def set_gpu_vis(args): force_gpu = args.force_GPU if not force_gpu: # Wait for free GPU from mpunet.utils import await_and_set_free_gpu await_and_set_free_gpu(N=args.num_GPUs, sleep_seconds=120) num_GPUs = args.num_GPUs else: from mpunet.utils import set_gpu set_gpu(force_gpu) num_GPUs = len(force_gpu.split(",")) return num_GPUs
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 mpunet.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) else: set_gpu(force_gpu) # 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.identifier: True for m in images} # Define random sets of images to train on simul. (cant be all due # to memory constraints) image_IDs = [m.identifier 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.identifier] = False image_IDs.append(m.identifier) 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 = FusionModel(n_inputs=len(views), n_classes=n_classes, weight=dice_weight, logger=logger, verbose=False) if continue_training: fusion_model.load_weights(fusion_weights) print("\n[OBS] CONTINUED TRAINING FROM:\n", fusion_weights) import tensorflow as tf with tf.distribute.MirroredStrategy().scope(): # Define model unet = init_model(hparams["build"], logger) print("\n[*] Loading weights: %s\n" % weights) unet.load_weights(weights, by_name=True) # 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.loss, metrics=metrics) fusion_model._log() try: _run_fusion_training(sets, logger, hparams, min_val_images, is_validation, views, n_classes, unet, 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.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"]) _file = args["f"] label = args["l"] N_extra = args["extra"] try: N_extra = int(N_extra) except ValueError: pass # Get settings from YAML file from mpunet.hyperparameters import YAMLHParams hparams = YAMLHParams(os.path.join(base_dir, "train_hparams.yaml")) # Set strides hparams["fit"]["strides"] = args["strides"] 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_only_pred = args["save_only_pred"] # Check if valid dir structures validate_folders(base_dir, data_dir, out_dir, overwrite) # Import all needed modules (folder is valid at this point) import numpy as np from mpunet.image import ImagePairLoader, ImagePair from mpunet.utils import get_best_model, create_folders, \ pred_to_class, await_and_set_free_gpu, set_gpu from mpunet.utils.fusion import predict_3D_patches, predict_3D_patches_binary, pred_3D_iso from mpunet.logging import init_result_dict_3D, save_all_3D from mpunet.evaluate import dice_all from mpunet.bin.predict import save_nii_files # Fetch GPU(s) num_GPUs = args["num_GPUs"] force_gpu = args["force_GPU"] # Wait for free GPU if force_gpu == -1: await_and_set_free_gpu(N=num_GPUs, sleep_seconds=240) else: set_gpu(force_gpu) # Read settings from the project hyperparameter file dim = hparams["build"]["dim"] n_classes = hparams["build"]["n_classes"] mode = hparams["fit"]["intrp_style"] # Set ImagePairLoader object if not _file: image_pair_loader = ImagePairLoader(predict_mode=predict_mode, **hparams["test_data"]) else: predict_mode = not bool(label) image_pair_loader = ImagePairLoader(predict_mode=predict_mode, initialize_empty=True) image_pair_loader.add_image(ImagePair(_file, label)) all_images = { image.identifier: image for image in image_pair_loader.images } # Set scaler and bg values image_pair_loader.set_scaler_and_bg_values( bg_value=hparams.get_from_anywhere('bg_value'), scaler=hparams.get_from_anywhere('scaler'), compute_now=False) # Init LazyQueue and get its sequencer from mpunet.sequences.utils import get_sequence seq = get_sequence(data_queue=image_pair_loader, is_validation=True, **hparams["fit"], **hparams["build"]) """ Define UNet model """ from mpunet.models import model_initializer hparams["build"]["batch_size"] = 1 unet = model_initializer(hparams, False, base_dir) model_path = get_best_model(base_dir + "/model") unet.load_weights(model_path) # Evaluate? if not predict_mode: # Prepare dictionary to store results in pd df results, detailed_res = init_result_dict_3D(all_images, n_classes) # Save to check correct format save_all_3D(results, detailed_res, out_dir) # Define result paths nii_res_dir = os.path.join(out_dir, "nii_files") create_folders(nii_res_dir) image_ids = sorted(all_images) for n_image, image_id in enumerate(image_ids): print("\n[*] Running on: %s" % image_id) with seq.image_pair_queue.get_image_by_id(image_id) as image_pair: if mode.lower() == "iso_live_3d": pred = pred_3D_iso(model=unet, sequence=seq, image=image_pair, extra_boxes=N_extra, min_coverage=None) else: # Predict on volume using model if n_classes > 1: pred = predict_3D_patches(model=unet, patches=seq, image=image_pair, N_extra=N_extra) else: pred = predict_3D_patches_binary(model=unet, patches=seq, image_id=image_id, N_extra=N_extra) if not predict_mode: # Get patches for the current image y = image_pair.labels # Calculate dice score print("Mean dice: ", end="", flush=True) p = pred_to_class(pred, img_dims=3, has_batch_dim=False) dices = dice_all(y, p, n_classes=n_classes, ignore_zero=True) mean_dice = dices[~np.isnan(dices)].mean() print("Dices: ", dices) print("%s (n=%i)" % (mean_dice, len(dices))) # Add to results results[image_id] = [mean_dice] detailed_res[image_id] = dices # Overwrite with so-far results save_all_3D(results, detailed_res, out_dir) # Save results save_nii_files(p, image_pair, nii_res_dir, save_only_pred) if not predict_mode: # Write final results save_all_3D(results, detailed_res, out_dir)
# Run one final time on full-size image img, loss_value = _run_optim(img, iterate_func, intermediate_dim, lr, steps) print("") return img.squeeze(), loss_value if __name__ == "__main__": from mpunet.bin.predict import get_model, load_hparams from mpunet.utils import await_and_set_free_gpu p = "/home/jovyan/work/projects/knee_projects/" \ "iwoai_challenge/splits/split_0" # Get model await_and_set_free_gpu(1) hparams = load_hparams(p) model = get_model(p, 1, hparams["build"])[0] # SETTINGS layer_range = range(0, len(model.layers)) filter_range = range(0, 200) n_highest = 9 steps = 30 lr = 0.5 upscaling_steps = 19 per_layer_filters = [] layer_names = [] for layer_ind in layer_range: layer = model.layers[layer_ind]