import data_transforms
import data_iterators
import pathfinder
import utils
import string
import numpy as np
import lasagne as nn
import utils_lung
# TODO: IMPORT A CORRECT PATCH CLASSIFICATION MODEL HERE
seg_config_name = 'luna_s3_p8'
# TODO: IMPORT A CORRECT PATCH CLASSIFICATION MODEL HERE
import configs_fpred_patch.luna_c1 as patch_class_config
p_transform = patch_class_config.p_transform
data_prep_function = patch_class_config.partial(patch_class_config.data_prep_function,
p_transform_augment=None,
p_transform=p_transform,
world_coord_system=False)
rng = patch_class_config.rng
# candidates after segmentations path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
segmentation_outputs_path = predictions_dir + '/%s' % seg_config_name
id2candidates = utils_lung.get_candidates_paths(segmentation_outputs_path)
data_iterator = data_iterators.FixedCandidatesLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
transform_params=p_transform,
data_prep_fun=data_prep_function,
id2candidates=id2candidates)
def build_model():
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, patch_class_config.__name__.split('.')[-1])
metadata = utils.load_pkl(metadata_path)
print 'Build model'
model = patch_class_config.build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print ' number of parameters: %d' % num_params
print string.ljust(' layer output shapes:', 36),
print string.ljust('#params:', 10),
print 'output shape:'
for layer in all_layers:
name = string.ljust(layer.__class__.__name__, 32)
num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
num_param = string.ljust(num_param.__str__(), 10)
print ' %s %s %s' % (name, num_param, layer.output_shape)
nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
return model
Datasets
Models
