def get_model(project_dir, num_GPUs, build_hparams): from MultiPlanarUNet.models.model_init import init_model model_path = get_best_model(project_dir + "/model") weights_name = os.path.splitext(os.path.split(model_path)[1])[0] print("\n[*] Loading model weights:\n", model_path) model = init_model(build_hparams) model.load_weights(model_path, by_name=True) if num_GPUs > 1: print("[*] Creating multi-GPU model... (N={})".format(num_GPUs)) from tensorflow.keras.utils import multi_gpu_model # n_classes = model.n_classes model = multi_gpu_model(model, gpus=num_GPUs) # model.n_classes = n_classes return model, weights_name
def entry_func(args=None): # Project base path args = vars(get_argparser().parse_args(args)) basedir = os.path.abspath(args["project_dir"]) overwrite = args["overwrite"] continue_training = args["continue_training"] eval_prob = args["eval_prob"] await_PID = args["wait_for"] dice_weight = args["dice_weight"] print("Fitting fusion model for project-folder: %s" % basedir) # Minimum images in validation set before also using training images min_val_images = 15 # Fusion model training params epochs = args['epochs'] fm_batch_size = args["batch_size"] # Early stopping params early_stopping = args["early_stopping"] # Wait for PID? if await_PID: from MultiPlanarUNet.utils import await_PIDs await_PIDs(await_PID) # Fetch GPU(s) num_GPUs = args["num_GPUs"] force_gpu = args["force_GPU"] # Wait for free GPU if not force_gpu: await_and_set_free_gpu(N=num_GPUs, sleep_seconds=120) num_GPUs = 1 else: set_gpu(force_gpu) num_GPUs = len(force_gpu.split(",")) # Get logger logger = Logger(base_path=basedir, active_file="train_fusion", overwrite_existing=overwrite) # Get YAML hyperparameters hparams = YAMLHParams(os.path.join(basedir, "train_hparams.yaml")) # Get some key settings n_classes = hparams["build"]["n_classes"] if hparams["build"]["out_activation"] == "linear": # Trained with logit targets? hparams["build"][ "out_activation"] = "softmax" if n_classes > 1 else "sigmoid" # Get views views = np.load("%s/views.npz" % basedir)["arr_0"] del hparams["fit"]["views"] # Get weights and set fusion (output) path weights = get_best_model("%s/model" % basedir) weights_name = os.path.splitext(os.path.split(weights)[-1])[0] fusion_weights = "%s/model/fusion_weights/" \ "%s_fusion_weights.h5" % (basedir, weights_name) create_folders(os.path.split(fusion_weights)[0]) # Log a few things log(logger, hparams, views, weights, fusion_weights) # Check if exists already... if not overwrite and os.path.exists(fusion_weights): from sys import exit print("\n[*] A fusion weights file already exists at '%s'." "\n Use the --overwrite flag to overwrite." % fusion_weights) exit(0) # Load validation data images = ImagePairLoader(**hparams["val_data"], logger=logger) is_validation = {m.id: True for m in images} # Define random sets of images to train on simul. (cant be all due # to memory constraints) image_IDs = [m.id for m in images] if len(images) < min_val_images: # Pick N random training images diff = min_val_images - len(images) logger("Adding %i training images to set" % diff) # Load the training data and pick diff images train = ImagePairLoader(**hparams["train_data"], logger=logger) indx = np.random.choice(np.arange(len(train)), diff, replace=diff > len(train)) # Add the images to the image set set train_add = [train[i] for i in indx] for m in train_add: is_validation[m.id] = False image_IDs.append(m.id) images.add_images(train_add) # Append to length % sub_size == 0 sub_size = args["images_per_round"] rest = int(sub_size * np.ceil(len(image_IDs) / sub_size)) - len(image_IDs) if rest: image_IDs += list(np.random.choice(image_IDs, rest, replace=False)) # Shuffle and split random.shuffle(image_IDs) sets = [ set(s) for s in np.array_split(image_IDs, len(image_IDs) / sub_size) ] assert (contains_all_images(sets, image_IDs)) # Define fusion model (named 'org' to store reference to orgiginal model if # multi gpu model is created below) fusion_model_org = FusionModel(n_inputs=len(views), n_classes=n_classes, weight=dice_weight, logger=logger, verbose=False) if continue_training: fusion_model_org.load_weights(fusion_weights) print("\n[OBS] CONTINUED TRAINING FROM:\n", fusion_weights) # Define model unet = init_model(hparams["build"], logger) print("\n[*] Loading weights: %s\n" % weights) unet.load_weights(weights, by_name=True) if num_GPUs > 1: from tensorflow.keras.utils import multi_gpu_model # Set for predictor model n_classes = n_classes unet = multi_gpu_model(unet, gpus=num_GPUs) unet.n_classes = n_classes # Set for fusion model fusion_model = multi_gpu_model(fusion_model_org, gpus=num_GPUs) else: fusion_model = fusion_model_org # Compile the model logger("Compiling...") metrics = [ "sparse_categorical_accuracy", sparse_fg_precision, sparse_fg_recall ] fusion_model.compile(optimizer=Adam(lr=1e-3), loss=fusion_model_org.loss, metrics=metrics) fusion_model_org._log() try: _run_fusion_training(sets, logger, hparams, min_val_images, is_validation, views, n_classes, unet, fusion_model_org, fusion_model, early_stopping, fm_batch_size, epochs, eval_prob, fusion_weights) except KeyboardInterrupt: pass finally: if not os.path.exists(os.path.split(fusion_weights)[0]): os.mkdir(os.path.split(fusion_weights)[0]) # Save fusion model weights # OBS: Must be original model if multi-gpu is performed! fusion_model_org.save_weights(fusion_weights)
