def run(fold=0):
print fold
# =================================================================================================================
I_AM_FOLD = fold
np.random.seed(65432)
lasagne.random.set_rng(np.random.RandomState(98765))
sys.setrecursionlimit(2000)
BATCH_SIZE = 2
INPUT_PATCH_SIZE =(128, 128, 128)
num_classes=4
EXPERIMENT_NAME = "final"
results_dir = os.path.join(paths.results_folder)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
results_dir = os.path.join(results_dir, EXPERIMENT_NAME)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
results_dir = os.path.join(results_dir, "fold%d"%I_AM_FOLD)
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
n_epochs = 300
lr_decay = np.float32(0.985)
base_lr = np.float32(0.0005)
n_batches_per_epoch = 100
n_test_batches = 10
n_feedbacks_per_epoch = 10.
num_workers = 6
workers_seeds = [123, 1234, 12345, 123456, 1234567, 12345678]
# =================================================================================================================
all_data = load_dataset()
keys_sorted = np.sort(all_data.keys())
crossval_folds = KFold(len(all_data.keys()), n_folds=5, shuffle=True, random_state=123456)
ctr = 0
for train_idx, test_idx in crossval_folds:
print len(train_idx), len(test_idx)
if ctr == I_AM_FOLD:
train_keys = [keys_sorted[i] for i in train_idx]
test_keys = [keys_sorted[i] for i in test_idx]
break
ctr += 1
train_data = {i:all_data[i] for i in train_keys}
test_data = {i:all_data[i] for i in test_keys}
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.75, 1.5), gamma_range = (0.8, 1.5),
num_workers=num_workers, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 1300.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(0., 2*np.pi), a_z=(0., 2*np.pi),
do_scale=True, scale_range=(0.75, 1.25), seeds=workers_seeds)
data_gen_validation = BatchGenerator3D_random_sampling(test_data, BATCH_SIZE, num_batches=None, seed=False,
patch_size=INPUT_PATCH_SIZE, convert_labels=True)
val_transforms = []
val_transforms.append(GenerateBrainMaskTransform())
val_transforms.append(BrainMaskAwareStretchZeroOneTransform(clip_range=(-5, 5), per_channel=True))
val_transforms.append(SegChannelSelectionTransform([0]))
val_transforms.append(ConvertSegToOnehotTransform(range(4), 0, "seg_onehot"))
val_transforms.append(DataChannelSelectionTransform([0, 1, 2, 3]))
data_gen_validation = MultiThreadedAugmenter(data_gen_validation, Compose(val_transforms), 2, 2)
x_sym = T.tensor5()
seg_sym = T.matrix()
net, seg_layer = build_net(x_sym, INPUT_PATCH_SIZE, num_classes, 4, 16, batch_size=BATCH_SIZE,
do_instance_norm=True)
output_layer_for_loss = net
# add some weight decay
l2_loss = lasagne.regularization.regularize_network_params(output_layer_for_loss, lasagne.regularization.l2) * 1e-5
# the distinction between prediction_train and test is important only if we enable dropout (batch norm/inst norm
# does not use or save moving averages)
prediction_train = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=False,
batch_norm_update_averages=False, batch_norm_use_averages=False)
loss_vec = - soft_dice_per_img_in_batch(prediction_train, seg_sym, BATCH_SIZE)[:, 1:]
loss = loss_vec.mean()
loss += l2_loss
acc_train = T.mean(T.eq(T.argmax(prediction_train, axis=1), seg_sym.argmax(-1)), dtype=theano.config.floatX)
prediction_test = lasagne.layers.get_output(output_layer_for_loss, x_sym, deterministic=True,
batch_norm_update_averages=False, batch_norm_use_averages=False)
loss_val = - soft_dice_per_img_in_batch(prediction_test, seg_sym, BATCH_SIZE)[:, 1:]
loss_val = loss_val.mean()
loss_val += l2_loss
acc = T.mean(T.eq(T.argmax(prediction_test, axis=1), seg_sym.argmax(-1)), dtype=theano.config.floatX)
