def compare_seg_with_gt(max_n_images=10, epoch=0):
data_gen_validation = SegmentationBatchGeneratorDavid(
all_patients,
BATCH_SIZE,
validation_patients,
PATCH_SIZE=INPUT_PATCH_SIZE,
mode="test",
ignore=[81, 10, 47],
losses=None,
num_batches=None,
seed=10,
)
data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2])
data_gen_validation = center_crop_seg_generator(data_gen_validation, OUTPUT_PATCH_SIZE)
data, seg, idx = data_gen_validation.next()
seg = np.array(seg)
seg_pred = get_segmentation(data)
plt.figure(figsize=(6, 20))
n_images = np.min((seg_pred.shape[0], max_n_images))
for i in range(n_images):
seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5])
seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5])
plt.subplot(n_images, 2, 2 * i + 1)
plt.imshow(seg[i, 0])
plt.subplot(n_images, 2, 2 * i + 2)
plt.imshow(seg_pred[i])
plt.savefig(os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))
def compare_seg_with_gt(max_n_images=5, epoch=0):
data_gen_validation = SegmentationBatchGeneratorBraTS2014(
all_patients,
BATCH_SIZE,
validation_patients,
PATCH_SIZE=PATCH_SIZE,
mode="test",
ignore=[81],
losses=None,
num_batches=None,
seed=10)
data_gen_validation = seg_channel_selection_generator(
data_gen_validation, [2])
data_gen_validation = center_crop_seg_generator(data_gen_validation,
(180, 164))
data, seg, idx = data_gen_validation.next()
seg = np.array(seg)
seg_pred = get_segmentation(data)
plt.figure(figsize=(6, 12))
n_images = np.min((seg_pred.shape[0], max_n_images))
for i in range(n_images):
seg_pred[i][0, :6] = np.array([0, 1, 2, 3, 4, 5])
seg[i, 0, 0, :6] = np.array([0, 1, 2, 3, 4, 5])
plt.subplot(n_images, 2, 2 * i + 1)
plt.imshow(seg[i, 0])
plt.subplot(n_images, 2, 2 * i + 2)
plt.imshow(seg_pred[i])
plt.savefig(
os.path.join(results_dir, "some_segmentations_ep_%d.png" % epoch))
validation_patients = [157, 69, 7, 97, 235, 12, 231, 200, 177, 104, 247, 41, 237, 24, 118, 198, 103, 6, 243, 35, 0, 18, 112, 180, 25] '''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()
'''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 = memmapGenerator_allInOne_segmentation_lossSampling(memmap_data, memmap_gt, BATCH_SIZE, validation_patients, mode="test", ignore=[40])
data_gen_validation = center_crop_generator(data_gen_validation, (PATCH_SIZE, PATCH_SIZE))
data_gen_validation = seg_channel_selection_generator(data_gen_validation, [2])
data_gen_validation = multi_threaded_generator(data_gen_validation, num_threads=4, num_cached=10)
net = build_UNet(20, BATCH_SIZE, num_output_classes=5, base_n_filters=16, input_dim=(PATCH_SIZE, PATCH_SIZE))
output_layer_for_loss = net["output_flattened"]
'''with open(os.path.join(results_dir, "%s_Params_ep30.pkl"%EXPERIMENT_NAME, 'r') as f:
params = cPickle.load(f)
lasagne.layers.set_all_param_values(output_layer_for_loss, params)
with open(os.path.join(results_dir, "%s_allLossesNAccur_ep30.pkl"%EXPERIMENT_NAME, 'r') as f:
# [all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies, auc_all] = cPickle.load(f)
[all_training_losses, all_training_accuracies, all_validation_losses, all_validation_accuracies] = cPickle.load(f)'''
n_batches_per_epoch = 500
# n_batches_per_epoch = np.floor(n_training_samples/float(BATCH_SIZE))
n_test_batches = 50
# n_test_batches = np.floor(n_val_samples/float(BATCH_SIZE))