def entry_func(args=None): # Get command line arguments args = vars(get_argparser().parse_args(args)) base_dir = os.path.abspath(args["project_dir"]) analytical = args["analytical"] majority = args["majority"] _file = args["f"] label = args["l"] await_PID = args["wait_for"] eval_prob = args["eval_prob"] _continue = args["continue"] if analytical and majority: raise ValueError("Cannot specify both --analytical and --majority.") # Get settings from YAML file from MultiPlanarUNet.train.hparams import YAMLHParams hparams = YAMLHParams(os.path.join(base_dir, "train_hparams.yaml")) if not _file: try: # Data specified from command line? data_dir = os.path.abspath(args["data_dir"]) # Set with default sub dirs hparams["test_data"] = { "base_dir": data_dir, "img_subdir": "images", "label_subdir": "labels" } except (AttributeError, TypeError): data_dir = hparams["test_data"]["base_dir"] else: data_dir = False out_dir = os.path.abspath(args["out_dir"]) overwrite = args["overwrite"] predict_mode = args["no_eval"] save_input_files = args["save_input_files"] no_argmax = args["no_argmax"] on_val = args["on_val"] # Check if valid dir structures validate_folders(base_dir, out_dir, overwrite, _continue) # Import all needed modules (folder is valid at this point) import numpy as np from MultiPlanarUNet.image import ImagePairLoader, ImagePair from MultiPlanarUNet.models import FusionModel from MultiPlanarUNet.models.model_init import init_model from MultiPlanarUNet.utils import await_and_set_free_gpu, get_best_model, \ create_folders, pred_to_class, set_gpu from MultiPlanarUNet.logging import init_result_dicts, save_all, load_result_dicts from MultiPlanarUNet.evaluate import dice_all from MultiPlanarUNet.utils.fusion import predict_volume, map_real_space_pred from MultiPlanarUNet.interpolation.sample_grid import get_voxel_grid_real_space # Wait for PID? if await_PID: from MultiPlanarUNet.utils import await_PIDs await_PIDs(await_PID) # Set GPU device # Fetch GPU(s) num_GPUs = args["num_GPUs"] force_gpu = args["force_GPU"] # Wait for free GPU if not force_gpu: await_and_set_free_gpu(N=num_GPUs, sleep_seconds=120) num_GPUs = 1 else: set_gpu(force_gpu) num_GPUs = len(force_gpu.split(",")) # Read settings from the project hyperparameter file n_classes = hparams["build"]["n_classes"] # Get views views = np.load("%s/views.npz" % base_dir)["arr_0"] # Force settings hparams["fit"]["max_background"] = 1 hparams["fit"]["test_mode"] = True hparams["fit"]["mix_planes"] = False hparams["fit"]["live_intrp"] = False if "use_bounds" in hparams["fit"]: del hparams["fit"]["use_bounds"] del hparams["fit"]["views"] if hparams["build"]["out_activation"] == "linear": # Trained with logit targets? hparams["build"][ "out_activation"] = "softmax" if n_classes > 1 else "sigmoid" # Set ImagePairLoader object if not _file: data = "test_data" if not on_val else "val_data" image_pair_loader = ImagePairLoader(predict_mode=predict_mode, **hparams[data]) else: predict_mode = not bool(label) image_pair_loader = ImagePairLoader(predict_mode=predict_mode, single_file_mode=True) image_pair_loader.add_image(ImagePair(_file, label)) # Put them into a dict and remove from image_pair_loader to gain more control with # garbage collection all_images = {image.id: image for image in image_pair_loader.images} image_pair_loader.images = None if _continue: all_images = remove_already_predicted(all_images, out_dir) # Evaluate? if not predict_mode: if _continue: csv_dir = os.path.join(out_dir, "csv") results, detailed_res = load_result_dicts(csv_dir=csv_dir, views=views) else: # Prepare dictionary to store results in pd df results, detailed_res = init_result_dicts(views, all_images, n_classes) # Save to check correct format save_all(results, detailed_res, out_dir) # Define result paths nii_res_dir = os.path.join(out_dir, "nii_files") create_folders(nii_res_dir) """ Define UNet model """ model_path = get_best_model(base_dir + "/model") unet = init_model(hparams["build"]) unet.load_weights(model_path, by_name=True) if num_GPUs > 1: from tensorflow.keras.utils import