postConstraintsLayerSize = config.constraintParms['postConstraintsLayerSize'] defaultLossFunc = config.modelParms['defaultLossFunction'] lossRotScale = config.modelParms['lossRotScale'] optimizerType = config.modelParms['optimizerType'] model_yaml_filename = config.modelParms['modelYamlFilename'] # Callbacks checkpoint_filename = config.checkpointParms['filename'] history_filename = config.trainingParms['histFilename'] # Get Files modelYamlsDict = config.trainPaths['model'] modelYamls = config.getOutputFiles(modelYamlsDict, True) saveModelPath = config.getFolderRef(modelYamls) model_filepath = os.path.join(saveModelPath, model_yaml_filename) checkpointFilesDict = config.trainPaths['checkpoint'] checkpointFiles = config.getOutputFiles(checkpointFilesDict, True) saveCheckpointPath = config.getFolderRef(checkpointFiles) checkpoint_filepath = os.path.join(saveCheckpointPath, checkpoint_filename) historyFilesDict = config.trainPaths['history'] historyFiles = config.getOutputFiles(historyFilesDict, True) saveHistoryPath = config.getFolderRef(historyFiles) history_filepath = os.path.join(saveHistoryPath, history_filename) # Get Callbacks List callbacksList = getCallbacksList(config, history_filepath, checkpoint_filepath)
print('%sReading Camera %s, Sequence %02d' % (' ' * 2, cam, seq))
calFile = config.getInputFiles(calFilesDict, seq)
if (calFile is None):
continue
origImages = config.getInputFiles(origImageFilesDict, seq, cam)
if (origImages is None):
continue
standardImages = config.getOutputFiles(standardImageFilesDict,
recalcStandardImages, seq, cam)
if (standardImages is None):
continue
standardImageFolder = config.getFolderRef(standardImages)
# get cal matrix for sequence
cameraCalMat = getCalMat(calFile, cam)
map_x, map_y = getMaps(destImageShape, standardDict['overlap'],
cameraCalMat)
numImages = len(origImages)
for idx, origImageName in enumerate(origImages):
if showProgress:
percentComplete = int(idx / numImages * 100)
if divmod(idx, 300)[1] == 0:
print('Percent Complete: %d%%' % percentComplete)
srcImage = cv.cvtColor(cv.imread(origImageName), cv.COLOR_BGR2GRAY)
def loadPredictions(tests, eType):
errors_list = []
preds_list = []
truth_list = []
filetruthrots_list = []
for i, test in enumerate(tests):
# if True:
# test = tests[0]
configFile = 'exp_configs/%s.yaml' % test
config = ThesisConfig(configFile)
# Parameters
name = config.expName
numOutputs = config.modelParms['numOutputs']
# Get Files
evalFilesDict = config.resultPaths['evaluations']
figFilesDict = config.resultPaths['figures']
truthFilesDict = config.kittiPrepared['truth']
figFolder = config.getFolderRef(
config.getOutputFiles(figFilesDict, True))
# Get Predictions Save File
evalFolder = ''
for pathSection in evalFilesDict['dir']:
evalFolder = os.path.join(evalFolder, pathSection)
predictionsFile = os.path.join(evalFolder, eType + '_predictions.hdf5')
if os.path.exists(predictionsFile):
# Load Predictions
y_pred_real, y_true_real, evalType, min_val_epoch, min_val_loss = loadPredFile(
predictionsFile)
print('Min Validation Loss: %s, Epoch %s' %
(min_val_loss, min_val_epoch))
# get test idxs
# numTested = y_true_real.shape[0]
splitFilesDict = config.kittiPrepared['split']
splitFile = config.getInputFiles(splitFilesDict)
with h5py.File(splitFile, 'r') as f:
if evalType == 'test':
turnIdxs = np.array(f['testTurnIdxs'])
nonTurnIdxs = np.array(f['testNonTurnIdxs'])
elif evalType == 'val':
turnIdxs = np.array(f['valTurnIdxs'])
nonTurnIdxs = np.array(f['valNonTurnIdxs'])
idxs = np.sort(np.concatenate(
(turnIdxs, nonTurnIdxs))) #[:numTested]
truthData = np.empty((0, 3))
for seq in config.usedSeqs:
truthFile = config.getInputFiles(truthFilesDict, seq)
with h5py.File(truthFile, 'r') as f:
rot_xyz = np.array(f['rot_xyz'])
truthData = np.append(truthData, rot_xyz, axis=0)
file_truth_rots = truthData[idxs, :]
# Calculate average loss in each direction
errors = y_true_real - y_pred_real
errors_list.append(errors)
preds_list.append(y_pred_real)
truth_list.append(y_true_real)
filetruthrots_list.append(file_truth_rots)
else:
print('predictions file %s does not exist' % predictionsFile)
return (errors_list, preds_list, truth_list, filetruthrots_list)
print('v Downsampling Images and Normalizing Pixels')
for cam in usedCams:
for seqStr in kittiSeqs:
seq = int(seqStr)
print('%sReading Camera %s, Sequence %02d' % (' ' * 2, cam, seq))
standardImages = config.getInputFiles(standardImageFilesDict, seq, cam)
if (standardImages is None):
continue
normImages = config.getOutputFiles(normImageFilesDict, recalcNormImages, seq, cam)
if (normImages is None):
continue
normImageFolder = config.getFolderRef(normImages)
numImages = len(standardImages)
for idx, standardImageName in enumerate(standardImages):
if showProgress:
percentComplete = int(idx/numImages*100)
if divmod(idx,300)[1]==0:
