def ref_to_str(self, ref):
secRefs = self.dat_['#refs#'][ref][:]
ch = []
#print secRefs
for sec in secRefs:
if not sec == 0:
#If the reference is not empty
ch.append(ou.ints_to_str(self.dat_['#refs#'][sec[0]]))
return ch
def ref_to_str(self, ref): secRefs = self.dat_['#refs#'][ref][:] ch = [] #print secRefs for sec in secRefs: if not sec == 0: #If the reference is not empty ch.append(ou.ints_to_str(self.dat_['#refs#'][sec[0]])) return ch
def save_caffe_model(
self,
outName='/work4/pulkitag-code/code/ief/IEF/models/ief-googlenet-dec2015',
**kwargs):
#caffe prototxt
defFile = outName + '.prototxt'
#caffe model
modelFile = outName + '.caffemodel'
#the meta data
metaFile = outName + '-meta.pkl'
#obtain prototxt from matconvnet and write to disk
pDef = self.to_caffe(**kwargs)
pDef.write(defFile)
#Store th weights
net = caffe.Net(defFile, caffe.TEST)
#List the parameter names of all the matconvnet params
matPrmNames = []
for p in range(len(self.dat_['net']['params']['name'])):
prmRef = self.dat_['net']['params']['name'][p][0]
matPrmNames.append(ou.ints_to_str(self.dat_['#refs#'][prmRef][:]))
#Name of caffe params
paramKeys = net.params.keys()
for k in paramKeys:
for i in range(2):
prm = pDef.get_layer_property(k, 'param', propNum=i)
prmName = prm['name'][1:-1]
idx = matPrmNames.index(prmName)
valRef = self.dat_['net']['params']['value'][idx][0]
vals = np.array(self.dat_['#refs#'][valRef])
if i == 0:
vals = vals.transpose((0, 1, 3, 2))
print(k, i, net.params[k][i].data.shape, vals.shape)
net.params[k][i].data[...] = vals.reshape(
net.params[k][i].data.shape)
net.save(modelFile)
#Store meta information
seedPose = np.array(self.dat_['params']['seed_pose'])
mxStpNrm = np.array(self.dat_['params']['MAX_STEP_NORM'])[0][0]
pickle.dump({
'seedPose': seedPose,
'mxStepNorm': mxStpNrm
}, open(metaFile, 'w'))
def save_caffe_model(self,
outName='/work4/pulkitag-code/code/ief/IEF/models/ief-googlenet-dec2015', **kwargs):
#caffe prototxt
defFile = outName + '.prototxt'
#caffe model
modelFile = outName + '.caffemodel'
#the meta data
metaFile = outName + '-meta.pkl'
#obtain prototxt from matconvnet and write to disk
pDef = self.to_caffe(**kwargs)
pDef.write(defFile)
#Store th weights
net = caffe.Net(defFile, caffe.TEST)
#List the parameter names of all the matconvnet params
matPrmNames = []
for p in range(len(self.dat_['net']['params']['name'])):
prmRef = self.dat_['net']['params']['name'][p][0]
matPrmNames.append(ou.ints_to_str(self.dat_['#refs#'][prmRef][:]))
#Name of caffe params
paramKeys = net.params.keys()
for k in paramKeys:
for i in range(2):
prm = pDef.get_layer_property(k, 'param', propNum=i)
prmName = prm['name'][1:-1]
idx = matPrmNames.index(prmName)
valRef = self.dat_['net']['params']['value'][idx][0]
vals = np.array(self.dat_['#refs#'][valRef])
if i==0:
vals = vals.transpose((0,1,3,2))
print (k, i, net.params[k][i].data.shape, vals.shape)
net.params[k][i].data[...] = vals.reshape(net.params[k][i].data.shape)
net.save(modelFile)
#Store meta information
seedPose = np.array(self.dat_['params']['seed_pose'])
mxStpNrm = np.array(self.dat_['params']['MAX_STEP_NORM'])[0][0]
pickle.dump({'seedPose': seedPose, 'mxStepNorm': mxStpNrm},
open(metaFile, 'w'))
def make_caffe_layer(self, lNum):
#Get the name
nameRef = self.dat_['net']['layers']['name'][lNum][0]
name = ou.ints_to_str(self.dat_['#refs#'][nameRef][:])
#Get the inputs
ipRef = self.dat_['net']['layers']['inputs'][lNum][0]
ipNames = self.ref_to_str(ipRef)
#Get the parameter names
pmRef = self.dat_['net']['layers']['params'][lNum][0]
pmNames = self.ref_to_str(pmRef)
#Get the output names
opRef = self.dat_['net']['layers']['outputs'][lNum][0]
opNames = self.ref_to_str(opRef)
#Get the layer type
tpRef = self.dat_['net']['layers']['type'][lNum][0]
lType = ou.ints_to_str(self.dat_['#refs#'][tpRef][:])
#Get the layer params
lpRef = self.dat_['net']['layers']['block'][lNum][0]
lParam = self.dat_['#refs#'][lpRef]
assert (lType[0:5] == 'dagnn')
lType = lType[6:]
if lType == 'Conv':
paramW = {'name': pmNames[0]}
paramB = {'name': pmNames[1]}
pDupKey = mpu.make_key('param', ['param'])
lDef = mpu.get_layerdef_for_proto(
'Convolution', name, ipNames[0], **{
'num_output': int(lParam['size'][3][0]),
'param': paramW,
pDupKey: paramB,
'kernel_size': int(lParam['size'][0][0]),
'stride': int(lParam['stride'][0][0]),