multi_gpu_model n_classes = unet.n_classes unet = multi_gpu_model(unet, gpus=num_GPUs) unet.n_classes = n_classes weights_name = os.path.splitext(os.path.split(model_path)[1])[0] if not analytical and not majority: # Get Fusion model fm = FusionModel(n_inputs=len(views), n_classes=n_classes) weights = base_dir + "/model/fusion_weights/%s_fusion_weights.h5" % weights_name print("\n[*] Loading weights:\n", weights) # Load fusion weights fm.load_weights(weights) print("\nLoaded weights:\n\n%s\n%s\n---" % tuple(fm.layers[-1].get_weights())) # Multi-gpu? if num_GPUs > 1: print("Using multi-GPU model (%i GPUs)" % num_GPUs) fm = multi_gpu_model(fm, gpus=num_GPUs) """ Finally predict on the images """ image_ids = sorted(all_images) N_images = len(image_ids) for n_image, image_id in enumerate(image_ids): print("\n[*] (%i/%s) Running on: %s" % (n_image + 1, N_images, image_id)) # Set image_pair_loader object with only the given file image = all_images[image_id] image_pair_loader.images = [image] # Load views kwargs = hparams["fit"] kwargs.update(hparams["build"]) seq = image_pair_loader.get_sequencer(views=views, **kwargs) # Get voxel grid in real space voxel_grid_real_space = get_voxel_grid_real_space(image) # Prepare tensor to store combined prediction d = image.image.shape[:-1] if not majority: combined = np.empty(shape=(len(views), d[0], d[1], d[2], n_classes), dtype=np.float32) else: combined = np.empty(shape=(d[0], d[1], d[2], n_classes), dtype=np.float32) print("Predicting on brain hyper-volume of shape:", combined.shape) # Predict for each view for n_view, v in enumerate(views): print("\n[*] (%i/%i) View: %s" % (n_view + 1, len(views), v)) # for each view, predict on all voxels and map the predictions # back into the original coordinate system # Sample planes from the image at grid_real_space grid # in real space (scanner RAS) coordinates. X, y, grid, inv_basis = seq.get_view_from(image.id, v, n_planes="same+20") # Predict on volume using model pred = predict_volume(unet, X, axis=2, batch_size=seq.batch_size) # Map the real space coordiante predictions to nearest # real space coordinates defined on voxel grid mapped_pred = map_real_space_pred(pred, grid, inv_basis, voxel_grid_real_space, method="nearest") if not majority: combined[n_view] = mapped_pred else: combined += mapped_pred if n_classes == 1: # Set to background if outside pred domain combined[n_view][np.isnan(combined[n_view])] = 0. if not predict_mode and np.random.rand() <= eval_prob: view_dices = dice_all(y, pred_to_class(pred, img_dims=3, has_batch_dim=False), ignore_zero=False, n_classes=n_classes, skip_if_no_y=False) mapped_dices = dice_all(image.labels, pred_to_class(mapped_pred, img_dims=3, has_batch_dim=False), ignore_zero=False, n_classes=n_classes, skip_if_no_y=False) mean_dice = mapped_dices[~np.isnan(mapped_dices)][1:].mean() # Print dice scores print("View dice scores: ", view_dices) print("Mapped dice scores: ", mapped_dices) print("Mean dice (n=%i): " % (len(mapped_dices) - 1), mean_dice) # Add to results results.loc[image_id, str(v)] = mean_dice detailed_res[str(v)][image_id] = mapped_dices[1:] # Overwrite with so-far results save_all(results, detailed_res, out_dir) else: print("Skipping evaluation for this view... " "(eval_prob=%.3f, predict_mode=%s)" % (eval_prob, predict_mode)) if not analytical and not majority: # Combine predictions across views using Fusion model print("\nFusing views...") combined = np.moveaxis(combined, 0, -2).reshape( (-1, len(views), n_classes)) combined = fm.predict(combined, batch_size=10**4, verbose=1).reshape( (d[0], d[1], d[2], n_classes)) elif analytical: print("\nFusing views (analytical)...") combined = np.sum(combined, axis=0) if not no_argmax: print("\nComputing majority vote...") combined = pred_to_class(combined.squeeze(), img_dims=3).astype(np.uint8) if not predict_mode: if no_argmax: # MAP only for dice calculation c_temp = pred_to_class(combined, img_dims=3).astype(np.uint8) else: c_temp = combined # Calculate combined prediction dice dices = dice_all(image.labels, c_temp, n_classes=n_classes, ignore_zero=True, skip_if_no_y=False) mean_dice = dices[~np.isnan(dices)].mean() detailed_res["MJ"][image_id] = dices print("Combined dices: ", dices) print("Combined mean dice: ", mean_dice) results.loc[image_id, "MJ"] = mean_dice # Overwrite with so-far results save_all(results, detailed_res, out_dir) # Save combined prediction volume as .nii file print("Saving .nii files...") save_nii_files(combined, image, nii_res_dir, save_input_files) # Remove image from dictionary and image_pair_loader to free memory del all_images[image_id] image_pair_loader.images.remove(image) if not predict_mode: # Write final results save_all(results, detailed_res, out_dir)