Python set_configuration Examples

Example #1

File: evaluate_luna_fpred_scan.py Project: ericsolo/python

import sys
import numpy as np
import theano
import pathfinder
import utils
from configuration import set_configuration
import utils_lung

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
pid2candidates_path = utils_lung.get_candidates_paths(outputs_path)
pid2candidates = {}
for k, v in pid2candidates_path.iteritems():
    pid2candidates[k] = utils.load_pkl(v)

pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

n_top = 1
tp_top_n = 0
fp_top_n = 0
tp = 0
n_pos = 0

Example #2

import theano
from datetime import datetime, timedelta
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_class_dsb', config_name)
expid = utils.generate_expid(config_name)
print()
print("Experiment ID: %s" % expid)
print()

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print('Build model')

Example #3

File: test_seg_scan.py Project: cmiranda16PonceHealthSciencesUniversity/dsb3_Celso-Schematic-Spatial-Reducing-Inception-3D-Block

        if j in correct_blobs_idxs:
            print 'blob in original', blob_j_original
        blobs_original_voxel_coords.append(blob_j_original)

    blobs = np.asarray(blobs_original_voxel_coords)
    utils.save_pkl(blobs, outputs_path + '/%s.pkl' % pid)


jobs = []
theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_seg_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))

Example #4

File: test_dsb_data_iterator.py Project: ericsolo/python

import theano
from datetime import datetime, timedelta
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_class_dsb', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print 'Build model'

Example #5

File: test_transformations_scan.py Project: cmiranda16PonceHealthSciencesUniversity/dsb3_Celso-Schematic-Spatial-Reducing-Inception-3D-Block

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_slice_3d_2, plot_2d, plot_2d_4, plot_slice_3d_3
import utils_lung
import lung_segmentation

set_configuration('configs_seg_scan', 'luna_s_local')


def test1():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(
        pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
    print len(luna_data_paths)
    print id2zyxd.keys()

    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')

Example #6

File: train.py Project: RickBoss/Data-Science-Bowl-1

import lasagne as nn
import numpy as np
import theano
from datetime import datetime, timedelta
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration(config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print

# metadata
metadata_dir = utils.get_dir_path('train', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print 'Build model'

Example #7

import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_size_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_size_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))

Example #8

File: evaluate_dsb_fpred_scan.py Project: cmiranda16PonceHealthSciencesUniversity/dsb3_Celso-Schematic-Spatial-Reducing-Inception-3D-Block

from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict
import glob
import data_transforms

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions',
                                     pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
outputs_img_path = predictions_dir + '/%s_img' % config_name
utils.auto_make_dir(outputs_img_path)

blob_files = sorted(glob.glob(outputs_path + '/*.pkl'))

p_transform = {
    'patch_size': (64, 64, 64),
    'mm_patch_size': (64, 64, 64),
    'pixel_spacing': (1., 1., 1.)
}

Example #9

File: generate_features_dsb.py Project: ericsolo/python

import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: generate_features_dsb.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_gen_features', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))

Example #10

import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: generate_features_dsb.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_gen_features', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))

Example #11

import theano
import buffering
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import utils_lung
import logger

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_fpred_patch', config_name)

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-test.log' % expid)
sys.stderr = sys.stdout

# predictions path
predictions_dir = utils.get_dir_path('model-predictions',

Example #12

File: test_seg_patch.py Project: ericsolo/python

import theano
import buffering
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import utils_lung
import logger

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_seg_patch', config_name)

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-test.log' % expid)
sys.stderr = sys.stdout

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)

Example #13

File: test_predictions.py Project: cmiranda16PonceHealthSciencesUniversity/dsb3_Celso-Schematic-Spatial-Reducing-Inception-3D-Block

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_2d_animation, plot_slice_3d_3

set_configuration('test_config')


def test_luna3d():
    # path = '/mnt/sda3/data/kaggle-lung/lunapred/luna_scan_v3_dice-20170131-173443/'
    path = '/mnt/sda3/data/kaggle-lung/lunapred_el/luna_scan_v3_dice-20170201-231707/'
    files = os.listdir(path)
    print files
    x, y, p = [], [], []
    for f in files:
        if 'in' in f:
            x.append(f)
        elif 'tgt' in f:
            y.append(f)
        else:
            p.append(f)
    x = sorted(x)
    y = sorted(y)
    p = sorted(p)
    for xf, yf, pf in zip(x, y, p):
        x_batch = utils.load_pkl(path + xf)
        pred_batch = utils.load_pkl(path + pf)

