def train_model():
batch_size = 16
num_epochs = c.ch4_train_epochs
sz = c.fcn_img_size
version=2
for i in xrange(5):
data = u.DataH5PyStreamer(os.path.join(c.data_intermediate, 'ch4_256.hdf5'),
batch_size=batch_size, folds=(5,i))
input_var = T.tensor4('input')
label_var = T.tensor4('label')
net, output, output_det = m.build_fcn_segmenter(input_var,
(None, 1, sz, sz), version=version)
params = nn.layers.get_all_params(net['output'], trainable=True)
lr = theano.shared(nn.utils.floatX(3e-3))
loss = du.sorenson_dice(output, label_var)
te_loss = du.sorenson_dice(output_det, label_var)
te_acc = nn.objectives.binary_accuracy(output_det, label_var).mean()
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, label_var], loss, updates=updates)
test_fn = theano.function([input_var, label_var], te_loss)
acc_fn = theano.function([input_var, label_var], te_acc)
pred_fn = theano.function([input_var], output_det)
hist = u.train_with_hdf5(data, num_epochs=num_epochs,
train_fn = train_fn, test_fn=test_fn,
max_per_epoch=-1, use_tqdm=False,
tr_transform=lambda x: du.segmenter_data_transform(x, rotate=(-180, 180)),
te_transform=lambda x: du.segmenter_data_transform(x, rotate=None),
last_layer = net['output'],
save_params_to=os.path.join(c.params_dir, 'ch4seg_v{}/test_ch4seg_f{}_v{}.npz'\
.format(version, i, version)))
def train_model():
batch_size = 16
num_epochs = c.ch4_train_epochs
sz = c.fcn_img_size
version = 2
for i in xrange(5):
data = u.DataH5PyStreamer(os.path.join(c.data_intermediate,
'ch4_256.hdf5'),
batch_size=batch_size,
folds=(5, i))
input_var = T.tensor4('input')
label_var = T.tensor4('label')
net, output, output_det = m.build_fcn_segmenter(input_var,
(None, 1, sz, sz),
version=version)
params = nn.layers.get_all_params(net['output'], trainable=True)
lr = theano.shared(nn.utils.floatX(3e-3))
loss = du.sorenson_dice(output, label_var)
te_loss = du.sorenson_dice(output_det, label_var)
te_acc = nn.objectives.binary_accuracy(output_det, label_var).mean()
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, label_var],
loss,
updates=updates)
test_fn = theano.function([input_var, label_var], te_loss)
acc_fn = theano.function([input_var, label_var], te_acc)
pred_fn = theano.function([input_var], output_det)
hist = u.train_with_hdf5(data, num_epochs=num_epochs,
train_fn = train_fn, test_fn=test_fn,
max_per_epoch=-1, use_tqdm=False,
tr_transform=lambda x: du.segmenter_data_transform(x, rotate=(-180, 180)),
te_transform=lambda x: du.segmenter_data_transform(x, rotate=None),
last_layer = net['output'],
save_params_to=os.path.join(c.params_dir, 'ch4seg_v{}/test_ch4seg_f{}_v{}.npz'\
.format(version, i, version)))
def train_model():
batch_size = 8
version = 2
total_epochs = c.fcn_train_epochs
for normpct in [(10, 90), None]:
stop_times = []
for i in [0, 1, 2, 3, 4, -1]:
num_epochs = int(np.mean(stop_times)) if i == -1 else total_epochs
data = u.DataH5PyStreamer(os.path.join(c.data_intermediate,
'scd_seg_256.hdf5'),
batch_size=batch_size,
folds=(5, i))
input_var = T.tensor4('input')
label_var = T.tensor4('label')
net, output, output_det = m.build_fcn_segmenter(
input_var, (None, 1, c.fcn_img_size, c.fcn_img_size), version)
params = nn.layers.get_all_params(net['output'], trainable=True)
lr = theano.shared(nn.utils.floatX(3e-3))
