testing_patients = [27,136,62,144,83,78,135,13,126] #validation_patients = [125, 68, 85, 88, 7, 112, 130, 8, 32, 122, 70, 100, 128, 91, 41] num_classes=5 '''all_patients = load_patients_David_not_adapted() all_patients_no_test = {} for k in all_patients.keys(): if k not in testing_patients: all_patients_no_test[k] = all_patients[k]''' train_ids = [i for i in range(200) if (i not in validation_patients and i not in testing_patients)] train_ids = [i for i in train_ids if i not in ignore] validation_patients = [i for i in validation_patients if i not in ignore] testing_patients = [i for i in testing_patients if i not in ignore] patients_train = load_patients_David_for_3D_UNet(train_ids) patients_validation = load_patients_David_for_3D_UNet(validation_patients) patients_test = load_patients_David_for_3D_UNet(testing_patients) # del all_patients # all_patients_no_test = all_patients '''tmp = SegmentationBatchGeneratorDavid(all_patients_no_test, 50, validation_patients, PATCH_SIZE=OUTPUT_PATCH_SIZE, mode="train", ignore=[40], losses=None, num_batches=None, seed=None) tmp = Multithreaded_Generator(tmp, 2, 20) ctr = 0 class_frequencies = np.zeros(5) for data, seg, id in data_gen_train: print ctr class_frequencies[0] += np.sum(seg[:, 0] == 0) class_frequencies[1] += np.sum(seg[:, 0] == 1) class_frequencies[2] += np.sum(seg[:, 0] == 2)
test_patients = np.unique([27,136,62,144,83,78,135,13,126]) experiment_name = "segmentPatches_David_UNet3D_noBN_adapted_expSchedule" results_folder = "/home/fabian/datasets/Hirntumor_von_David/experiments/results/%s/" % experiment_name epoch = 27 results_out_folder = "/home/fabian/datasets/Hirntumor_von_David/experiments/results/%s/ep%03.0d" % (experiment_name, epoch) if not os.path.isdir(results_out_folder): os.mkdir(results_out_folder) all_official_metrics = np.zeros((len(test_patients), 13)) ctr=0 patients_test = load_patients_David_for_3D_UNet(test_patients) for patient_id in test_patients: print patient_id output_folder = os.path.join(results_out_folder, "%03.0d" % patient_id) if not os.path.isdir(output_folder): os.mkdir(output_folder) patient_data = patients_test[patient_id] patient_data["t1"] = extract_brain_region(patient_data["t1"], patient_data["seg"], 0) patient_data["t1km"] = extract_brain_region(patient_data["t1km"], patient_data["seg"], 0) patient_data["flair"] = extract_brain_region(patient_data["flair"], patient_data["seg"], 0) patient_data["adc"] = extract_brain_region(patient_data["adc"], patient_data["seg"], 0) patient_data["cbv"] = extract_brain_region(patient_data["cbv"], patient_data["seg"], 0) patient_data["seg"] = extract_brain_region(patient_data["seg"], patient_data["seg"], 0) curr_shape = patient_data["t1"].shape