'pad': int(lParam['pad'][0][0])
})
elif lType == 'ReLU':
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'top': opNames[0]})
elif lType == 'Pooling':
poolType = lParam['method'][0]
if poolType == 'max':
poolType = 'MAX'
elif poolType == 'avg':
poolType = 'AVE'
lDef = mpu.get_layerdef_for_proto(
lType, name, ipNames[0], **{
'top': opNames[0],
'kernel_size': int(lParam['poolSize'][0][0]),
'stride': int(lParam['stride'][0][0]),
'pad': int(lParam['pad'][0][0]),
'pool': poolType
})
elif lType == 'LRN':
N, kappa, alpha, beta = lParam['param'][0][0], lParam['param'][1][0],\
lParam['param'][2][0], lParam['param'][3][0]
lDef = mpu.get_layerdef_for_proto(
lType, name, ipNames[0], **{
'top': opNames[0],
'local_size': int(N),
'alpha': N * alpha,
'beta': beta,
'k': kappa
})
elif lType == 'Concat':
lDef = mpu.get_layerdef_for_proto(
lType, name, ipNames[0], **{
'bottom2': ipNames[1:],
'concat_dim': 1,
'top': opNames[0]
})
elif lType == 'Loss':
lossType = ou.ints_to_str(lParam['loss'])
if lossType == 'pdist':
p = lParam['p'][0][0]
if p == 2:
lossName = 'EuclideanLoss'
else:
raise Exception('Loss type %s not recognized' % lossType)
else:
raise Exception('Loss type %s not recognized' % lossType)
lDef = mpu.get_layerdef_for_proto(lossName, name, ipNames[0],
**{'bottom2': ipNames[1]})
elif lType == 'gaussRender':
lDef = mpu.get_layerdef_for_proto(
lType, name, ipNames[0], **{
'top': opNames[0],
'K': lParam['K'][0][0],
'T': lParam['T'][0][0],
'sigma': lParam['sigma'][0][0],
'imgSz': int(lParam['img_size'][0][0])
})
else:
raise Exception('Layer Type %s not recognized, %d' % (lType, lNum))
return lDef
def make_caffe_layer(self, lNum):
#Get the name
nameRef = self.dat_['net']['layers']['name'][lNum][0]
name = ou.ints_to_str(self.dat_['#refs#'][nameRef][:])
#Get the inputs
ipRef = self.dat_['net']['layers']['inputs'][lNum][0]
ipNames = self.ref_to_str(ipRef)
#Get the parameter names
pmRef = self.dat_['net']['layers']['params'][lNum][0]
pmNames = self.ref_to_str(pmRef)
#Get the output names
opRef = self.dat_['net']['layers']['outputs'][lNum][0]
opNames = self.ref_to_str(opRef)
#Get the layer type
tpRef = self.dat_['net']['layers']['type'][lNum][0]
lType = ou.ints_to_str(self.dat_['#refs#'][tpRef][:])
#Get the layer params
lpRef = self.dat_['net']['layers']['block'][lNum][0]
lParam = self.dat_['#refs#'][lpRef]
assert (lType[0:5] == 'dagnn')
lType = lType[6:]
if lType == 'Conv':
paramW = {'name': pmNames[0]}
paramB = {'name': pmNames[1]}
pDupKey = mpu.make_key('param', ['param'])
lDef = mpu.get_layerdef_for_proto('Convolution', name, ipNames[0],
**{'num_output': int(lParam['size'][3][0]),
'param': paramW, pDupKey: paramB,
'kernel_size': int(lParam['size'][0][0]),
'stride': int(lParam['stride'][0][0]),
'pad': int(lParam['pad'][0][0])})
elif lType == 'ReLU':
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'top': opNames[0]})
elif lType == 'Pooling':
poolType = lParam['method'][0]
if poolType == 'max':
poolType = 'MAX'
elif poolType == 'avg':
poolType = 'AVE'
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'top': opNames[0], 'kernel_size': int(lParam['poolSize'][0][0]),
'stride': int(lParam['stride'][0][0]), 'pad': int(lParam['pad'][0][0]),
'pool': poolType})
elif lType == 'LRN':
N, kappa, alpha, beta = lParam['param'][0][0], lParam['param'][1][0],\
lParam['param'][2][0], lParam['param'][3][0]
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'top': opNames[0],
'local_size': int(N),
'alpha': N * alpha,
'beta' : beta,
'k' : kappa})
elif lType == 'Concat':
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'bottom2': ipNames[1:],
'concat_dim': 1,
'top': opNames[0]})
elif lType == 'Loss':
lossType = ou.ints_to_str(lParam['loss'])
if lossType == 'pdist':
p = lParam['p'][0][0]
if p == 2:
lossName = 'EuclideanLoss'
else:
raise Exception('Loss type %s not recognized' % lossType)
else:
raise Exception('Loss type %s not recognized' % lossType)
lDef = mpu.get_layerdef_for_proto(lossName, name, ipNames[0],
**{'bottom2': ipNames[1]})
elif lType == 'gaussRender':
lDef = mpu.get_layerdef_for_proto(lType, name, ipNames[0],
**{'top': opNames[0],
'K': lParam['K'][0][0], 'T': lParam['T'][0][0],
'sigma': lParam['sigma'][0][0], 'imgSz': int(lParam['img_size'][0][0])})
else:
raise Exception('Layer Type %s not recognized, %d' % (lType, lNum))
return lDef
