isTheanoShape=parIsTheanoShape)
batcherVal = BatcherOnImageCT3D(pathDataIdx=fidxVal,
pathMeanData=batcherTrain.pathMeanData,
isTheanoShape=parIsTheanoShape)
print (':: Train data: %s' % batcherTrain)
print (':: Val data: %s' % batcherVal)
#
wdir = os.path.dirname(fidxTrain)
# modelTrained = BatcherOnImageCT3D.loadModelFromDir(wdir, paramFilter='950')
# modelTrained = BatcherOnImageCT3D.loadModelFromDir(wdir)
# modelTrained = BatcherOnImageCT3D.loadModelFromDir(wdir, paramFilter='adagrad')
modelTrained = BatcherOnImageCT3D().loadModelFromDir(wdir, paramFilter='adam')
modelTrained.summary()
#
t0 = time.time()
lstIdxSplit = split_list_by_blocks(range(batcherVal.numImg), parBatchSizeVal)
numSplit = len(lstIdxSplit)
for ii,ll in enumerate(lstIdxSplit):
print ('[%d/%d] process batch size [%d]' % (ii, numSplit, len(ll)))
dataX, dataY = batcherVal.getBatchDataByIdx(parBatchIdx=ll)
tret = modelTrained.predict_on_batch(dataX)
# convert to 3D-data
if batcherVal.isTheanoShape:
tshape3D = list(dataX.shape[2:])
tret3D = tret.reshape([dataX.shape[0]] + tshape3D + [tret.shape[-1]])
dataY3D = dataY.reshape([dataX.shape[0]] + tshape3D + [tret.shape[-1]])
else:
tret3D = tret
dataY3D = dataY
sizSplit = len(ll)
for iidx,idx in enumerate(ll):
dataX, dataY = batcherTrn.getBatchDataSliced(
parNumImages=parBatchNumImgTrn,
parNumSlices=parBatchNumSlcTrn,
isReturnDict=False)
tret = model.train_on_batch(dataX, dataY)
if (ii % stepPrintTrn) == 0:
print(
'\t[train] epoch [%d/%d], iter = [%d/%d] : loss[iter]/acc[iter] = %0.3f/%0.2f%%'
% (eei, parNumEpoch, ii, parNumIterPerEpochTrn, tret[0],
100. * tret[1]))
tmpDT = time.time() - tmpT1
print('\t*** train-time for epoch #%d is %0.2fs' % (eei, tmpDT))
# (2) model validation step
if ((eei + 1) % 3) == 0:
tmpIdxListSclices = split_list_by_blocks(
range(batcherTrn.numSlices,
batcherTrn.sizeZ - batcherTrn.numSlices),
parBatchNumSlcVal)
tmpIdxList = range(batcherVal.numImg)
# lstIdxSplit = split_list_by_blocks(tmpIdxList, parBatchSizeVal)
tmpVal = []
for imgIdx in tmpIdxList:
for tlistSliceIdx in tmpIdxListSclices:
dataX, dataY = batcherTrn.getBatchDataSlicedByIdx(
dictImg2SliceIdx={imgIdx: tlistSliceIdx},
isReturnDict=False)
tret = model.evaluate(dataX, dataY, verbose=False)
tmpVal.append(tret)
if (imgIdx % stepPrintVal) == 0:
print(
'\t\t[val] epoch [%d/%d], iter = [%d/%d] : loss[iter]/acc[iter] = %0.3f/%0.2f%%'
% (eei, parNumEpoch, imgIdx, parNumIterPerEpochVal,
numSlicesProc) / parBatchSizeVal
stepPrintVal = int(parNumIterPerEpochVal / 5)
#
model = batcherTrn.loadModelFromDir(dir_with_models=batcherTrn.wdir,
paramFilter=parExportInfo)
# model.compile(loss='categorical_crossentropy', optimizer=parOptimizer, metrics=['accuracy'])
# model.summary()
for ii in range(batcherVal.numImg):
tpathMsk = batcherVal.arrPathDataMsk[ii]
if K.image_dim_ordering() == 'th':
raise NotImplementedError
else:
tsegm3D = np.zeros(batcherVal.shapeImg[:-1], np.float)
lstIdxScl = range(batcherVal.numSlices,
batcherVal.sizeZ - batcherVal.numSlices)
lstIdxScl = split_list_by_blocks(lstIdxScl, parBatchNumSlcVal)
for ss, sslst in enumerate(lstIdxScl):
dataX, dataY = batcherVal.getBatchDataSlicedByIdx(
{ii: sslst}, isReturnDict=False)
tret = model.predict_on_batch(dataX)
if K.image_dim_ordering() == 'th':
raise NotImplementedError
else:
sizXY = batcherVal.shapeImg[:2]
tret = tret.transpose((1, 0, 2))
tret = tret.reshape(list(sizXY) + list(tret.shape[1:]))
tmskSlc = (tret[:, :, :, 1] > 0.5).astype(np.float)
tsegm3D[:, :, sslst] = tmskSlc
tmsk = nib.load(tpathMsk)
tsegm = nib.Nifti1Image(tsegm3D.copy().astype(np.float16),
tmsk.affine,
tmpT1 = time.time()
for ii in range(parNumIterPerEpochTrn):
dataX, dataY = batcherTrn.getBatchData(
parBatchSize=parBatchSizeTrn, isRemoveMean=True)
tret = model.train_on_batch(dataX, dataY)
if (ii % stepPrintTrn) == 0:
print(
'\t[train] epoch [%d/%d], iter = [%d/%d] : loss[iter]/acc[iter] = %0.3f/%0.2f%%'
% (eei, parNumEpoch, ii, parNumIterPerEpochTrn, tret[0],
100. * tret[1]))
tmpDT = time.time() - tmpT1
print('\t*** train-time for epoch #%d is %0.2fs' % (eei, tmpDT))
# (2) model validation step
if ((eei + 1) % 3) == 0:
tmpIdxList = range(batcherVal.numImg)
lstIdxSplit = split_list_by_blocks(tmpIdxList, parBatchSizeVal)
tmpVal = []
for ii, ll in enumerate(lstIdxSplit):
dataX, dataY = batcherVal.getBatchDataByIdx(parBatchIdx=ll,
isRemoveMean=True)
tret = model.evaluate(dataX, dataY, verbose=False)
tmpVal.append(tret)
if (ii % stepPrintVal) == 0:
print(
'\t\t[val] epoch [%d/%d], iter = [%d/%d] : loss[iter]/acc[iter] = %0.3f/%0.2f%%'
% (eei, parNumEpoch, ii, parNumIterPerEpochVal,
tret[0], 100. * tret[1]))
tmpVal = np.array(tmpVal)
tmeanValLoss = float(np.mean(tmpVal[:, 0]))
tmeanValAcc = float(np.mean(tmpVal[:, 1]))
print('\t::validation: mean-losss/mean-acc = %0.3f/%0.3f' %