def climate_test_1_3d(args={"sigma": 0.}):
conv = InputLayer((None, 16, 8, 96, 96))
conv = GaussianNoiseLayer(conv, sigma=args["sigma"])
conv = dnn.Conv3DDNNLayer(conv,
num_filters=128,
filter_size=(4, 5, 5),
stride=(1, 2, 2),
nonlinearity=rectify)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=256,
filter_size=(3, 5, 5),
stride=(1, 2, 2),
nonlinearity=rectify)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=512,
filter_size=(3, 5, 5),
stride=(1, 2, 2),
nonlinearity=rectify)
if "bottleneck" in args:
conv = DenseLayer(conv,
num_units=args["bottleneck"],
nonlinearity=rectify)
l_out = conv
print_network(l_out)
final_out, decoder_layers, encoder_layers = get_encoder_and_decoder(l_out)
ladder = []
for a, b in zip(decoder_layers, encoder_layers[::-1]):
ladder += [a, b]
return final_out, ladder
def full_image_net_1_3d_v3b(args):
conv = InputLayer((None, 16, 8, 768, 1152))
conv = GaussianNoiseLayer(conv, sigma=args["sigma"])
encoder_layers = []
conv = dnn.Conv3DDNNLayer(conv,
num_filters=196,
filter_size=(3, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=256,
filter_size=(3, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
#conv = dnn.Conv3DDNNLayer(conv, num_filters=512, filter_size=(3,5,5), stride=(1,2,2)); encoder_layers.append(conv)
#conv = dnn.Conv3DDNNLayer(conv, num_filters=1024, filter_size=(2,5,5), stride=(1,2,2)); encoder_layers.append(conv)
decoder_layers = []
for layer in get_all_layers(conv)[::-1]:
if isinstance(layer, InputLayer):
break
conv = InverseLayer(conv, layer)
decoder_layers.append(conv)
l_out = conv
# bug: get_encoder_and_decoder doesn't work for this
#final_out, decoder_layers, encoder_layers = get_encoder_and_decoder(l_out)
ladder = []
for a, b in zip(decoder_layers, encoder_layers[::-1]):
ladder += [a, b]
print_network(l_out)
return l_out, ladder
def build_model():
l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((None, 1))
l = dnn.Conv3DDNNLayer(l_in,
num_filters=64,
filter_size=3,
pad='valid',
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.01),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
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), 128)
l_out = nn.layers.DenseLayer(l, num_units=10,
W=lasagne.init.Orthogonal(),
b=lasagne.init.Constant(0.1),
nonlinearity=nn.nonlinearities.softmax)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_model():
l_in = nn.layers.InputLayer((None, n_candidates_per_patient, 1,) + p_transform['patch_size'])
l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size,))
l = dnn.Conv3DDNNLayer(l_in_rshp,
filter_size=3,
num_filters=64,
pad='valid',
W=nn.init.Orthogonal(),
nonlinearity=nn.nonlinearities.very_leaky_rectify)
print 'l1', l.output_shape
l = inrn_v2_red(l)
print 'l2', l.output_shape
l = inrn_v2(l)
print 'l3', l.output_shape
l = inrn_v2_red(l)
print 'l4', l.output_shape
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2_red(l)
l = dense(drop(l), 256)
l = nn.layers.DenseLayer(l, num_units=1, W=nn.init.Orthogonal(),
nonlinearity=None)
l = nn.layers.ReshapeLayer(l, (-1, n_candidates_per_patient, 1))
l_out = nn_lung.AggAllBenignExp(l)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_model():
l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
net = {}
base_n_filters = 64
net['contr_1_1'] = conv_prelu_layer(l_in, base_n_filters)