Example #14

import theano
import buffering
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import utils_lung
import data_transforms

# theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_patch', config_name)

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# predictions path
predictions_dir = utils.get_dir_path('model-predictions',
                                     pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/' + expid
utils.auto_make_dir(outputs_path)

print 'Build model'

Example #15

File: train_luna_size_patch.py Project: ericsolo/python

import theano
from datetime import datetime, timedelta
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_size_patch', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print 'Build model'

Example #16

File: test_predictions.py Project: ericsolo/python

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_2d_animation, plot_slice_3d_3

set_configuration('test_config')


def test_luna3d():
    # path = '/mnt/sda3/data/kaggle-lung/lunapred/luna_scan_v3_dice-20170131-173443/'
    path = '/mnt/sda3/data/kaggle-lung/lunapred_el/luna_scan_v3_dice-20170201-231707/'
    files = os.listdir(path)
    print files
    x, y, p = [], [], []
    for f in files:
        if 'in' in f:
            x.append(f)
        elif 'tgt' in f:
            y.append(f)
        else:
            p.append(f)
    x = sorted(x)
    y = sorted(y)
    p = sorted(p)
    for xf, yf, pf in zip(x, y, p):
        x_batch = utils.load_pkl(path + xf)
        pred_batch = utils.load_pkl(path + pf)

Example #17

File: predict.py Project: Keesiu/meta-kaggle

                ])
                csvwriter.writerow(["%d_Systole" % prediction["patient"]] + [
                    "%.18f" % p
                    for p in prediction["systole_average"].flatten()
                ])
    print("submission file dumped")

    return


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description=__doc__)
    required = parser.add_argument_group('required arguments')
    required.add_argument('-c',
                          '--config',
                          help='configuration to run',
                          required=True)
    optional = parser.add_argument_group('optional arguments')
    optional.add_argument('-m',
                          '--metadata',
                          help='metadatafile to use',
                          required=False)

    args = parser.parse_args()
    set_configuration(args.config)

    expid = utils.generate_expid(args.config)
    mfile = args.metadata

    predict_model(expid, mfile)

Example #18

File: __init__.py Project: npyoung/mdp-toolkit

class MDPDeprecationWarning(DeprecationWarning, MDPWarning):
    """Warn about deprecated MDP API."""
    pass


import configuration

__version__ = '3.4'
__revision__ = configuration.get_git_revision()
__authors__ = 'MDP Developers'
__copyright__ = '(c) 2003-2012 [email protected]'
__license__ = 'BSD License, see COPYRIGHT'
__contact__ = '*****@*****.**'
__homepage__ = 'http://mdp-toolkit.sourceforge.net'

configuration.set_configuration()

config = configuration.config
(numx_description, numx, numx_linalg, numx_fft,
 numx_rand, numx_version) = configuration.get_numx()

# import the utils module (used by other modules)
import utils
# set symeig
utils.symeig = configuration.get_symeig(numx_linalg)

# import exceptions from nodes and flows
from signal_node import (NodeException, InconsistentDimException,
                         TrainingException,
                         TrainingFinishedException, IsNotTrainableException,
                         IsNotInvertibleException)

Example #19

File: predictions_dump.py Project: 317070/kaggle-heart

import buffering
import utils_heart
from configuration import config, set_configuration, set_subconfiguration
from pathfinder import METADATA_PATH

if not (len(sys.argv) < 3):
    sys.exit("Usage: predict.py <metadata_path>")

metadata_path = sys.argv[1]
metadata_dir = utils.get_dir_path('train', METADATA_PATH)
metadata = utils.load_pkl(metadata_dir + '/%s' % metadata_path)
config_name = metadata['configuration']
if 'subconfiguration' in metadata:
    set_subconfiguration(metadata['subconfiguration'])

set_configuration(config_name)

# predictions paths
prediction_dir = utils.get_dir_path('predictions', METADATA_PATH)
prediction_path = prediction_dir + "/%s.pkl" % metadata['experiment_id']
prediction_mu_std_path = prediction_dir + "/%s_mu_sigma.pkl" % metadata['experiment_id']

print "Build model"
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_top)
all_params = nn.layers.get_all_params(model.l_top)
num_params = nn.layers.count_params(model.l_top)
print '  number of parameters: %d' % num_params
nn.layers.set_all_param_values(model.l_top, metadata['param_values'])

xs_shared = [nn.utils.shared_empty(dim=len(l.shape)) for l in model.l_ins]

