Example #1
0
def net_prms(dbFile=DEF_DB % 'nyu2_net', **kwargs):
    dArgs = mec.get_default_net_prms(dbFile, **kwargs)
    del dArgs['expStr']
    #The data NetDefProto
    dArgs.dataNetDefProto = 'data_layer_nyu2'
    #the basic network architecture: baseNetDefProto
    dArgs.baseNetDefProto = 'doublefc-v1_window_fc6'
    #the loss layers:
    dArgs.lossNetDefProto = 'nyu2_loss_classify_layers'
    if dArgs.batchSize is None:
        dArgs.batchSize = 128
    #The amount of jitter in both the images
    dArgs.maxJitter = 0
    #The size of crop that should be cropped from the image
    dArgs.cropScale = 0.9
    #the size to which the cropped image should be resized
    dArgs.ipImSz = 101
    ##The mean file
    dArgs.meanFile = ''
    dArgs.meanType = None
    dArgs.opLrMult = None
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    allKeys = dArgs.keys()
    dArgs['expStr'] = mec.get_sql_id(dbFile, dArgs)
    return dArgs, allKeys
Example #2
0
def get_default_solver_prms(dbFile=DEF_DB, **kwargs):
	'''
		Refer to caffe.proto for a description of the
		variables. 
	'''	
	dArgs = edict()
	dArgs.baseSolDefFile = None
	dArgs.iter_size   = 1
	dArgs.max_iter    = 250000
	dArgs.base_lr   = 0.001
	dArgs.lr_policy   = 'step' 
	dArgs.stepsize    = 20000	
	dArgs.gamma     = 0.5
	dArgs.weight_decay = 0.0005
	dArgs.clip_gradients = -1
	#Momentum
	dArgs.momentum  = 0.9
	#Other
	dArgs.regularization_type = 'L2'
	dArgs.random_seed = -1
	#Testing info
	dArgs.test_iter     = 100
	dArgs.test_interval = 1000
	dArgs.snapshot      = 2000	
	dArgs.display       = 20
	#Update parameters
	dArgs        = mpu.get_defaults(kwargs, dArgs, False)
	dArgs.expStr = 'solprms' + get_sql_id(dbFile, dArgs,
									ignoreKeys=['test_iter',  'test_interval',
								 'snapshot', 'display'])
	return dArgs 
Example #3
0
def get_default_solver_prms(dbFile=DEF_DB, **kwargs):
    '''
		Refer to caffe.proto for a description of the
		variables. 
	'''
    dArgs = edict()
    dArgs.baseSolDefFile = None
    dArgs.iter_size = 1
    dArgs.max_iter = 250000
    dArgs.base_lr = 0.001
    dArgs.lr_policy = 'step'
    dArgs.stepsize = 20000
    dArgs.gamma = 0.5
    dArgs.weight_decay = 0.0005
    dArgs.clip_gradients = -1
    #Momentum
    dArgs.momentum = 0.9
    #Other
    dArgs.regularization_type = 'L2'
    dArgs.random_seed = -1
    #Testing info
    dArgs.test_iter = 100
    dArgs.test_interval = 1000
    dArgs.snapshot = 2000
    dArgs.display = 20
    #Update parameters
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    dArgs.expStr = 'solprms' + get_sql_id(
        dbFile,
        dArgs,
        ignoreKeys=['test_iter', 'test_interval', 'snapshot', 'display'])
    return dArgs
Example #4
0
def net_prms(dbFile=DEF_DB % 'net', **kwargs):
    dArgs = mec.get_siamese_net_prms(dbFile, **kwargs)
    del dArgs['expStr']
    #The data NetDefProto
    dArgs.dataNetDefProto = 'data_layer_groups'
    #the basic network architecture: baseNetDefProto
    dArgs.baseNetDefProto = 'smallnet-v5_window_siamese_fc5'
    #the loss layers:
    dArgs.lossNetDefProto = 'pose_loss_log_l1_layers'
    if dArgs.batchSize is None:
        dArgs.batchSize = 128
    #The amount of jitter in both the images
    dArgs.maxJitter = 0
    #The amount of roll jitter to apply to the images
    dArgs.maxRollJitter = None