# learning rate has to be a shared variable because we decrease it with every epoch
params = lasagne.layers.get_all_params(output_layer_for_loss, trainable=True)
learning_rate = theano.shared(base_lr)
updates = lasagne.updates.adam(T.grad(loss, params), params, learning_rate=learning_rate, beta1=0.9, beta2=0.999)
dc = hard_dice_per_img_in_batch(prediction_test, seg_sym.argmax(1), num_classes, BATCH_SIZE).mean(0)
train_fn = theano.function([x_sym, seg_sym], [loss, acc_train, loss_vec], updates=updates)
val_fn = theano.function([x_sym, seg_sym], [loss_val, acc, dc])
all_val_dice_scores=None
all_training_losses = []
all_validation_losses = []
all_validation_accuracies = []
all_training_accuracies = []
val_dice_scores = []
epoch = 0
while epoch < n_epochs:
if epoch == 100:
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.85, 1.25), gamma_range = (0.8, 1.5),
num_workers=6, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 1000.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(-np.pi/8., np.pi/8.),
a_z=(-np.pi/8., np.pi/8.), do_scale=True, scale_range=(0.85, 1.15),
seeds=workers_seeds)
if epoch == 175:
data_gen_train = create_data_gen_train(train_data, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE,
contrast_range=(0.9, 1.1), gamma_range = (0.85, 1.3),
num_workers=6, num_cached_per_worker=2,
do_elastic_transform=True, alpha=(0., 750.), sigma=(10., 13.),
do_rotation=True, a_x=(0., 2*np.pi), a_y=(-0.00001, 0.00001),
a_z=(-0.00001, 0.00001), do_scale=True, scale_range=(0.85, 1.15),
seeds=workers_seeds)
epoch_start_time = time.time()
learning_rate.set_value(np.float32(base_lr* lr_decay**(epoch)))
print "epoch: ", epoch, " learning rate: ", learning_rate.get_value()
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data_dict in data_gen_train:
data = data_dict["data"].astype(np.float32)
seg = data_dict["seg_onehot"].astype(np.float32).transpose(0, 2, 3, 4, 1).reshape((-1, num_classes))
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/n_feedbacks_per_epoch)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", \
train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch), " train accuracy: ", \
train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
all_training_accuracies.append(train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
train_loss_tmp = 0
train_acc_tmp = 0
if len(val_dice_scores) > 0:
all_val_dice_scores = np.concatenate(val_dice_scores, axis=0).reshape((-1, num_classes))
try:
printLosses(all_training_losses, all_training_accuracies, all_validation_losses,
all_validation_accuracies, os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME),
n_feedbacks_per_epoch, val_dice_scores=all_val_dice_scores,
val_dice_scores_labels=["brain", "1", "2", "3", "4", "5"])
except:
pass
loss_vec, acc, l = train_fn(data, seg)
loss = loss_vec.mean()
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr >= n_batches_per_epoch:
break
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
all_training_accuracies.append(train_acc_tmp/np.floor(n_batches_per_epoch/n_feedbacks_per_epoch))
train_loss /= n_batches_per_epoch
print "training loss average on epoch: ", train_loss
val_loss = 0
accuracies = []
valid_batch_ctr = 0
all_dice = []
for data_dict in data_gen_validation:
data = data_dict["data"].astype(np.float32)
seg = data_dict["seg_onehot"].astype(np.float32).transpose(0, 2, 3, 4, 1).reshape((-1, num_classes))
w = np.zeros(num_classes, dtype=np.float32)
w[np.unique(seg.argmax(-1))] = 1
loss, acc, dice = val_fn(data, seg)
dice[w==0] = 2
all_dice.append(dice)
val_loss += loss
accuracies.append(acc)
valid_batch_ctr += 1
if valid_batch_ctr >= n_test_batches:
break
all_dice = np.vstack(all_dice)
