Source code for

# -*- coding: utf-8 -*-
# ELEKTRONN - Neural Network Toolkit
# Copyright (c) 2014 - now
# Max-Planck-Institute for Medical Research, Heidelberg, Germany
# Authors: Marius Killinger, Gregor Urban

import os
from import default_config, Config  # the global user-set config

# prevent Qt-backend on remote machines early! (other modules may import mpl)
#no_X = default_config.no_X
#hostname = socket.gethostname()    
#if hostname in default_config.no_X_hosts or no_X:
#  print "Importing Matplotlib without interactive backend, plots can only be saved to files in this session!"
#  import matplotlib
#  matplotlib.use('AGG')

[docs]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 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
if __name__ == "__main__": import numpy as np path = "~/devel/ELEKTRONN/Other/config_files/" cnn = create_predncnn(path, 1, 2, gpu=0, override_MFP_to_active=True, imposed_input_size=(60, 300, 300)) x = np.random.rand(1, 800, 800, 200).astype(np.float32) y = cnn.predictDense(x)