    #The size of crop that should be cropped from the image
    dArgs.crpSz = 192
    #the size to which the cropped image should be resized
    dArgs.ipImSz = 101
    #The size of the fc layer if present
    dArgs.fcSz = None
    dArgs.fcName = 'fc5'
    ##The mean file
    dArgs.meanFile = ''
    dArgs.meanType = None
    dArgs.ncpu = 3
    dArgs.readSingleGrp = False
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    allKeys = dArgs.keys()
    dArgs['expStr'] = mec.get_sql_id(dbFile,
                                     dArgs,
                                     ignoreKeys=['ncpu', 'readSingleGrp'])
    return dArgs, allKeys
Example #5
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def get_lr_prms(**kwargs):
    #return mec.get_lr_prms()
    dArgs = edict()
    dArgs.batchsize = 128
    dArgs.stepsize = 20000
    dArgs.base_lr = 0.001
    dArgs.max_iter = 250000
    dArgs.gamma = 0.5
    dArgs.weight_decay = 0.0005
    dArgs.clip_gradients = -1
    dArgs.debug_info = False
    dArgs = mpu.get_defaults(kwargs, dArgs)
    #Make the solver
    debugStr = '%s' % dArgs.debug_info
    debugStr = debugStr.lower()
    del dArgs['debug_info']
    solArgs = edict({
        'test_iter': 100,
        'test_interval': 1000,
        'snapshot': 2000,
        'debug_info': debugStr
    })
    print dArgs.keys()
    for k in dArgs.keys():
        if k in ['batchsize']:
            continue
        solArgs[k] = copy.deepcopy(dArgs[k])
    dArgs.solver = mpu.make_solver(**solArgs)
    expStr = 'batchSz%d_stepSz%.0e_blr%.5f_mxItr%.1e_gamma%.2f_wdecay%.6f'\
         % (dArgs.batchsize, dArgs.stepsize, dArgs.base_lr,
          dArgs.max_iter, dArgs.gamma, dArgs.weight_decay)
    if not (dArgs.clip_gradients == -1):
        expStr = '%s_gradClip%.1f' % (expStr, dArgs.clip_gradients)
    dArgs.expStr = expStr
    return dArgs
Example #6
0
def get_data_prms(dbFile=DEF_DB % 'nyu2_data', **kwargs):
    dArgs = edict()
    dArgs.dataset = 'nyu2'
    allKeys = dArgs.keys()
    dArgs = mpu.get_defaults(kwargs, dArgs)
    dArgs['expStr'] = mec.get_sql_id(dbFile, dArgs)
    dArgs['paths'] = snyu.get_paths()
    return dArgs
Example #7
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def get_siamese_window_net_prms(dbFile=DEF_DB, **kwargs):
	dArgs = get_siamese_net_prms(dbFile)
	del dArgs['expStr']
	#Size of input image
	dArgs.imSz = 227
	#If random cropping is to be used	
	dArgs.randCrop = False
	#If gray scale images need to be used
	dArgs.isGray   = False
	dArgs = mpu.get_defaults(kwargs, dArgs, False)
	dArgs.expStr   = get_sql_id(dbFile, dArgs)
	return dArgs
Example #8
0
def get_siamese_window_net_prms(dbFile=DEF_DB, **kwargs):
    dArgs = get_siamese_net_prms(dbFile)
    del dArgs['expStr']
    #Size of input image
    dArgs.imSz = 227
    #If random cropping is to be used
    dArgs.randCrop = False
    #If gray scale images need to be used
    dArgs.isGray = False
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    dArgs.expStr = get_sql_id(dbFile, dArgs)
    return dArgs
Example #9
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def get_siamese_net_prms(dbFile=DEF_DB, **kwargs):
	dArgs = get_default_net_prms(dbFile)
	del dArgs['expStr']
	#Layers at which the nets are to be concatenated
	dArgs.concatLayer = 'fc6'
	#If dropouts should be used in the concatenation layer
	dArgs.concatDrop  = False