dice_means = np.zeros(num_classes)
for i in range(num_classes):
dice_means[i] = all_dice[all_dice[:, i]!=2, i].mean()
val_loss /= n_test_batches
print "val loss: ", val_loss
print "val acc: ", np.mean(accuracies), "\n"
print "val dice: ", dice_means
print "This epoch took %f sec" % (time.time()-epoch_start_time)
val_dice_scores.append(dice_means)
all_validation_losses.append(val_loss)
all_validation_accuracies.append(np.mean(accuracies))
all_val_dice_scores = np.concatenate(val_dice_scores, axis=0).reshape((-1, num_classes))
try:
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies,
os.path.join(results_dir, "%s.png" % EXPERIMENT_NAME), n_feedbacks_per_epoch,
val_dice_scores=all_val_dice_scores, val_dice_scores_labels=["brain", "1", "2", "3", "4", "5"])
except:
pass
with open(os.path.join(results_dir, "%s_Params.pkl" % (EXPERIMENT_NAME)), 'w') as f:
cPickle.dump(lasagne.layers.get_all_param_values(output_layer_for_loss), f)
with open(os.path.join(results_dir, "%s_allLossesNAccur.pkl"% (EXPERIMENT_NAME)), 'w') as f:
cPickle.dump([all_training_losses, all_training_accuracies, all_validation_losses,
all_validation_accuracies, val_dice_scores], f)
epoch += 1
n_epochs = 10
for epoch in range(n_epochs):
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, labels in multi_threaded_generator(memmapGenerator_tumorClassRot(train_pos_memmap, train_neg_memmap, BATCH_SIZE, n_pos_train, n_pos_train), num_threads=2, num_cached=50):
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/10.)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", train_loss_tmp/np.floor(n_batches_per_epoch/10.), " train accuracy: ", train_acc_tmp/np.floor(n_batches_per_epoch/10.)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/10.))
all_training_accs.append(train_acc_tmp/np.floor(n_batches_per_epoch/10.))
train_loss_tmp = 0
train_acc_tmp = 0
printLosses(all_training_losses, all_training_accs, all_validation_losses, all_validation_accuracies, "../results/%s.png" % EXPERIMENT_NAME, 10)
loss, acc = train_fn(data[:, :12, :, :], labels)
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr > n_batches_per_epoch:
break
train_loss /= n_batches_per_epoch
print "training loss average on epoch: ", train_loss
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in multi_threaded_generator(memmapGenerator_tumorClassRot(val_pos_memmap, val_neg_memmap, BATCH_SIZE, n_pos_val, n_pos_val), num_threads=2, num_cached=50):
n_epochs = 20
for epoch in range(n_epochs):
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, labels in threaded_generator(memmapGeneratorDataAugm(train_neg_memmap, train_pos_memmap, BATCH_SIZE, n_pos_train, n_neg_train)):
if batch_ctr != 0 and batch_ctr % int(np.floor(n_batches_per_epoch/10.)) == 0:
print "number of batches: ", batch_ctr, "/", n_batches_per_epoch
print "training_loss since last update: ", train_loss_tmp/np.floor(n_batches_per_epoch/10.), " train accuracy: ", train_acc_tmp/np.floor(n_batches_per_epoch/10.)
all_training_losses.append(train_loss_tmp/np.floor(n_batches_per_epoch/10.))
all_training_accs.append(train_acc_tmp/np.floor(n_batches_per_epoch/10.))
train_loss_tmp = 0
train_acc_tmp = 0
printLosses(all_training_losses, all_training_accs, all_validation_losses, all_validation_accuracies, "../results/%s.png" % EXPERIMENT_NAME, 10)
loss, acc = train_fn(data, labels)
train_loss += loss
train_loss_tmp += loss
train_acc_tmp += acc
batch_ctr += 1
if batch_ctr > n_batches_per_epoch:
break
train_loss /= n_batches_per_epoch
print "training loss average on epoch: ", train_loss
test_loss = 0
accuracies = []
valid_batch_ctr = 0
for data, seg, labels in threaded_generator(memmapGenerator(val_neg_memmap, val_pos_memmap, BATCH_SIZE, n_pos_val, n_neg_val)):