Example #20

File: predict_per_slice.py Project: fdoperezi/kaggle-heart

                        'predictions_per_slice': predictions,
                    }, f, pickle.HIGHEST_PROTOCOL)
    print "prediction file dumped"


    return


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description=__doc__)
    required = parser.add_argument_group('required arguments')
    required.add_argument('-c', '--config',
                          help='configuration to run',
                          required=True)
    required.add_argument('-o', '--output',
                          help='output file',
                          required=True)
    optional = parser.add_argument_group('optional arguments')
    optional.add_argument('-m', '--metadata',
                          help='metadatafile to use',
                          required=False)

    args = parser.parse_args()
    set_configuration(args.config)

    expid = utils.generate_expid(args.config)
    mfile = args.metadata
    ofile = args.output

    predict_slice_model(expid, ofile, mfile)

Example #21

File: luna_test_patient_segment.py Project: ericsolo/python

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_slice_3d_2, plot_2d, plot_2d_4, plot_slice_3d_3
import utils_lung
import lung_segmentation

set_configuration('configs_seg_scan', 'luna_s_local')

p_transform = {'patch_size': (416, 416, 416),
               'mm_patch_size': (416, 416, 416),
               'pixel_spacing': (1., 1., 1.)
               }


def test_luna3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = [
        'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']

    candidates = utils.load_pkl(
        'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')

Example #22

File: test_transformations_patch.py Project: neouuid/dsb3

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_slice_3d_2, plot_2d, plot_2d_4, plot_slice_3d_3

set_configuration('configs_luna_patch', 'luna_patch_local')


def test_luna_patches_3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(
        pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]

    # pid = '1.3.6.1.4.1.14519.5.2.1.6279.6001.138080888843357047811238713686'
    # luna_data_paths = [pathfinder.LUNA_DATA_PATH + '/%s.mhd' % pid]
    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        # img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')
        print(id)

Example #23

parser.add_argument("eval",
                    type=str,
                    help="test/valid/feat/train/test_tta/valid_tta")
parser.add_argument("--dump",
                    type=int,
                    default=0,
                    help="Should we store the predictions in raw format")
parser.add_argument(
    "--best",
    type=int,
    default=0,
    help="Should we use the best model instead of the last model")
args = parser.parse_args()

config_name = args.config_name
set_configuration('configs_pytorch', config_name)

all_tta_feat = args.eval == 'all_tta_feat'
feat = args.eval == 'feat'

train = args.eval == 'train'
train_tta = args.eval == 'train_tta'
train_tta_feat = args.eval == 'train_tta_feat'

valid = args.eval == 'valid'
valid_tta = args.eval == 'valid_tta'
valid_tta_feat = args.eval == 'valid_tta_feat'
valid_tta_majority = args.eval == 'valid_tta_majority'

test = args.eval == 'test'
test_tta = args.eval == 'test_tta'

Example #24

class MDPDeprecationWarning(DeprecationWarning, MDPWarning):
    """Warn about deprecated MDP API."""
    pass


import configuration

__version__ = '3.3'
__revision__ = configuration.get_git_revision()
__authors__ = 'MDP Developers'
__copyright__ = '(c) 2003-2011 [email protected]'
__license__ = 'BSD License, see COPYRIGHT'
__contact__ = '*****@*****.**'
__homepage__ = 'http://mdp-toolkit.sourceforge.net'

configuration.set_configuration()

config = configuration.config
(numx_description, numx, numx_linalg, numx_fft, numx_rand,
 numx_version) = configuration.get_numx()

# import the utils module (used by other modules)
import utils
# set symeig
utils.symeig = configuration.get_symeig(numx_linalg)

# import exceptions from nodes and flows
from signal_node import (NodeException, InconsistentDimException,
                         TrainingException, TrainingFinishedException,
                         IsNotTrainableException, IsNotInvertibleException)
from linear_flows import CrashRecoveryException, FlowException, FlowExceptionCR

Example #25

File: test_luna_props_scan_dsb.py Project: neouuid/dsb3

import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_props_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_props_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions',
                                     pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