	#Number of filters in concatenation layer
	dArgs.concatSz    = None
	#If an extra FC layer needs to be added
	dArgs.extraFc     = None
	dArgs = mpu.get_defaults(kwargs, dArgs, False)
	dArgs.expStr      = get_sql_id(dbFile, dArgs)
	return dArgs
Example #10
0
def get_siamese_net_prms(dbFile=DEF_DB, **kwargs):
    dArgs = get_default_net_prms(dbFile)
    del dArgs['expStr']
    #Layers at which the nets are to be concatenated
    dArgs.concatLayer = 'fc6'
    #If dropouts should be used in the concatenation layer
    dArgs.concatDrop = False
    #Number of filters in concatenation layer
    dArgs.concatSz = None
    #If an extra FC layer needs to be added
    dArgs.extraFc = None
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    dArgs.expStr = get_sql_id(dbFile, dArgs)
    return dArgs
Example #11
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def get_default_net_prms(dbFile=DEF_DB, **kwargs):
	dArgs = edict()
	#Name of the net which will be constructed
	dArgs.netName = 'alexnet'
	#For layers below lrAbove, learning rate is set to 0
	dArgs.lrAbove     = None
	#If weights from a pretrained net are to be used
	dArgs.preTrainNet = None
	#The base proto from which net will be constructed
	dArgs.baseNetDefProto = None
	#Batch size
	dArgs.batchSize   = None
	#runNum
	dArgs.runNum      = 0
	dArgs = mpu.get_defaults(kwargs, dArgs, False)
	dArgs.expStr      = get_sql_id(dbFile, dArgs)
	return dArgs
Example #12
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def get_default_net_prms(dbFile=DEF_DB, **kwargs):
    dArgs = edict()
    #Name of the net which will be constructed
    dArgs.netName = 'alexnet'
    #For layers below lrAbove, learning rate is set to 0
    dArgs.lrAbove = None
    #If weights from a pretrained net are to be used
    dArgs.preTrainNet = None
    #The base proto from which net will be constructed
    dArgs.baseNetDefProto = None
    #Batch size
    dArgs.batchSize = None
    #runNum
    dArgs.runNum = 0
    dArgs = mpu.get_defaults(kwargs, dArgs, False)
    dArgs.expStr = get_sql_id(dbFile, dArgs)
    return dArgs
Example #13
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def get_data_prms(dbFile=DEF_DB % 'data', lbPrms=None, tvPrms=None, **kwargs):
    if lbPrms is None:
        lbPrms = slu.PosePrms()
    if tvPrms is None:
        tvPrms = get_trainval_split_prms()
    dArgs = mec.edict()
    dArgs.dataset = 'dc-v2'
    dArgs.lbStr = lbPrms.get_lbstr()
    dArgs.tvStr = tvPrms.pStr
    dArgs.isAlign = True
    allKeys = dArgs.keys()
    dArgs = mpu.get_defaults(kwargs, dArgs)
    dArgs['expStr'] = mec.get_sql_id(dbFile, dArgs)
    dArgs['splitPrms'] = tvPrms
    dArgs['lbPrms'] = lbPrms
    dArgs['paths'] = get_paths(dArgs)
    return dArgs
Example #14
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def get_finetune_prms(**kwargs):
    '''
		sourceModelIter: The number of model iterations of the source model to consider
		fine_max_iter  : The maximum iterations to which the target model should be trained.
		lrAbove        : If learning is to be performed some layer. 
		fine_base_lr   : The base learning rate for finetuning. 
 		fineRunNum     : The run num for the finetuning.
		fineNumData    : The amount of data to be used for the finetuning. 
		fineMaxLayer   : The maximum layer of the source n/w that should be considered.  
