def create_predncnn(config_file,
n_ch,
n_lab,
gpu=None,
override_MFP_to_active=False,
imposed_input_size=None,
param_file=None):
"""
Creates and compiles a CNN/NN as specified in a config file (used for training).
Loads the last parameters from the training directory.
The CNN/NN object is returned
Parameters
----------
config_file: string
Path to a CNN config file
n_ch: int
Number of input channels, for a MLP this is the dimensionality of the input vectors, for CNNs this is the
number of channels in an image/volume (e.g. 1 for plain gray value images)
n_lab: int
Number of distinct labels/classes
gpu: int
Specifying id of GPU to initialise for usage. E.g. 1 --> "gpu1", None will initialise gpu0,\
False will not initialise any GPU. This only works if "device" is not set in ``.theanorc`` or if theano
has not been imported up to now. If the initialisation fails an error will be printed but the script
will not crash.
override_MFP_to_active: Bool
If true, activates MFP in all layers where possible, ignoring the configuration in the config file.
This is useful for prediction using a config file from training. (only for CNN)
imposed_input_size: tuple or None
Similar as above, this can be used to impose another input size than specified in the config file.
(z,x,y)!!! (only for CNN)
param_file: string/None
If other parameters than "*-Last.param" should be loaded, this can specify the param file.
"""
config_file = os.path.expanduser(config_file)
if gpu == None:
gpu = default_config.device
config = Config(config_file,
gpu,
None,
use_existing_dir=True,
override_MFP_to_active=override_MFP_to_active,
imposed_input_size=imposed_input_size) # inits gpu
from elektronn.net.netcreation import createNet # import after gpu init
cnn = createNet(config, config.dimensions.input, n_ch, n_lab, config.dimensions) # 1 ch 2 label
if param_file is None:
os.chdir(config.save_path) # The trainer works directly in the save dir
path = "%s-LAST.param" % config.save_name
else:
path = os.path.expanduser(param_file)
cnn.loadParameters(path, strict=True)
return cnn
def createNet(self):
"""
Creates CNN according to config
"""
n_lab = self.data.n_lab
if self.config.class_weights is not None:
if self.config.target == 'nll':
assert len(self.config.class_weights) == n_lab,\
"The number of class weights must equal the number of classes"
if self.config.mode != 'vect-scalar': # image training
n_ch = self.data.n_ch
self.cnn = createNet(self.config, self.config.patch_size, n_ch, n_lab, self.config.dimensions)
else: # non-image training
n_ch = None
if self.config.rnn_layer_kwargs is not None:
n_ch = self.data.n_taps # must be None if the data should be repeated
self.cnn = createNet(self.config, self.data.example_shape, n_ch, n_lab, None)
def createNet(self):
"""
Creates CNN according to config
"""
n_lab = self.data.n_lab
if self.config.class_weights is not None:
if self.config.target == 'nll':
assert len(self.config.class_weights) == n_lab,\
"The number of class weights must equal the number of classes"
if self.config.mode != 'vect-scalar': # image training
n_ch = self.data.n_ch
self.cnn = createNet(self.config, self.config.patch_size, n_ch,
n_lab, self.config.dimensions)
else: # non-image training
n_ch = None
if self.config.rnn_layer_kwargs is not None:
n_ch = self.data.n_taps # must be None if the data should be repeated
self.cnn = createNet(self.config, self.data.example_shape, n_ch,
n_lab, None)
def create_predncnn(config_file,
n_ch,
n_lab,
gpu=None,
override_MFP_to_active=False,
imposed_input_size=None,
param_file=None):
"""
Creates and compiles a CNN/NN as specified in a config file (used for training).
Loads the last parameters from the training directory.
The CNN/NN object is returned
Parameters
----------
config_file: string
Path to a CNN config file
n_ch: int
Number of input channels, for a MLP this is the dimensionality of the input vectors, for CNNs this is the
number of channels in an image/volume (e.g. 1 for plain gray value images)
n_lab: int
Number of distinct labels/classes
gpu: int
Specifying id of GPU to initialise for usage. E.g. 1 --> "gpu1", None will initialise gpu0,\
False will not initialise any GPU. This only works if "device" is not set in ``.theanorc`` or if theano
has not been imported up to now. If the initialisation fails an error will be printed but the script
will not crash.
override_MFP_to_active: Bool
If true, activates MFP in all layers where possible, ignoring the configuration in the config file.
This is useful for prediction using a config file from training. (only for CNN)
imposed_input_size: tuple or None
Similar as above, this can be used to impose another input size than specified in the config file.
(z,x,y)!!! (only for CNN)
param_file: string/None
If other parameters than "*-Last.param" should be loaded, this can specify the param file.
"""
config_file = os.path.expanduser(config_file)
if gpu == None:
gpu = default_config.device
config = Config(config_file,
gpu,
None,
use_existing_dir=True,
override_MFP_to_active=override_MFP_to_active,
imposed_input_size=imposed_input_size) # inits gpu
from elektronn.net.netcreation import createNet # import after gpu init
cnn = createNet(config, config.dimensions.input, n_ch, n_lab,
config.dimensions) # 1 ch 2 label
if param_file is None:
os.chdir(
config.save_path) # The trainer works directly in the save dir
path = "%s-LAST.param" % config.save_name
else:
path = os.path.expanduser(param_file)
cnn.loadParameters(path, strict=True)
return cnn