net['contr_1_2'] = conv_prelu_layer(net['contr_1_1'], base_n_filters)
net['contr_1_3'] = conv_prelu_layer(net['contr_1_2'], base_n_filters)
net['pool1'] = max_pool3d(net['contr_1_3'])
net['encode_1'] = conv_prelu_layer(net['pool1'], base_n_filters)
net['encode_2'] = conv_prelu_layer(net['encode_1'], base_n_filters)
net['encode_3'] = conv_prelu_layer(net['encode_2'], base_n_filters)
net['encode_4'] = conv_prelu_layer(net['encode_3'], base_n_filters)
net['upscale1'] = nn.layers.Upscale3DLayer(net['encode_4'], 2)
net['concat1'] = nn.layers.ConcatLayer([net['upscale1'], net['contr_1_3']],
cropping=(None, None, "center", "center", "center"))
net['expand_1_1'] = conv_prelu_layer(net['concat1'], base_n_filters)
net['expand_1_2'] = conv_prelu_layer(net['expand_1_1'], base_n_filters)
net['expand_1_3'] = conv_prelu_layer(net['expand_1_2'], base_n_filters)
net['expand_1_4'] = conv_prelu_layer(net['expand_1_3'], base_n_filters)
net['expand_1_5'] = conv_prelu_layer(net['expand_1_4'], base_n_filters)
l_out = dnn.Conv3DDNNLayer(net['expand_1_5'], num_filters=1,
filter_size=1,
nonlinearity=nn.nonlinearities.sigmoid)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
def build_model(image_size=(IMAGE_SIZE, IMAGE_SIZE, IMAGE_SIZE)):
l_in = lasagne.layers.InputLayer(shape=(None, ) + image_size)
l0 = lasagne.layers.DimshuffleLayer(l_in, pattern=[0, 'x', 1, 2, 3])
net = {}
base_n_filters = 64
net['contr_1_1'] = conv3d(l0, base_n_filters)
net['contr_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_1'])
net['contr_1_2'] = conv3d(net['contr_1_1'], base_n_filters)
net['contr_1_2'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_2'])
net['contr_1_3'] = conv3d(net['contr_1_2'], base_n_filters)
net['contr_1_3'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_3'])
net['pool1'] = max_pool3d(net['contr_1_3'])
net['encode_1'] = conv3d(net['pool1'], base_n_filters)
net['encode_1'] = lasagne.layers.ParametricRectifierLayer(net['encode_1'])
net['encode_2'] = conv3d(net['encode_1'], base_n_filters)
net['encode_2'] = lasagne.layers.ParametricRectifierLayer(net['encode_2'])
net['encode_3'] = conv3d(net['encode_2'], base_n_filters)
net['encode_3'] = lasagne.layers.ParametricRectifierLayer(net['encode_3'])
net['encode_4'] = conv3d(net['encode_3'], base_n_filters)
net['encode_4'] = lasagne.layers.ParametricRectifierLayer(net['encode_4'])
net['upscale1'] = lasagne.layers.Upscale3DLayer(net['encode_4'], 2)
net['concat1'] = lasagne.layers.ConcatLayer(
[net['upscale1'], net['contr_1_3']],
cropping=(None, None, "center", "center", "center"))
net['expand_1_1'] = conv3d(net['concat1'], 2 * base_n_filters)
net['expand_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_1'])
net['expand_1_2'] = conv3d(net['expand_1_1'], 2 * base_n_filters)
net['expand_1_2'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_2'])
net['expand_1_3'] = conv3d(net['expand_1_2'], base_n_filters)
net['expand_1_3'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_3'])
net['output_segmentation'] = dnn.Conv3DDNNLayer(
net['expand_1_3'],
num_filters=1,
filter_size=1,
nonlinearity=lasagne.nonlinearities.sigmoid)
l_out = lasagne.layers.SliceLayer(net['output_segmentation'],
indices=0,
axis=1)
return {
"inputs": {
"luna:3d": l_in,
},
"outputs": {
"predicted_segmentation": l_out
},
}
def build_model(patch_size=None):