loss = du.sorenson_dice(output, label_var)
te_loss = du.sorenson_dice(output_det, label_var)
te_acc = nn.objectives.binary_accuracy(output_det,
label_var).mean()
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, label_var],
loss,
updates=updates)
test_fn = theano.function([input_var, label_var], te_loss)
pred_fn = theano.function([input_var], output_det)
normstr = (str(normpct[0]) + str(normpct[1])) if normpct else 'MS'
pfn = os.path.join(
c.params_dir, 'fcn_v{}_p{}/fcn_v{}_p{}_f{}_{}.npz'.format(
version, normstr, version, normstr, i,
np.random.randint(100000)))
hist = u.train_with_hdf5(
data,
num_epochs=num_epochs,
train_fn=train_fn,
test_fn=test_fn,
max_per_epoch=-1,
tr_transform=lambda x: du.segmenter_data_transform(
x, rotate=(-10, 50), normalize_pctwise=normpct),
te_transform=lambda x: du.segmenter_data_transform(
x, rotate=None, normalize_pctwise=normpct),
use_tqdm=False,
last_layer=net['output'],
save_last_params=(i == -1),
save_params_to=pfn)
if i != -1:
stop_times.append(np.argmin(np.array(hist)[:, 1]) + 1)
print 'stop time {}'.format(stop_times[-1])
sca = c.scale
ntimes = 6
#NCV+1
np.random.seed(1234)
input_var = T.tensor4('input')
label_var = T.tensor4('label')
net, output, output_det = build_fcn_segmenter(
input_var, (None, 1, c.fcn_img_size, c.fcn_img_size), version)
for l in nn.layers.get_all_layers(net['output']):
print nn.layers.get_output_shape(l)
params = nn.layers.get_all_params(net['output'], trainable=True)
init0 = nn.layers.get_all_param_values(net['output'])
lr = theano.shared(nn.utils.floatX(3e-3))
loss = du.sorenson_dice(output, label_var, ss=ss)
te_loss = du.sorenson_dice(output_det, label_var, ss=ss)
te_acc = nn.objectives.binary_accuracy(output_det, label_var).mean()
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, label_var], loss, updates=updates)
test_fn = theano.function([input_var, label_var], te_loss)
acc_fn = theano.function([input_var, label_var], te_acc)
pred_fn = theano.function([input_var], output_det)
batch_size = 16
max_epoch = (0 if CV else num_epochs)
for i in xrange(ntimes):
if not CV and i != 5:
continue
if i == 5:
num_epochs = max_epoch + 1
shi = c.shift;
rot = c.rotation;
sca = c.scale;
ntimes = 6; #NCV+1
np.random.seed(1234);
input_var = T.tensor4('input')
label_var = T.tensor4('label')
net, output, output_det =build_fcn_segmenter(input_var, (None, 1, c.fcn_img_size, c.fcn_img_size), version)
for l in nn.layers.get_all_layers(net['output']):
print nn.layers.get_output_shape(l)
params = nn.layers.get_all_params(net['output'], trainable=True)
init0 = nn.layers.get_all_param_values(net['output']);
lr = theano.shared(nn.utils.floatX(3e-3))
loss = du.sorenson_dice(output, label_var,ss=ss)
te_loss = du.sorenson_dice(output_det, label_var,ss=ss)
te_acc = nn.objectives.binary_accuracy(output_det, label_var).mean()
updates = nn.updates.adam(loss, params, learning_rate=lr)
train_fn = theano.function([input_var, label_var], loss, updates=updates)
test_fn = theano.function([input_var, label_var], te_loss)
acc_fn = theano.function([input_var, label_var], te_acc)
pred_fn = theano.function([input_var], output_det)
batch_size=16
max_epoch = (0 if CV else num_epochs);
for i in xrange(ntimes):
if not CV and i!=5:
continue
if i == 5: