'''d, s, l = data_gen_train.next() plt.figure(figsize=(12, 5)) plt.subplot(1, 3, 1) plt.imshow(d[0,0], cmap="gray") plt.subplot(1, 3, 2) d1=elastic_transform_2d(d[0,0], 550., 20.) plt.imshow(d1, cmap="gray") plt.subplot(1, 3, 3) plt.imshow(d[0,0]-d1) plt.show() plt.close()''' data_gen_validation = SegmentationBatchGeneratorBraTS2014(all_patients, BATCH_SIZE, validation_patients, PATCH_SIZE=PATCH_SIZE, mode="test", ignore=[81], losses=None, num_batches=None, seed=None) data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2]) data_gen_validation = center_crop_seg_generator(data_gen_validation, (180, 164)) data_gen_validation = Multithreaded_Generator(data_gen_validation, 2, 30) data_gen_validation._start() net = build_UNet(20, BATCH_SIZE, num_output_classes=6, base_n_filters=16, input_dim=PATCH_SIZE, pad="valid") output_layer_for_loss = net["output_flattened"] n_batches_per_epoch = 300 # n_batches_per_epoch = np.floor(n_training_samples/float(BATCH_SIZE)) n_test_batches = 30 # n_test_batches = np.floor(n_val_samples/float(BATCH_SIZE)) x_sym = T.tensor4() seg_sym = T.ivector() w_sym = T.vector() # add some weight decay
memmap_name = "patchSegmentation_allInOne_ws_t1km_flair_adc_cbv_resized"
BATCH_SIZE = 10
PATCH_SIZE = 15
with open(dataset_folder + "%s_properties.pkl" % (memmap_name), 'r') as f:
my_dict = cPickle.load(f)
data_ctr = my_dict['n_data']
train_shape = my_dict['train_neg_shape']
info_memmap_shape = my_dict['info_shape']
memmap_data = memmap(dataset_folder + "%s.memmap" % (memmap_name), dtype=np.float32, mode="r", shape=train_shape)
memmap_gt = memmap(dataset_folder + "%s_info.memmap" % (memmap_name), dtype=np.float32, mode="r", shape=info_memmap_shape)
data_gen = memmapGenerator_allInOne_segmentation_lossSampling(memmap_data, memmap_gt, 1, [0, 1], num_batches=10)
for data, seg, ids in data_gen:
print ids[0]
for _ in range(5):
data_gen = memmapGenerator_allInOne_segmentation_lossSampling(memmap_data, memmap_gt, 1, [0, 1])
data_gen_mt = Multithreaded_Generator(data_gen, 8, 30)
ctr = 0
for data, seg, ids in data_gen_mt:
print ids[0]
ctr += 1
if ctr > 10:
break
data_gen_mt._finish()
loss_new = (loss_old + losses/avg_loss) / 2.
return loss_new'''
def update_losses(losses, idx, loss):
losses[idx] = (losses[idx] + loss*2.) / 3.
return losses
n_epochs = 30
auc_scores=None
for epoch in range(14,n_epochs):
data_gen_train = memmapGenerator_allInOne_segmentation_lossSampling(memmap_data, memmap_gt, BATCH_SIZE, validation_patients, mode="train", ignore=[40], losses=losses)
data_gen_train = seg_channel_selection_generator(data_gen_train, [2])
data_gen_train = rotation_generator(data_gen_train)
data_gen_train = center_crop_generator(data_gen_train, (PATCH_SIZE, PATCH_SIZE))
data_gen_train = elastric_transform_generator(data_gen_train, 550., 20.)
data_gen_train = Multithreaded_Generator(data_gen_train, 12, 100)
data_gen_train._start()
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, idx in data_gen_train:
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
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, auc_scores=auc_scores, auc_labels=["bg", "brain", "edema", "ce_tumor", "necrosis"], ylim_score=(0,0.75))
def convert_seg_flat_to_binary_label_indicator_array(seg_flat, num_classes=5):
seg2 = np.zeros((len(seg_flat), num_classes))
for i in xrange(seg2.shape[0]):
seg2[i, int(seg_flat[i])] = 1
return seg2
n_epochs = 80
auc_scores=None
for epoch in range(0,n_epochs):
data_gen_train = memmapGenerator_allInOne_segmentation_lossSampling(memmap_data, memmap_gt, BATCH_SIZE, validation_patients, mode="train", ignore=[40], losses=losses)
data_gen_train = seg_channel_selection_generator(data_gen_train, [2])
data_gen_train = rotation_generator(data_gen_train)
data_gen_train = center_crop_generator(data_gen_train, (PATCH_SIZE, PATCH_SIZE))
data_gen_train = elastric_transform_generator(data_gen_train, 550., 20.)
data_gen_train = Multithreaded_Generator(data_gen_train, 4, 20)
data_gen_train._start()
print "epoch: ", epoch
train_loss = 0
train_acc_tmp = 0
train_loss_tmp = 0
batch_ctr = 0
for data, seg, idx in data_gen_train:
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
printLosses(all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, "../../../results/%s.png" % EXPERIMENT_NAME, n_feedbacks_per_epoch, auc_scores=auc_scores, auc_labels=["bg", "brain", "edema", "ce_tumor", "necrosis"], ylim_score=(0,1.5))
