import lasagne as L
import lasagne.layers as LL
import lasagne.objectives as LO
from lasagne.layers.normalization import batch_norm
sys.path.extend(['/home/mabbasloo/ShapeNet/'])
from icnn import utils_lasagne, dataset, snapshotter
''' Data loading '''
base_path = '/home/mabbasloo/ShapeNet/carData/data/'
ds = dataset.ClassificationDatasetPatchesMinimal(
'/home/mabbasloo/ShapeNet/carData_train.txt',
'/home/mabbasloo/ShapeNet/carData_test.txt',
os.path.join(base_path, 'geovec'),
os.path.join(base_path, 'disk'),
None,
os.path.join(base_path, 'labels'),
epoch_size=50)
''' Network definition '''
nin = 100
nclasses = 1714
l2_weight = 1e-5
def get_model(inp, patch_op):
icnn = LL.DenseLayer(inp, 16)
icnn = batch_norm(
utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=4, nrays=8))
icnn = batch_norm(
import theano.tensor as T
import theano.sparse as Tsp
import lasagne as L
import lasagne.layers as LL
import lasagne.objectives as LO
from lasagne.layers.normalization import batch_norm
sys.path.append('..')
from icnn import utils_lasagne, dataset, snapshotter
base_path = '../data/'
ds = dataset.ClassificationDatasetPatchesMinimal(
'train.txt', 'test.txt',
os.path.join(base_path, 'descs'),
os.path.join(base_path, 'patch'),
None,
os.path.join(base_path, 'labels'),
epoch_size=100)
nin =795
nclasses = 6890
l2_weight = 1e-5
def get_model(inp, patch_op):
icnn1 = batch_norm(utils_lasagne.GCNNLayer([icnn, patch_op], 16, nrings=5, nrays=16))
ffn1 = icnn1
icnn2 = batch_norm(utils_lasagne.GCNNLayer([icnn1, patch_op], 32, nrings=5, nrays=16))
ffn2 = icnn2