Example #26

import dicom
import matplotlib
import matplotlib.pyplot as plt
import numpy as np

import utils

from matplotlib import animation
from pkl2patient import clean_metadata

import configuration
import data_loader
import utils

#configuration.set_configuration('je_test')
configuration.set_configuration('j8_ira_layer')
_config = configuration.config


def extract_image_patch(chunk_dst, img):
    """
    extract a correctly sized patch from img and place it into chunk_dst,
    which assumed to be preinitialized to zeros.
    """
    # # DEBUG: draw a border to see where the image ends up
    # img[0, :] = 127
    # img[-1, :] = 127
    # img[:, 0] = 127
    # img[:, -1] = 127

    p_x, p_y = chunk_dst.shape

Example #27

File: visualize_normscale_preprocessing.py Project: 317070/kaggle-heart

import dicom
import matplotlib
import matplotlib.pyplot as plt
import numpy as np

import utils

from matplotlib import animation
from pkl2patient import clean_metadata

import configuration
import data_loader
import utils

#configuration.set_configuration('je_test')
configuration.set_configuration('j8_ira_layer')
_config = configuration.config


def extract_image_patch(chunk_dst, img):
    """
    extract a correctly sized patch from img and place it into chunk_dst,
    which assumed to be preinitialized to zeros.
    """
    # # DEBUG: draw a border to see where the image ends up
    # img[0, :] = 127
    # img[-1, :] = 127
    # img[:, 0] = 127
    # img[:, -1] = 127

    p_x, p_y = chunk_dst.shape

Example #28

File: test.py Project: necrazy/edge_detection_framework

parser.add_argument("eval",
                    type=str,
                    help="test/valid/feat/train/test_tta/valid_tta")
parser.add_argument("--dump",
                    type=int,
                    default=0,
                    help="Should we store the predictions in raw format")
parser.add_argument(
    "--best",
    type=int,
    default=0,
    help="Should we use the best model instead of the last model")
args = parser.parse_args()

config_name = args.config_name
set_configuration('configs', config_name)

all_tta_feat = args.eval == 'all_tta_feat'
feat = args.eval == 'feat'

train = args.eval == 'train'
train_tta = args.eval == 'train_tta'
train_tta_feat = args.eval == 'train_tta_feat'

valid = args.eval == 'valid'
valid_tta = args.eval == 'valid_tta'
valid_tta_feat = args.eval == 'valid_tta_feat'
valid_tta_majority = args.eval == 'valid_tta_majority'

test = args.eval == 'test'
test_tta = args.eval == 'test_tta'

Example #29

File: test_luna_props_scan_dsb.py Project: ericsolo/python

import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import theano.tensor as T
import utils_lung
import blobs_detection
import logger
from collections import defaultdict

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: test_luna_props_scan.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_props_scan', config_name)

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
utils.auto_make_dir(outputs_path)

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
sys.stderr = sys.stdout

# builds model and sets its parameters
model = config().build_model()

x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))

Example #30

File: train_luna_prop_patch.py Project: ericsolo/python

from datetime import datetime, timedelta
from collections import defaultdict
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_luna_props_patch', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout

print 'Build model'

Example #31

File: test_fpred_patch.py Project: ericsolo/python

import theano
import buffering
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import utils_lung
import logger

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_fpred_patch', config_name)

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-test.log' % expid)
sys.stderr = sys.stdout

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)

Example #32

import theano
import buffering
import pathfinder
import utils
from configuration import config, set_configuration
from utils_plots import plot_slice_3d_3
import utils_lung
import logger

theano.config.warn_float64 = 'raise'

if len(sys.argv) < 2:
    sys.exit("Usage: train.py <configuration_name>")

config_name = sys.argv[1]
set_configuration('configs_seg_patch', config_name)

# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)

metadata = utils.load_pkl(metadata_path)
expid = metadata['experiment_id']

# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s-test.log' % expid)
sys.stderr = sys.stdout

# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)

Example #33

File: test_transformations_patch.py Project: ericsolo/python

import os
import numpy as np
import data_transforms
import pathfinder
import utils
import utils_lung
from configuration import set_configuration, config
from utils_plots import plot_slice_3d_2, plot_2d, plot_2d_4, plot_slice_3d_3

set_configuration('configs_luna_patch', 'luna_patch_local')


def test_luna_patches_3d():
    image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
    image_dir = image_dir + '/test_luna/'
    utils.auto_make_dir(image_dir)

    id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)

    luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
    luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]

    # pid = '1.3.6.1.4.1.14519.5.2.1.6279.6001.138080888843357047811238713686'
    # luna_data_paths = [pathfinder.LUNA_DATA_PATH + '/%s.mhd' % pid]
    for k, p in enumerate(luna_data_paths):
        img, origin, pixel_spacing = utils_lung.read_mhd(p)
        # img = data_transforms.hu2normHU(img)
        id = os.path.basename(p).replace('.mhd', '')
        print id

        annotations = id2zyxd[id]