	'''
    dArgs = edict()
    dArgs.base_lr = 0.001
    dArgs.runNum = 1
    dArgs.maxLayer = None
    dArgs.lrAbove = None
    dArgs.dataset = 'sun'
    dArgs.maxIter = 40000
    dArgs.extraFc = False
    dArgs.extraFcDrop = False
    dArgs.sourceModelIter = 60000
    dArgs = mpu.get_defaults(kwargs, dArgs)
    return dArgs
Example #15
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def get_data_prms(dbFile=DEF_DB % 'pascal_data', **kwargs):
    dArgs = edict()
    dArgs.dataset = 'pascal'
    dArgs.imCutSz = 256
    dArgs.imPadSz = 36
    dArgs.angleFormat = 'radian'
    dArgs.anglePreProc = 'mean_sub'
    dArgs.nAzBins = None
    dArgs.nElBins = None
    allKeys = dArgs.keys()
    dArgs = mpu.get_defaults(kwargs, dArgs)
    if dArgs.anglePreProc == 'classify':
        assert dArgs.nAzBins is not None
        assert dArgs.nElBins is not None
    dArgs['expStr'] = mec.get_sql_id(dbFile, dArgs)
    dArgs['paths'] = get_paths(dArgs)
    dArgs.azBins = None
    dArgs.elBins = None
    if dArgs.nAzBins is not None:
        dArgs.azBins = np.linspace(-np.pi, np.pi, dArgs.nAzBins + 1)
    if dArgs.nElBins is not None:
        dArgs.elBins = np.linspace(-np.pi, np.pi, dArgs.nElBins + 1)
    return dArgs
Example #16
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def get_nw_prms(**kwargs):
    #return mec.get_nw_prms(**kwargs)
    dArgs = edict()
    dArgs.netName = 'alexnet'
    dArgs.concatLayer = 'fc6'
    dArgs.concatDrop = False
    dArgs.contextPad = 0
    dArgs.imSz = 227
    dArgs.imgntMean = True
    dArgs.maxJitter = 11
    dArgs.randCrop = False
    dArgs.lossWeight = 1.0
    dArgs.multiLossProto = None
    dArgs.ptchStreamNum = 256
    dArgs.poseStreamNum = 256
    dArgs.isGray = False
    dArgs.isPythonLayer = False
    dArgs.extraFc = None
    dArgs.numFc5 = None
    dArgs.numConv4 = None
    dArgs.numCommonFc = None
    dArgs.lrAbove = None
    dArgs = mpu.get_defaults(kwargs, dArgs)
    if dArgs.numFc5 is not None:
        assert (dArgs.concatLayer == 'fc5')
    expStr = 'net-%s_cnct-%s_cnctDrp%d_contPad%d_imSz%d_imgntMean%d_jit%d'\
         %(dArgs.netName, dArgs.concatLayer, dArgs.concatDrop,
          dArgs.contextPad,
          dArgs.imSz, dArgs.imgntMean, dArgs.maxJitter)
    if dArgs.numFc5 is not None:
        expStr = '%s_numFc5-%d' % (expStr, dArgs.numFc5)
    if dArgs.numConv4 is not None:
        expStr = '%s_numConv4-%d' % (expStr, dArgs.numConv4)
    if dArgs.numCommonFc is not None:
        expStr = '%s_numCommonFc-%d' % (expStr, dArgs.numCommonFc)
    if dArgs.randCrop:
        expStr = '%s_randCrp%d' % (expStr, dArgs.randCrop)
    if not (dArgs.lossWeight == 1.0):
        if type(dArgs.lossWeight) == list:
            lStr = ''
            for i, l in enumerate(dArgs.lossWeight):
                lStr = lStr + 'lw%d-%.1f_' % (i, l)
            lStr = lStr[0:-1]
            print lStr
            expStr = '%s_%s' % (expStr, lStr)
        else:
            assert isinstance(dArgs.lossWeight, (int, long, float))
            expStr = '%s_lw%.1f' % (expStr, dArgs.lossWeight)
    if dArgs.multiLossProto is not None:
        expStr = '%s_mlpr%s-posn%d-ptsn%d' % (expStr, dArgs.multiLossProto,
                                              dArgs.poseStreamNum,
                                              dArgs.ptchStreamNum)
    if dArgs.isGray:
        expStr = '%s_grayIm' % expStr
    if dArgs.isPythonLayer:
        expStr = '%s_pylayers' % expStr
    if dArgs.extraFc is not None:
        expStr = '%s_extraFc%d' % (expStr, dArgs.extraFc)
    if dArgs.lrAbove is not None:
        expStr = '%s_lrAbove-%s' % (expStr, dArgs.lrAbove)
    dArgs.expStr = expStr
    return dArgs