def trainCNN(x,y,solverDir,cellIdx):
# parse Caffe's param prototxt
netDir,solverFn = os.path.split(solverDir)
projDir = os.getcwd()
if len(netDir)>0:
os.chdir(netDir)
solverParam = caffe_pb2.SolverParameter()
text_format.Merge(open(solverFn).read(),solverParam)
netFn = solverParam.net
netParam = caffe_pb2.NetParameter()
text_format.Merge(open(netFn).read(),netParam)
batchDim = utils.infer_data_dimensions(netFn)
print('[train]: batch shape: %s' %str(batchDim))
preDir = str(solverParam.snapshot_prefix) # unicode ---> str
outDir = os.path.join(projDir,preDir)
if os.path.isdir(outDir):
os.mkdir(outDir)
print('[train]: training data shape: %s' %(str(x.shape)) )
# Create the caffe solover
solver = caffe.SGDSolver(solverFn)
for name, blobs in solver.net.params.iteritems():
for bIdx, b in enumerate(blobs):
print("%s[%d] : %s" %( name,bIdx,b.data.shape))
GPU = solverParam.solver_mode # cpu 0 , gpu 1
if GPU :
isModeCPU = 0
gpuId = 0
else:
isModeCPU = (solverParam.solver_mode == solverParam.SolverMode.Value('CPU'))
gpuId = 0
# different edition has different caffe API
try:
if not isModeCPU:
caffe.set_mode_gpu()
caffe.set_device(gpuId)
else:
caffe.set_mode_cpu()
# caffe.set_phase_train() # Needn't set phase as Train
except AttributeError:
if not isModeCPU:
caffe.set_mode_gpu()
# caffe.set_device(gpuId)
solver.net.set_device(gpuId)
else:
solver.net.set_mode_cpu()
solver.net.set_phase_train()
losses, acc = train_loop(solver, x, y, solverParam, batchDim, outDir)
solver.net.save('%d_final.caffemodel'%(cellIdx))
# np.save(os.path.join(outDir, '%s_losses' % outDir), losses)
# np.save(os.path.join(outDir, '%s_acc' % outDir), acc)
print('[train]: ID %d done pretrain!' %(cellIdx))
# change to projDir
if len(projDir) >0 :
os.chdir(projDir)
return losses, acc
def testCNN(x, solverDir,cellId):
curDir = os.getcwd()
netDir, solverFn = os.path.split(solverDir)
# change to prototext Dirs
if len(netDir)>0:
os.chdir(netDir)
solverParam = caffe_pb2.SolverParameter()
text_format.Merge(open(solverFn).read(),solverParam)
netFn = str(solverParam.net)
netParam = caffe_pb2.NetParameter()
text_format.Merge(open(netFn).read(),netParam)
modelDir = str(cellId)+'_final.caffemodel'
print( modelDir )
net = caffe.Net(netFn,modelDir, caffe.TEST)
for name, blobs in net.params.iteritems():
print("%s:%s" %(name,blobs[0].data.shape))
GPU = solverParam.solver_mode # cpu 0 , gpu 1
if GPU :
isModeCPU = 0
gpuId = 0
else:
isModeCPU = (solverParam.solver_mode == solverParam.SolverMode.Value('CPU'))
gpuId = 0
# different edition has different caffe API
try:
if not isModeCPU:
caffe.set_mode_gpu()
caffe.set_device(gpuId)
else:
caffe.set_mode_cpu()
# caffe.set_phase_train()
except AttributeError:
if not isModeCPU:
net.set_mode_gpu()
net.set_device(gpuId)
else:
net.set_mode_cpu()
net.set_phase_train()
print("[test:] GPU mode: %s" % GPU )
batchDim = utils.infer_data_dimensions(netFn)
print("[test:] batch shape:%s" %(batchDim))
Xi = np.zeros((batchDim[0],1,28,28),dtype=np.float32)
Yi = np.zeros((batchDim[0],),dtype=np.float32)
xmean = np.mean(x)
x = x - xmean
maxValue = -1
for idx in utils.testDataGenerator(x,batchDim[0]):
# resize
for ii in range( 0, min(batchDim[0]-1, np.max(idx)+1) ):
temp = symc.imresize(Xi[ii,0,:,:],[28,28])
Xi[ii,0,:,:] = np.float32(temp)
if np.max(idx)<(batchDim[0]-1):
Xi[idx::,:,:,:] = x[idx::,:,:,:] # work ? or not
net.set_input_arrays(Xi,Yi)
rv = net.forward()
out = rv['prob']
out = np.squeeze(out)
ymax = np.max(out[:,0]) # 0 ???????????????? is positive???
yidx = np.argmax(out[:,0])
if ymax>maxValue:
maxValue = ymax
yidx = yidx
# yidx in appended data
if yidx > batchDim[0]-1:
yidx = batchDim[0]-1
if len(curDir)>0:
os.chdir(curDir)
return yidx,maxValue
def updateTrain(x,y,solverDir,cellIdx):
# finetune
projDir = os.getcwd()
netDir, solverFn = os.path.split(solverDir)
# change to prototext Dirs
if len(netDir)>0:
os.chdir(netDir)
solverParam = caffe_pb2.SolverParameter()
text_format.Merge(open(solverFn).read(),solverParam)
netFn = str(solverParam.net)
netParam = caffe_pb2.NetParameter()
text_format.Merge(open(netFn).read(),netParam)
modelDir = str(cellIdx)+'_final.caffemodel'
solverParam.max_iter = 10
print( modelDir )
solver = caffe.SGDSolver(solverDir)
solver.net.copy_from(modelDir)
batchDim = utils.infer_data_dimensions(netFn)
print('[train]: batch shape: %s' %str(batchDim))
preDir = str(solverParam.snapshot_prefix) # unicode ---> str
outDir = os.path.join(projDir,preDir)
if os.path.isdir(outDir):
os.mkdir(outDir)
print('[train]: training data shape: %s' %(str(x.shape)) )
# Create the caffe solover
# solver = caffe.SGDSolver(solverFn)
for name, blobs in solver.net.params.iteritems():
for bIdx, b in enumerate(blobs):
print("%s[%d] : %s" %( name,bIdx,b.data.shape))
GPU = solverParam.solver_mode # cpu 0 , gpu 1
if GPU :
isModeCPU = 0
gpuId = 0
else:
isModeCPU = (solverParam.solver_mode == solverParam.SolverMode.Value('CPU'))
# different edition has different caffe API
try:
if not isModeCPU:
caffe.set_mode_gpu()
caffe.set_device(gpuId)
# solver.net.set_phase_train()
else:
caffe.set_mode_cpu()
# solver.net.set_phase_train() # Needn't set phase as Train
except AttributeError:
if not isModeCPU:
solver.net.set_mode_gpu()
# solver.net.set_phase_train()
# caffe.set_device(gpuId)
else:
solver.net.set_mode_cpu()
solver.net.set_phase_train()
losses, acc = train_loop(solver, x, y, solverParam, batchDim, outDir)
solver.net.save('%d_final.caffemodel'%(cellIdx))
print('[train]: ID %d done pretrain!' %(cellIdx))
# change to projDir
if len(projDir) >0 :
os.chdir(projDir)
# niter = 10
# train_loss = np.zeros(niter)
# for it in range(niter):
# solver.step(1) # SGD by Caffe
# # store the train loss
# train_loss[it] = solver.net.blobs['loss'].data
# if it % 10 == 0:
# print 'iter %d, finetune_loss=%f' % (it, train_loss[it])
# print 'done'
return
