def cremi_unet(name='unet', sample_to_isotropy=False):
in_shape = (84, 268, 268)
n_channels = 12
# These values reproduce jans network
initial_fmaps = 12
fmap_increase = 5
downsample_factors = [[1, 3, 3], [1, 3, 3], [3, 3, 3]] if sample_to_isotropy else \
[[1, 3, 3], [1, 3, 3], [1, 3, 3]]
raw = tf.placeholder(tf.float32, shape=in_shape)
raw_batched = tf.reshape(raw, (
1,
1,
) + in_shape)
unet = networks.unet(raw_batched, initial_fmaps, fmap_increase,
downsample_factors)
affs_batched = networks.conv_pass(unet,
kernel_size=1,
num_fmaps=n_channels,
num_repetitions=1,
activation='sigmoid')
output_shape_batched = affs_batched.get_shape().as_list()
output_shape = output_shape_batched[1:] # strip the batch dimension
affs = tf.reshape(affs_batched, output_shape)
gt_affs = tf.placeholder(tf.float32, shape=output_shape)
loss_weights = tf.placeholder(tf.float32, shape=output_shape)
loss = tf.losses.mean_squared_error(gt_affs, affs, loss_weights)
tf.summary.scalar('loss_total', loss)
opt = tf.train.AdamOptimizer(learning_rate=0.5e-4,
beta1=0.95,
beta2=0.999,
epsilon=1e-8)
optimizer = opt.minimize(loss)
#for trainable in tf.trainable_variables():
# networks.tf_var_summary(trainable)
merged = tf.summary.merge_all()
tf.train.export_meta_graph(filename='%s.meta' % name)
names = {
'raw': raw.name,
'affs': affs.name,
'gt_affs': gt_affs.name,
'loss_weights': loss_weights.name,
'loss': loss.name,
'optimizer': optimizer.name,
'summary': merged.name
}
with open('net_io_names.json', 'w') as f:
json.dump(names, f)
raw_0 = tf.placeholder(tf.float32, shape=shape_0)
raw_0_batched = tf.reshape(raw_0, (1, 1) + shape_0)
input_0 = tf.concat([raw_0_batched, affs_0_batched], 1)
if ignore:
keep_raw = tf.ones_like(raw_0_batched)
ignore_aff = tf.zeros_like(affs_0_batched)
ignore_mask = tf.concat([keep_raw, ignore_aff], 1)
input_0 = networks.ignore(input_0, ignore_mask)
unet = networks.unet(input_0, 24, 3, [[2, 2, 2], [2, 2, 2], [2, 2, 2]])
affs_1_batched = networks.conv_pass(unet,
kernel_size=1,
num_fmaps=3,
num_repetitions=1,
activation='sigmoid')
affs_1 = tf.reshape(affs_1_batched, (3, ) + shape_1)
gt_affs_1 = tf.placeholder(tf.float32, shape=(3, ) + shape_1)
loss_weights_1 = tf.placeholder(tf.float32, shape=(3, ) + shape_1)
loss_1 = tf.losses.mean_squared_error(gt_affs_1, affs_1,
loss_weights_1)
# phase 2
tf.summary.scalar('loss_pred0', loss_1)
scope.reuse_variables()
tf.stop_gradient(affs_1_batched)
raw_1 = center_crop(raw_0, shape_1)
(1, 3, 3)], [(3, 3, 3),
(3, 3, 3)], [(3, 3, 3), (3, 3, 3)]],
[[(1, 3, 3),
(1, 3, 3)], [(1, 3, 3),
(1, 3, 3)], [(3, 3, 3),
(3, 3, 3)], [(3, 3, 3), (3, 3, 3)]],
anisotropy=[10, 1, 1],
fov=[10, 1, 1])
# raw = tf.placeholder(tf.float32, shape=(132,)*3)
# raw_batched = tf.reshape(raw, (1, 1,) + (132,)*3)
#
# unet = networks.unet(raw_batched, 24, 3, [[2, 2, 2], [2, 2, 2], [2, 2, 2]])
dist_batched, fov = networks.conv_pass(unet,
kernel_size=[[1, 1, 1]],
num_fmaps=1,
activation=None,
fov=fov,
anisotropy=anisotropy)
output_shape_batched = dist_batched.get_shape().as_list()
output_shape = output_shape_batched[1:] # strip the batch dimension
dist = tf.reshape(dist_batched, output_shape)
gt_dist = tf.placeholder(tf.float32, shape=output_shape)
loss_weights = tf.placeholder(tf.float32, shape=output_shape[1:])
loss_weights_batched = tf.reshape(loss_weights, shape=output_shape)
loss_eucl = tf.losses.mean_squared_error(gt_dist, dist,
raw = tf.placeholder(tf.float32, shape=(196, ) * 3)
raw_batched = tf.reshape(raw, (
1,
1,
) + (196, ) * 3)
unet = networks.unet(raw_batched, 12, 6, [[2, 2, 2], [2, 2, 2], [3, 3, 3]])
# raw = tf.placeholder(tf.float32, shape=(132,)*3)
# raw_batched = tf.reshape(raw, (1, 1,) + (132,)*3)
#
# unet = networks.unet(raw_batched, 24, 3, [[2, 2, 2], [2, 2, 2], [2, 2, 2]])
dist_batched = networks.conv_pass(unet,
kernel_size=1,
num_fmaps=1,
num_repetitions=1,
activation='tanh')
output_shape_batched = dist_batched.get_shape().as_list()
output_shape = output_shape_batched[1:] # strip the batch dimension
dist = tf.reshape(dist_batched, output_shape)
gt_dist = tf.placeholder(tf.float32, shape=output_shape)
#loss_weights = tf.placeholder(tf.float32, shape=output_shape)
loss_eucl = tf.losses.mean_squared_error(gt_dist, dist)
tf.summary.scalar('loss_total', loss_eucl)
(1, 3, 3)], [(3, 3, 3),
(3, 3, 3)], [(3, 3, 3), (3, 3, 3)]],
[[(1, 3, 3),
(1, 3, 3)], [(1, 3, 3),
(1, 3, 3)], [(3, 3, 3),
(3, 3, 3)], [(3, 3, 3), (3, 3, 3)]],
voxel_size=[10, 1, 1],
fov=[10, 1, 1])
# raw = tf.placeholder(tf.float32, shape=(132,)*3)
# raw_batched = tf.reshape(raw, (1, 1,) + (132,)*3)
#
# unet = networks.unet(raw_batched, 24, 3, [[2, 2, 2], [2, 2, 2], [2, 2, 2]])
dist_batched, fov1 = networks.conv_pass(unet,
kernel_size=[[1, 1, 1]],
num_fmaps=1,
activation=None,
fov=fov,
voxel_size=anisotropy)
aff_batched, fov = networks.conv_pass(unet,
kernel_size=[[1, 1, 1]],
num_fmaps=3,
activation=None,
name='aff_conv',
fov=fov,
voxel_size=anisotropy)
print("distbatched", dist_batched.get_shape().as_list())
dist_output_shape_batched = dist_batched.get_shape().as_list()
dist_output_shape = dist_output_shape_batched[1:]
syn_dist = tf.reshape(dist_batched, dist_output_shape)
gt_syn_dist = tf.placeholder(tf.float32, shape=dist_output_shape)
loss_weights = tf.placeholder(tf.float32, shape=dist_output_shape[1:])