patch_size = p_transform['patch_size'] if patch_size is None else patch_size
l_in = nn.layers.InputLayer((
None,
1,
) + patch_size)
l_target = nn.layers.InputLayer((
None,
1,
) + patch_size)
net = {}
base_n_filters = 128
net['contr_1_1'] = conv3d(l_in, base_n_filters)
net['contr_1_1'] = nn.layers.ParametricRectifierLayer(net['contr_1_1'])
net['contr_1_2'] = conv3d(net['contr_1_1'], base_n_filters)
net['contr_1_2'] = nn.layers.ParametricRectifierLayer(net['contr_1_2'])
net['contr_1_3'] = conv3d(net['contr_1_2'], base_n_filters)
net['contr_1_3'] = nn.layers.ParametricRectifierLayer(net['contr_1_3'])
net['pool1'] = max_pool3d(net['contr_1_3'])
net['encode_1'] = conv3d(net['pool1'], base_n_filters)
net['encode_1'] = nn.layers.ParametricRectifierLayer(net['encode_1'])
net['encode_2'] = conv3d(net['encode_1'], base_n_filters)
net['encode_2'] = nn.layers.ParametricRectifierLayer(net['encode_2'])
net['encode_3'] = conv3d(net['encode_2'], base_n_filters)
net['encode_3'] = nn.layers.ParametricRectifierLayer(net['encode_3'])
net['encode_4'] = conv3d(net['encode_3'], base_n_filters)
net['encode_4'] = nn.layers.ParametricRectifierLayer(net['encode_4'])
net['dropout_1'] = nn.layers.DropoutLayer(net['encode_4'])
net['upscale1'] = nn.layers.Upscale3DLayer(net['dropout_1'], 2)
net['concat1'] = nn.layers.ConcatLayer([net['upscale1'], net['contr_1_3']],
cropping=(None, None, "center",
"center", "center"))
net['expand_1_1'] = conv3d(net['concat1'], 2 * base_n_filters)
net['expand_1_1'] = nn.layers.ParametricRectifierLayer(net['expand_1_1'])
net['expand_1_2'] = conv3d(net['expand_1_1'], 2 * base_n_filters)
net['expand_1_2'] = nn.layers.ParametricRectifierLayer(net['expand_1_2'])
net['expand_1_3'] = conv3d(net['expand_1_2'], base_n_filters)
net['expand_1_3'] = nn.layers.ParametricRectifierLayer(net['expand_1_3'])
l_out = dnn.Conv3DDNNLayer(net['expand_1_3'],
num_filters=1,
filter_size=1,
nonlinearity=nn.nonlinearities.sigmoid)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out,
l_target)
def build_model():
l_in = lasagne.layers.InputLayer(shape=(None, IMAGE_SIZE, IMAGE_SIZE,
IMAGE_SIZE))
l0 = lasagne.layers.DimshuffleLayer(l_in, pattern=[0, 'x', 1, 2, 3])
net = {}
base_n_filters = 256
net['contr_1_1'] = conv3d(l0, base_n_filters)
net['contr_1_2'] = conv3d(net['contr_1_1'], base_n_filters)
net['pool1'] = max_pool3d(net['contr_1_2'])
net['contr_2_1'] = conv3d(net['pool1'], base_n_filters * 2)
net['contr_2_2'] = conv3d(net['contr_2_1'], base_n_filters * 2)
net['expand_3_1'] = conv3d(net['contr_2_2'], base_n_filters * 2)
net['expand_3_2'] = conv3d(net['expand_3_1'], base_n_filters * 2)
net['upscale4'] = Upscale3DLayer(net['expand_3_2'], 2)
net['concat4'] = lasagne.layers.ConcatLayer(
[net['upscale4'], net['contr_1_2']],
cropping=(None, None, "center", "center", "center"))
net['expand_4_1'] = conv3d(net['concat4'], base_n_filters)
net['expand_4_2'] = conv3d(net['expand_4_1'], base_n_filters)
net['output_segmentation'] = dnn.Conv3DDNNLayer(
net['expand_4_2'],
num_filters=1,
filter_size=1,
W=lasagne.init.Constant(0),
b=None,
nonlinearity=lasagne.nonlinearities.sigmoid)
l_out = lasagne.layers.SliceLayer(net['output_segmentation'],
indices=0,
axis=1)
return {
"inputs": {
"luna:3d": l_in,
},
"outputs": {
"predicted_segmentation": l_out
},
}
def build_layers(input_var, nk):
'''nk: network_kwargs'''
