best working malignancy network

configs_luna_props_patch/r_mal_2.py

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+import numpy as np
+import data_transforms
+import data_iterators
+import pathfinder
+import lasagne as nn
+
+from collections import OrderedDict, namedtuple
+from functools import partial
+import lasagne.layers.dnn as dnn
+import lasagne
+import theano.tensor as T
+import utils
+
+restart_from_save = False
+rng = np.random.RandomState(33)
+
+# transformations
+p_transform = {'patch_size': (48, 48, 48),
+               'mm_patch_size': (48, 48, 48),
+               'pixel_spacing': (1., 1., 1.)
+               }
+
+p_transform_augment = {
+    'translation_range_z': [-3, 3],
+    'translation_range_y': [-3, 3],
+    'translation_range_x': [-3, 3],
+    'rotation_range_z': [-180, 180],
+    'rotation_range_y': [-180, 180],
+    'rotation_range_x': [-180, 180]
+}
+
+positive_proportion = 0.5
+
+properties = ['diameter', 'calcification', 'lobulation', 'malignancy', 'margin', 'sphericity',
+              'spiculation', 'texture']
+nproperties = len(properties)
+
+
+def label_prep_function(annotation,properties_included):
+    patch_zyxd = annotation[:4]
+    if patch_zyxd[-1] == 0:
+        if len(properties_included)>0:
+            return np.asarray([0] * len(properties_included), dtype='float32')
+        else:
+            return np.asarray([0] * len(properties), dtype='float32')
+    else:
+        label = []
+        properties_dict = annotation[-1]
+        if len(properties_included)>0:
+            for p in properties_included:
+                label.append(properties_dict[p]/5.0)
+        else:
+            for p in properties:
+                    label.append(properties_dict[p])
+    return label
+
+
+
+# data preparation function
+def data_prep_function(data, patch_center, pixel_spacing, luna_origin, p_transform,
+                       p_transform_augment, world_coord_system, **kwargs):
+    x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
+                                                               luna_annotations=None,
+                                                               patch_center=patch_center,
+                                                               p_transform=p_transform,
+                                                               p_transform_augment=p_transform_augment,
+                                                               pixel_spacing=pixel_spacing,
+                                                               luna_origin=luna_origin,
+                                                               world_coord_system=world_coord_system)
+    x = data_transforms.hu2normHU(x)
+
+    return x
+
+
+data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment,
+                                   p_transform=p_transform, world_coord_system=True)
+data_prep_function_valid = partial(data_prep_function, p_transform_augment=None,
+                                   p_transform=p_transform, world_coord_system=True)
+
+# data iterators
+batch_size = 16
+nbatches_chunk = 1
+chunk_size = batch_size * nbatches_chunk
+
+train_valid_ids = utils.load_pkl(pathfinder.LUNA_VALIDATION_SPLIT_PATH)
+train_pids, valid_pids = train_valid_ids['train'], train_valid_ids['valid']
+
+
+train_data_iterator = data_iterators.CandidatesPropertiesLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                           batch_size=chunk_size,
+                                                                           transform_params=p_transform,
+                                                                           label_prep_fun=label_prep_function,
+                                                                           nproperties=nproperties,
+                                                                           data_prep_fun=data_prep_function_train,
+                                                                           rng=rng,
+                                                                           patient_ids=train_pids,
+                                                                           full_batch=True, random=True, infinite=True,
+                                                                           positive_proportion=positive_proportion,
