(epoch, loggersDict)
# update scheduler
scheduler.step(self.schedulerMetric(validData.lossDict()))
self.postEpoch(epoch, optimizer, trainData, validData)
self.postTrain()
@staticmethod
def loadPreTrained(model: BaseNet, path: str, logger: HtmlLogger) -> dict:
optimizerStateDict = None
if path is not None:
if exists(path):
# load checkpoint
checkpoint = loadModel(
path, map_location=lambda storage, loc: storage.cuda())
# load weights
model.loadPreTrained(checkpoint['state_dict'])
# # load optimizer state dict
# optimizerStateDict = checkpoint['optimizer']
# add info rows about checkpoint
loggerRows = []
loggerRows.append(['Path', '{}'.format(path)])
validationAccRows = [[
'Ratio', 'Accuracy'
]] + HtmlLogger.dictToRows(checkpoint['best_prec1'],
nElementPerRow=1)
loggerRows.append(['Validation accuracy', validationAccRows])
# set optimizer table row
optimizerRow = HtmlLogger.dictToRows(
optimizerStateDict, nElementPerRow=3
) if optimizerStateDict else optimizerStateDict
loggerRows.append(['Optimizer', optimizerRow])
logger.addInfoTable('Pre-trained model', loggerRows)
else:
raise ValueError(
'Failed to load pre-trained from [{}], path does not exists'
.format(path))
return optimizerStateDict