'''conv, extra_convs, pool multiple times then fc with dropout, fc with dropout and softmax then reshape'''
'''total number of conv layers is num_convpool * (1 + num_extra_conv)'''
filter_dim = nk['filter_dim']
num_layers = nk['num_layers']
filters_list = [128, 256, 512, 768, 1024, 1280]
conv = lasagne.layers.InputLayer(shape=nk['input_shape'])
for i in range(num_layers):
num_filters = int(nk["filters_scale"] * filters_list[i])
if nk["im_dim"] == 3:
conv = dnn.Conv3DDNNLayer(conv, num_filters=num_filters, filter_size=(3,5,5),pad=(1,2,2), stride=(1,2,2))
else:
#print nk["filters_scale"]
#print num_filters
conv = Conv2DLayer(conv,
num_filters=num_filters,
filter_size=nk['filter_dim'],
pad=nk['filter_dim'] / 2,
stride=2,
W=HeNormal(),
nonlinearity=LeakyRectify(0.1))
encoder = conv
hid_fmap = conv
if nk["yolo_batch_norm"]:
encoder = BatchNormLayer(encoder)
if nk["im_dim"] == 3:
#encoder = FeaturePoolLayer(encoder, pool_size=640, axis=1)
box_conf = dnn.Conv3DDNNLayer(encoder, num_filters=2, filter_size=(3,3,3),
stride=(2,1,1), pad=(1,1,1), nonlinearity=softmax4D)
#box_conf = NonlinearityLayer(box_conf, softmax4D)
class_conf = dnn.Conv3DDNNLayer(encoder, num_filters=nk['num_classes'], filter_size=(3,3,3), stride=(2,1,1), pad=(1,1,1), nonlinearity=softmax4D)
#class_conf = NonlinearityLayer(class_conf, softmax4D)
coord_net = dnn.Conv3DDNNLayer(encoder, num_filters=4, filter_size=(3,3,3), stride=(2,1,1), pad=(1,1,1), W=nk['w_init'],
nonlinearity=rectify)
#outputs a batch_size x 10 x 4 x 12 x 18
bbox_reg = ConcatLayer([coord_net,box_conf, class_conf])
#print get_output_shape(bbox_reg,input_shapes=(1,16,8,768,1152))
s = get_output_shape(bbox_reg)
# reshape to be like the 2D case -> batch_size*time_steps(4) x 10 x 12 x 18
net = ExpressionLayer(bbox_reg, function=lambda g: g.transpose((0,2,1,3,4)),
output_shape=(nk["batch_size"],s[2], s[1],s[3], s[4]))
#print get_output_shape(net,input_shapes=(1,16,8,768,1152))
# after transpose
s = get_output_shape(net)
bbox_reg = ReshapeLayer(net, shape=(nk["batch_size"],s[1], s[2],s[3], s[4]))
#print get_output_shape(bbox_reg,input_shapes=(1,16,8,768,1152))
else:
box_conf = Conv2DLayer(encoder, num_filters=2, filter_size=3, pad=1, nonlinearity=linear)
box_conf = NonlinearityLayer(box_conf, softmax3D)
class_conf = Conv2DLayer(encoder, num_filters=nk['num_classes'], filter_size=3, pad=1, nonlinearity=linear)
class_conf = NonlinearityLayer(class_conf, softmax3D)
coord_net = Conv2DLayer(encoder, num_filters=4, filter_size=3,pad=1, W=nk['w_init'],
nonlinearity=rectify)
bbox_reg = ConcatLayer([coord_net,box_conf, class_conf])
for layer in get_all_layers(conv)[::-1]:
if nk["batch_norm"]:
conv = batch_norm(conv)
if isinstance(layer, InputLayer):
break
conv = InverseLayer(conv, layer)
return {'yolo': bbox_reg, 'ae':conv, 'hid_fmap':hid_fmap}#, "decoder":decoder_layers}
def getmodel(X1, X2, X3, X4, seg, p_drop_hidden, inputsize, inputsize3D,
nrofinputs, classes):
#67x67 orthogonal input
segnetwork1 = lasagne.layers.InputLayer(shape=(None, nrofinputs, inputsize,
inputsize),
input_var=X1)
segnetwork2 = lasagne.layers.InputLayer(shape=(None, nrofinputs, inputsize,
inputsize),
input_var=X2)
segnetwork3 = lasagne.layers.InputLayer(shape=(None, nrofinputs, inputsize,
inputsize),
input_var=X3)
#25x25x25 input
