def main(args):
config_file = args.config
test = args.test
cfg = Config(config_file)
tr = None
if test is None:
tr = DataSet(cfg.tr_data, cfg)
te = DataSet(cfg.te_data, cfg, sub_sample=1)
tr0 = DataSet([cfg.tr_data[0]], cfg, sub_sample=1)
cfg.att = te.sz[1]
else:
if test == 'te':
te = DataSet([cfg.te_data[0]], cfg)
else:
te = DataSet([cfg.tr_data[0]], cfg)
cfg.att = te.sz[1]
iterations = 10000
loop = cfg.loop
print "input attribute", cfg.att, "LR", cfg.lr, 'feature', cfg.feature_len
n_att = cfg.att
# n_length = cfg.feature_len
n_hidden = cfg.nodes[1][-1]
n_output = cfg.num_output
hidden0 = ToTensor(np.ones(n_hidden).astype(np.float32))
mrnn = FC(n_att * cfg.feature_len, cfg.nodes[0], n_output, cfg.lr)
if test:
mrnn.load_state_dict(torch.load(cfg.netTest[:-3]))
run_test(mrnn, te, cfg, hidden0)
tr_loss, tr_median = run_test(mrnn, te, cfg, hidden0)
for a in range(len(tr_loss)):
print a, tr_loss[a], tr_median[a]
exit(0)
if cfg.renetFile:
mrnn.load_state_dict(torch.load(cfg.renetFile[:-3]))
t00 = datetime.datetime.now()
T = 0
T_err = 0
for a in range(iterations):
tr_pre_data = tr.prepare(multi=1)
while tr_pre_data:
for b in tr_pre_data:
x = ToTensor(b[0].astype(np.float32))
y = ToTensor(b[1].astype(np.float32))
err = mrnn.train(y, x)
if a % loop == 0 and a > 0:
t1 = datetime.datetime.now()
print a, (t1 - t00).total_seconds() / 3600.0, T_err / T
T_err = 0
T = 0
torch.save(mrnn.state_dict(), cfg.netFile[:-3])
T_err += err
T += 1
tr_pre_data = tr.get_next()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from dataset import DatasetFactory, Config
from dataset.images import Image
Config().verbose = True
Config().dataset_type = 'wrapper_dataset'
ops = ('cropping', )
# val_ind
factory = DatasetFactory()
factory.add_image_type('image', 'label', 'mask', 'bounding_box')
factory.add_dataset(dataset_id='1', dirname='t_data_1', val_ind=[0, 2])
factory.add_dataset(dataset_id='2', dirname='t_data_2')
factory.add_dataset(dataset_id='3', dirname='t_data_3', val_ind=[0])
factory.add_training_operation(*ops)
t_dataset, v_dataset = factory.create()
t_keys = ['1/at1006', '2/at1025', '2/at1029', '3/at1034', '3/at1040']
v_keys = ['1/at1000', '1/at1007', '3/at1033']
assert list(t_dataset.images.keys()) == t_keys
assert list(v_dataset.images.keys()) == v_keys
for im in t_dataset[0]:
if hasattr(im, 'labels'):
print(im.labels)
assert isinstance(im, Image)
Config().dataset_type = 'dataset'
t_dataset, v_dataset = factory.create()
for im in t_dataset[0]:
def config():
_config = dict(key1='val1', key2=dict())
_config['key2']['subkey1'] = 'val21'
return Config(_config)
script_config['validation_batch_size'] = args.validation_batch_size
script_config['num_workers'] = args.num_workers
script_config['training_dir'] = args.training_dir
script_config['validation_dir'] = args.validation_dir
script_config['output_prefix'] = args.output_prefix
for key, value in script_config.items():
setattr(args, key, value)
print(checkpoint.keys())
TConfig.load_dict(checkpoint['trainer_config'])
TConfig.num_epochs = args.num_epochs
TConfig.model_period = args.model_period
TConfig.pred_period = args.pred_period
TConfig.val_period = args.val_period
DConfig.load_dict(checkpoint['dataset_config'])
LConfig.load_dict(checkpoint['layers_config'])
else:
script_config = args.__dict__
print('Script config')
keylen = max([len(key) + 1 for key in script_config.keys()])
for key, value in script_config.items():
print(' %s %s' % ((key + ':').ljust(keylen), value))
DConfig.verbose = args.verbose
DConfig.aug_prob = args.augmentation_prob
DConfig.crop_shape = args.cropping_shape
DConfig.image_shape = args.input_shape
DConfig.image_suffixes = [args.image_suffix]
DConfig.label_suffixes = [args.label_suffix]
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from dataset import Config
Config.show()
assert Config.image_suffixes == ['image']
Config.load_json('input.json')
assert Config.image_suffixes == ['image', 'hello']
Config.save_json('output.json')
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import nibabel as nib
from image_processing_3d import calc_bbox3d, resize_bbox3d, crop3d
from dataset import DatasetFactory, Config
from dataset.trees import Tree, TensorTree
ref_obj = nib.load('data/at1000_label.nii.gz')
# Config().dataset_type = 'wrapper_dataset'
Config().verbose = True
t_ops = ('cropping', 'label_normalization')
v_ops = ('cropping', 'label_normalization')
# t_ops = ('cropping', )
# v_ops = ('cropping', )
factory = DatasetFactory()
factory.add_image_type('image', 'hierachical_label', 'mask')
factory.add_dataset(dataset_id='tmc', dirname='data')
factory.add_dataset(dataset_id='kki', dirname='ped_data')
factory.add_training_operation(*t_ops)
factory.add_validation_operation(*v_ops)
t_dataset, v_dataset = factory.create()
mapping1 = {
'Anterior Lobe': [33, 36, 43, 46, 53, 56],
'Background': [0],
'Cerebellum': [