dirName = "sub04_results"
try:
# Create target directory
os.mkdir(dirName)
print("Directory ", dirName, " was created")
except FileExistsError:
print("Directory", dirName, " already exists")
os.mkdir(dirName + "//training_results")
os.mkdir(dirName + "//validation_results")
os.mkdir(dirName + "//test_results")
debug = False # keep it False - othewise it does not use the trained network to predict
make_predictions(valimpaths,
valmaskpaths,
dirName,
mode='val',
cnn=cnn,
halfsize=halfsize,
debug=debug)
make_predictions(testimpaths,
testmaskpaths,
dirName,
mode='test',
cnn=cnn,
halfsize=halfsize,
debug=debug)
make_predictions(trainimpaths,
trainmaskpaths,
dirName,
mode='train',
cnn=cnn,
dirName = "sub10_results"
try:
# Create target directory
os.mkdir(dirName)
print("Directory ", dirName, " was created")
except FileExistsError:
print("Directory", dirName, " already exists")
os.mkdir(dirName + "//training_results")
os.mkdir(dirName + "//validation_results")
os.mkdir(dirName + "//test_results")
debug = False # keep it False - othewise it does not use the trained network to predict
make_predictions(valimpaths,
valmaskpaths,
dirName,
mode='val',
cnn=cnn,
patchsize=halfsize,
debug=debug) # patchsize=halfsize when data augmenting
make_predictions(testimpaths,
testmaskpaths,
dirName,
mode='test',
cnn=cnn,
patchsize=halfsize,
debug=debug)
make_predictions(trainimpaths,
trainmaskpaths,
dirName,
mode='train',
cnn=cnn,