segnetwork4 = lasagne.layers.InputLayer(shape=(None, nrofinputs,
inputsize3D, inputsize3D,
inputsize3D),
input_var=X4)
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(2, 2),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(2, 2),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(2, 2),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(4, 4),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(4, 4),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(4, 4),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(8, 8),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(8, 8),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(8, 8),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(16, 16),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(16, 16),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(16, 16),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
segnetwork1 = lasagne.layers.DilatedConv2DLayer(
segnetwork1,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork1 = lasagne.layers.batch_norm(segnetwork1)
segnetwork2 = lasagne.layers.DilatedConv2DLayer(
segnetwork2,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork2 = lasagne.layers.batch_norm(segnetwork2)
segnetwork3 = lasagne.layers.DilatedConv2DLayer(
segnetwork3,
num_filters=32,
filter_size=(3, 3),
dilation=(1, 1),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork3 = lasagne.layers.batch_norm(segnetwork3)
print segnetwork1.output_shape
for i in xrange(int((inputsize3D - 1) / 2)):
segnetwork4 = dnn.Conv3DDNNLayer(
segnetwork4,
num_filters=32,
filter_size=(3, 3, 3),
nonlinearity=lasagne.nonlinearities.rectify,
W=lasagne.init.GlorotUniform())
segnetwork4 = lasagne.layers.batch_norm(segnetwork4)
print segnetwork4.output_shape
segnetwork4 = lasagne.layers.ReshapeLayer(segnetwork4, ((-1, 32, 1, 1)))
print segnetwork4.output_shape
segnetwork = lasagne.layers.concat(
(segnetwork1, segnetwork2, segnetwork3, segnetwork4))
print segnetwork.output_shape
segnetwork = lasagne.layers.dropout(segnetwork, p=p_drop_hidden)
segnetwork = lasagne.layers.DilatedConv2DLayer(
segnetwork,
num_filters=classes,
filter_size=(1, 1),
dilation=(1, 1),
nonlinearity=softmax,
W=lasagne.init.GlorotUniform())
return segnetwork1, segnetwork2, segnetwork3, segnetwork4, segnetwork
def build_model():
#l_in = lasagne.layers.InputLayer((None, 1,) + p_transform['patch_size'])
# l_target = lasagne.layers.InputLayer((None, 1,) + p_transform['patch_size'])
l_in = lasagne.layers.InputLayer(shape=(None, 128, 128, 128))
l0 = lasagne.layers.DimshuffleLayer(l_in, pattern=[0, 'x', 1, 2, 3])
net = {}
base_n_filters = 64
net['contr_1_1'] = conv3d(l0, base_n_filters)
net['contr_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_1'])
net['contr_1_2'] = conv3d(net['contr_1_1'], base_n_filters)
net['contr_1_2'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_2'])
net['contr_1_3'] = conv3d(net['contr_1_2'], base_n_filters)
net['contr_1_3'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_3'])
net['pool1'] = max_pool3d(net['contr_1_3'])
net['encode_1'] = conv3d(net['pool1'], base_n_filters)
net['encode_1'] = lasagne.layers.ParametricRectifierLayer(net['encode_1'])
net['encode_2'] = conv3d(net['encode_1'], base_n_filters)
net['encode_2'] = lasagne.layers.ParametricRectifierLayer(net['encode_2'])
net['encode_3'] = conv3d(net['encode_2'], base_n_filters)