print('Percent Complete: %d%%' % percentComplete)
srcImage = cv.cvtColor(cv.imread(standardImageName), cv.COLOR_BGR2GRAY)
dstImage = cv.resize(srcImage, dsize=destImageShape[::-1], interpolation=cv.INTER_AREA)
srcImage = dstImage
dstImage = cv.normalize(srcImage, None, normRange[0], normRange[1], cv.NORM_MINMAX, dtype=cv.CV_32F)
for configFile in configFiles:
config = ThesisConfig(configFile)
# Parameters
name = config.expName
numOutputs = config.modelParms['numOutputs']
# Get Files
evalFilesDict = config.resultPaths['evaluations']
figFilesDict = config.resultPaths['figures']
figFolder = config.getFolderRef(config.getOutputFiles(figFilesDict, True))
# Get Predictions Save File
evalFolder = ''
for pathSection in evalFilesDict['dir']:
evalFolder = os.path.join(evalFolder, pathSection)
predictionsFile = os.path.join(evalFolder, evalType+'_predictions.hdf5')
if os.path.exists(predictionsFile):
# Load Predictions
with h5py.File(predictionsFile, 'r') as f:
from src.helpers.helper_functions import getOptimizer
from src.helpers.training_helpers import getCallbacksList, getGenerator, getTrainAndValGenerators
from src.helpers.custom_loss import scaledMSE_RT
import tensorflow as tf
from keras import backend as K
import keras.losses
# Load Configuration
configFile = 'exp_configs/scale_test_4.yaml'
config = ThesisConfig(configFile)
checkpoint_filename = config.checkpointParms['filename']
checkpointFilesDict = config.trainPaths['checkpoint']
checkpointFiles = config.getOutputFiles(checkpointFilesDict, True)
saveCheckpointPath = config.getFolderRef(checkpointFiles)
checkpoint_filepath = os.path.join(saveCheckpointPath, checkpoint_filename)
numOutputs = config.modelParms['numOutputs']
lossRotScale = config.modelParms['lossRotScale']
defaultLossFunc = config.modelParms['defaultLossFunction']
if numOutputs > 3:
lossFunc = scaledMSE_RT(lossRotScale)
keras.losses.lossFunction = lossFunc
else:
lossFunc = defaultLossFunc
modelPath = checkpoint_filepath.format(epoch=1)
model = load_model(modelPath)
model.summary()
# test = tests[0]
# if True:
#
for test in tests:
configFile = os.path.join('exp_configs', test + '.yaml')
config = ThesisConfig(configFile)
numOutputs = config.modelParms['numOutputs']
lossRotScale = config.modelParms['lossRotScale']
defaultLossFunc = config.modelParms['defaultLossFunction']
checkpointFilesDict = config.trainPaths['checkpoint']
checkpointFiles = config.getInputFiles(checkpointFilesDict)
if isinstance(checkpointFiles, str):
checkpointFiles = np.array([checkpointFiles])
checkpointFolder = config.getFolderRef(checkpointFiles)
# Loss Function
if numOutputs > 3:
lossFunc = scaledMSE_RT(lossRotScale)
keras.losses.lossFunction = lossFunc
else:
lossFunc = defaultLossFunc
learning_rates = []
epochs = []
# FOR EACH MODEL
totalEpochs = len(checkpointFiles)
for modelFile in checkpointFiles:
t = time.time()
K.clear_session()
# 'exp_configs/CNN_test_12.yaml',
# 'exp_configs/CNN_test_13.yaml',
# 'exp_configs/CNN_test_14.yaml',
# 'exp_configs/CNN_test_15.yaml',
# 'exp_configs/CNN_test_16.yaml',
# 'exp_configs/CNN_test_17.yaml']
configFileList = ['exp_configs/scale_test_3.yaml']
for configFile in configFileList:
config = ThesisConfig(configFile)
history_filename = config.trainingParms['histFilename']
historyFilesDict = config.trainPaths['history']
historyFiles = config.getInputFiles(historyFilesDict)
saveHistoryPath = config.getFolderRef(historyFiles)
history_filepath = os.path.join(saveHistoryPath, history_filename)
saveFigFilesDict = config.resultPaths['figures']
figureFile = config.getOutputFiles(saveFigFilesDict, True)
saveFigFolder = config.getFolderRef(figureFile)
saveFigFilename = config.experiment['experiment'][
'name'] + config.resultPaths['figures']['type']
saveFigFile = os.path.join(saveFigFolder, saveFigFilename)
if os.path.exists(history_filepath):
print()
with h5py.File(history_filepath, 'r') as f:
epochs = np.array(f['epochs'], dtype=np.int)
numEpochs = len(epochs)
if 'loss' in f:
train_loss = np.array(f['loss'])
shuffle=False)
# LOAD MODEL
min_val_loss, min_val_epoch = getBestValLoss(config)
print('Min Validation Loss: %s, Epoch %s' % (min_val_loss, min_val_epoch))
# Loss Function
if numOutputs > 3:
lossFunc = scaledMSE_RT(lossRotScale)
keras.losses.lossFunction = lossFunc
else:
lossFunc = defaultLossFunc
checkpointFiles = config.getInputFiles(checkpointFilesDict)
checkpointFolder = config.getFolderRef(checkpointFiles)
checkpointFile = 'epoch_%03d%s' % (min_val_epoch,
checkpointFilesDict['type'])
modelFile = os.path.join(checkpointFolder, checkpointFile)
model = load_model(modelFile)
# PREDICT
y_true_norm = np.empty((0, numOutputs))
y_pred_norm = np.empty((0, numOutputs))
total = len(seqGen)
for idx, data in enumerate(seqGen):
percentComplete = int(idx / total * 100)
if divmod(idx, 50)[1] == 0:
print('Percent Complete: %d%%' % percentComplete)
x, y = data