+                                                                           random_negative_samples=True,
+                                                                           properties_included=["malignancy"])
+
+valid_data_iterator = data_iterators.CandidatesLunaValidDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                      transform_params=p_transform,
+                                                                      data_prep_fun=data_prep_function_valid,
+                                                                      patient_ids=valid_pids,
+                                                                      label_prep_fun=label_prep_function,
+                                                                      properties_included=["malignancy"])
+
+nchunks_per_epoch = train_data_iterator.nsamples / chunk_size
+max_nchunks = nchunks_per_epoch * 100
+
+validate_every = int(5 * nchunks_per_epoch)
+save_every = int(1. * nchunks_per_epoch)
+
+learning_rate_schedule = {
+    0: 1e-4,
+    int(max_nchunks * 0.4): 6e-5,
+    int(max_nchunks * 0.6): 3e-5,
+    int(max_nchunks * 0.8): 1e-5,
+    int(max_nchunks * 0.9): 0.5e-5
+}
+
+# model
+conv3d = partial(dnn.Conv3DDNNLayer,
+                 filter_size=3,
+                 pad='same',
+                 W=nn.init.Orthogonal(),
+                 b=nn.init.Constant(0.01),
+                 nonlinearity=nn.nonlinearities.very_leaky_rectify)
+
+max_pool3d = partial(dnn.MaxPool3DDNNLayer,
+                     pool_size=2)
+
+drop = lasagne.layers.DropoutLayer
+
+bn = lasagne.layers.batch_norm
+
+dense = partial(lasagne.layers.DenseLayer,
+                W=lasagne.init.Orthogonal('relu'),
+                b=lasagne.init.Constant(0.0),
+                nonlinearity=lasagne.nonlinearities.rectify)
+
+
+def inrn_v2(lin):
+    n_base_filter = 32
+
+    l1 = conv3d(lin, n_base_filter, filter_size=1)
+
+    l2 = conv3d(lin, n_base_filter, filter_size=1)
+    l2 = conv3d(l2, n_base_filter, filter_size=3)
+
+    l3 = conv3d(lin, n_base_filter, filter_size=1)
+    l3 = conv3d(l3, n_base_filter, filter_size=3)
+    l3 = conv3d(l3, n_base_filter, filter_size=3)
+
+    l = lasagne.layers.ConcatLayer([l1, l2, l3])
+
+    l = conv3d(l, lin.output_shape[1], filter_size=1)
+
+    l = lasagne.layers.ElemwiseSumLayer([l, lin])
+
+    l = lasagne.layers.NonlinearityLayer(l, nonlinearity=lasagne.nonlinearities.rectify)
+
+    return l
+
+
+def inrn_v2_red(lin):
+    # We want to reduce our total volume /4
+
+    den = 16
+    nom2 = 4
+    nom3 = 5
+    nom4 = 7
+
+    ins = lin.output_shape[1]
+
+    l1 = max_pool3d(lin)
+
+    l2 = conv3d(lin, ins // den * nom2, filter_size=3, stride=2)
+
+    l3 = conv3d(lin, ins // den * nom2, filter_size=1)
+    l3 = conv3d(l3, ins // den * nom3, filter_size=3, stride=2)
+
+    l4 = conv3d(lin, ins // den * nom2, filter_size=1)
+    l4 = conv3d(l4, ins // den * nom3, filter_size=3)
+    l4 = conv3d(l4, ins // den * nom4, filter_size=3, stride=2)
+
+    l = lasagne.layers.ConcatLayer([l1, l2, l3, l4])
+
+    return l
+
+
+def feat_red(lin):
+    # We want to reduce the feature maps by a factor of 2
+    ins = lin.output_shape[1]
+    l = conv3d(lin, ins // 2, filter_size=1)
+    return l
+
+
+
+
+
+def build_model():
+    l_in = nn.layers.InputLayer((None,1) + p_transform['patch_size'])
+    l_target = nn.layers.InputLayer((None, 1))
+    l = conv3d(l_in, 64)
+    l = inrn_v2_red(l)
+    l = inrn_v2(l)
+
+    l = inrn_v2_red(l)
+    l = inrn_v2(l)
+
+    l = inrn_v2_red(l)
+    l = inrn_v2_red(l)
+
+    l = dense(drop(l), 512)
+
+    l_out = nn.layers.DenseLayer(l,1,nonlinearity=nn.nonlinearities.sigmoid, W=lasagne.init.Orthogonal(),
+                b=lasagne.init.Constant(0))
+
+    return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
+
+
+d_objectives_deterministic = {}
+d_objectives = {}
+
+
+def build_objective(model, deterministic=False):
+    predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
+    targets = T.flatten(nn.layers.get_output(model.l_target))
+    objective = lasagne.objectives.squared_error(predictions,targets)
+    loss = T.mean(objective)
+    return loss
+
+
+def build_updates(train_loss, model, learning_rate):
+    updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out, trainable=True), learning_rate)
+    return updates