net['encode_3'] = lasagne.layers.ParametricRectifierLayer(net['encode_3'])
net['upscale1'] = lasagne.layers.Upscale3DLayer(net['encode_2'], 2)
net['concat1'] = lasagne.layers.ConcatLayer(
[net['upscale1'], net['contr_1_3']],
cropping=(None, None, "center", "center", "center"))
net['expand_1_1'] = conv3d(net['concat1'], 2 * base_n_filters)
net['expand_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_1'])
net['expand_1_2'] = conv3d(net['expand_1_1'], 2 * base_n_filters)
net['expand_1_2'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_2'])
net['expand_1_3'] = conv3d(net['expand_1_2'], base_n_filters)
net['expand_1_3'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_3'])
net['output_segmentation'] = dnn.Conv3DDNNLayer(
net['expand_1_3'],
num_filters=1,
filter_size=1,
nonlinearity=lasagne.nonlinearities.sigmoid)
l_out = lasagne.layers.SliceLayer(net['output_segmentation'],
indices=0,
axis=1)
#return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
return {
"inputs": {
"luna:3d": l_in,
},
"outputs": {
"predicted_segmentation": l_out
},
}
def full_image_net_1_3d_segmenter(args):
"""
Returns: encoder_out, segmentation_out, decoder_out
"""
# ###############
# X encoder part
# ###############
conv = InputLayer((None, 16, 4, 768, 1152))
conv = GaussianNoiseLayer(conv, sigma=args["sigma"])
encoder_layers = []
conv = dnn.Conv3DDNNLayer(conv,
num_filters=128,
filter_size=(3, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=256,
filter_size=(2, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=512,
filter_size=(1, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=768,
filter_size=(1, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=1024,
filter_size=(1, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
conv = dnn.Conv3DDNNLayer(conv,
num_filters=1280,
filter_size=(1, 5, 5),
stride=(1, 2, 2))
encoder_layers.append(conv)
# ###############
# classifier part
# ###############
conv2 = ReshapeLayer(conv, (-1, 1280, 9, 15)) # prep it before the 2d conv
conv2 = Conv2DLayer(conv2, num_filters=(3 * 4),
filter_size=(1,
1)) # we need masks for every time step
conv2 = ReshapeLayer(conv2, (-1, 3, 4, 9, 15)) # reshaping
l_out_for_X = NonlinearityLayer(conv2, nonlinearity=sigmoid)
print "classifier part:"
print_network(l_out_for_X)
# ###########
# Y decimator
# ###########
yconv = InputLayer((None, 3, 4, 768, 1152))
yconv = ReshapeLayer(yconv, (-1, 3 * 4, 768, 1152))
num_decimation_layers = 6
for i in range(num_decimation_layers):
yconv = MaxPool2DLayer(yconv, pool_size=5, stride=2)
yconv = ReshapeLayer(yconv, (-1, 3, 4, 9, 15))
l_out_for_Y = yconv
print "y decimator part:"
print_network(l_out_for_Y)
# ##############
# X decoder part
# ##############
conv3 = conv
for layer in get_all_layers(conv)[::-1]:
if isinstance(layer, InputLayer):
break
conv3 = InverseLayer(conv3, layer)
l_out_for_decoder = conv3
print "decoder part:"
print_network(l_out_for_decoder)
return l_out_for_X, l_out_for_Y, l_out_for_decoder
def build_model(image_size=(IMAGE_SIZE, IMAGE_SIZE, IMAGE_SIZE)):
l_in = lasagne.layers.InputLayer(shape=(None, ) + image_size)
#preprocessing layers
l_norm = Hu2normHULayer(l_in, min_hu=-1000, max_hu=400)
l_norm = ZMUVLayer(l_norm, mean=0.36, std=0.31)
l0 = lasagne.layers.DimshuffleLayer(l_norm, pattern=[0, 'x', 1, 2, 3])
net = {}
base_n_filters = 128
net['contr_1_1'] = conv3d(l0, base_n_filters)
net['contr_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_1'])
net['contr_1_2'] = conv3d(net['contr_1_1'], base_n_filters)
net['contr_1_2'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_2'])
net['contr_1_3'] = conv3d(net['contr_1_2'], base_n_filters)
net['contr_1_3'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_3'])
net['contr_1_4'] = conv3d(net['contr_1_3'], base_n_filters)
net['contr_1_4'] = lasagne.layers.ParametricRectifierLayer(
net['contr_1_4'])
net['pool1'] = max_pool3d(net['contr_1_4'])
net['contr_2_1'] = conv3d(net['pool1'], base_n_filters)
net['contr_2_1'] = lasagne.layers.ParametricRectifierLayer(
net['contr_2_1'])
net['contr_2_2'] = conv3d(net['contr_2_1'], base_n_filters)
net['contr_2_2'] = lasagne.layers.ParametricRectifierLayer(
net['contr_2_2'])
net['pool2'] = max_pool3d(net['contr_2_2'])
net['contr_3_1'] = conv3d(net['pool2'], base_n_filters)
net['contr_3_1'] = lasagne.layers.ParametricRectifierLayer(
net['contr_3_1'])
net['dropout_1'] = lasagne.layers.DropoutLayer(net['contr_3_1'])
net['upscale1'] = lasagne.layers.Upscale3DLayer(net['dropout_1'], 2)
net['concat1'] = lasagne.layers.ConcatLayer(
[net['upscale1'], net['contr_2_2']],
cropping=(None, None, "center", "center", "center"))
net['expand_1_1'] = conv3d(net['concat1'], base_n_filters)
net['expand_1_1'] = lasagne.layers.ParametricRectifierLayer(
net['expand_1_1'])
net['upscale1'] = lasagne.layers.Upscale3DLayer(net['expand_1_1'], 2)
net['concat2'] = lasagne.layers.ConcatLayer(
[net['upscale1'], net['contr_1_4']],
cropping=(None, None, "center", "center", "center"))
net['expand_2_1'] = conv3d(net['concat2'], base_n_filters)
net['expand_2_1'] = lasagne.layers.ParametricRectifierLayer(
net['expand_2_1'])
net['expand_2_2'] = conv3d(net['expand_2_1'], base_n_filters)
net['expand_2_2'] = lasagne.layers.ParametricRectifierLayer(
net['expand_2_2'])
net['output_segmentation'] = dnn.Conv3DDNNLayer(
net['expand_2_2'],
num_filters=1,
filter_size=1,
nonlinearity=lasagne.nonlinearities.sigmoid)
l_out = lasagne.layers.SliceLayer(net['output_segmentation'],
indices=0,
axis=1)
return {
"inputs": {
"luna:3d": l_in,
},
"outputs": {
"predicted_segmentation": l_out
},
}
import theano
from theano import tensor as T
import lasagne
from lasagne.layers import *
from lasagne.layers import dnn
import sys
sys.path.append("..")
import common
l_conv = InputLayer((None,16,8,96,96))
l_conv = dnn.Conv3DDNNLayer(l_conv, num_filters=128, filter_size=(4,5,5), stride=(1,2,2))
l_conv = dnn.Conv3DDNNLayer(l_conv, num_filters=256, filter_size=(3,5,5), stride=(1,2,2))
l_conv = dnn.Conv3DDNNLayer(l_conv, num_filters=512, filter_size=(3,5,5), stride=(1,2,2))
for layer in get_all_layers(l_conv)[::-1]:
if isinstance(layer, InputLayer):
break
l_conv = InverseLayer(l_conv, layer)
for layer in get_all_layers(l_conv):
print layer, layer.output_shape
print count_params(l_conv)
for x, _ in common.data_iterator(32, "/storeSSD/cbeckham/nersc/big_images/", start_day=1, end_day=1, img_size=96, time_chunks_per_example=8):
# right now it is: (bs, time, nchannels, height, width)
# needs to be: (bs, nchannels, time, height, width)
print x.shape
x = x.reshape((x.shape[0], x.shape[2], x.shape[1], x.shape[3], x.shape[4]))
print x.shape
# break
