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feat(AutoML): Implemented function block prediction
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#==============================================================================# | ||
# Author: Dominik Müller # | ||
# Copyright: 2022 IT-Infrastructure for Translational Medical Research, # | ||
# University of Augsburg # | ||
# # | ||
# This program is free software: you can redistribute it and/or modify # | ||
# it under the terms of the GNU General Public License as published by # | ||
# the Free Software Foundation, either version 3 of the License, or # | ||
# (at your option) any later version. # | ||
# # | ||
# This program is distributed in the hope that it will be useful, # | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of # | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # | ||
# GNU General Public License for more details. # | ||
# # | ||
# You should have received a copy of the GNU General Public License # | ||
# along with this program. If not, see <http://www.gnu.org/licenses/>. # | ||
#==============================================================================# | ||
#-----------------------------------------------------# | ||
# Library imports # | ||
#-----------------------------------------------------# | ||
# External libraries | ||
import os | ||
import json | ||
import pandas as pd | ||
# Internal libraries | ||
from aucmedi import * | ||
from aucmedi.data_processing.io_loader import image_loader, sitk_loader | ||
from aucmedi.data_processing.subfunctions import * | ||
from aucmedi.ensemble import * | ||
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#-----------------------------------------------------# | ||
# Building Blocks for Inference # | ||
#-----------------------------------------------------# | ||
def block_predict(config): | ||
""" | ||
Attributes: | ||
path_imagedir | ||
input | ||
output | ||
batch_size | ||
workers | ||
""" | ||
# Peak into the dataset via the input interface | ||
ds = input_interface("directory", | ||
config["path_imagedir"], | ||
path_data=None, | ||
training=False, | ||
ohe=False, | ||
image_format=None) | ||
(index_list, _, _, _, image_format) = ds | ||
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# Create output directory | ||
if not os.path.exists(config["output"]) : os.mkdir(config["output"]) | ||
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# Verify existence of input directory | ||
if not os.path.exists(config["input"]): | ||
raise FileNotFoundError(config["input"]) | ||
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# Load metadata from training | ||
path_meta = os.path.join(config["input"], "meta.training.json") | ||
with open(path_meta, "r") as json_file: | ||
meta_training = json.load(json_file) | ||
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# Define neural network parameters | ||
nn_paras = {"n_labels": 1, # placeholder | ||
"channels": 1, # placeholder | ||
"workers": config["workers"], | ||
"batch_queue_size": 4, | ||
"multiprocessing": False, | ||
} | ||
# Select input shape for 3D | ||
if not meta_training["two_dim"]: | ||
nn_paras["input_shape"] = tuple(meta_training["shape_3D"]) | ||
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# Subfunctions | ||
sf_list = [] | ||
if not meta_training["two_dim"]: | ||
sf_norm = Standardize(mode="grayscale") | ||
sf_pad = Padding(mode="constant", shape=meta_training["shape_3D"]) | ||
sf_crop = Crop(shape=meta_training["shape_3D"], mode="random") | ||
sf_chromer = Chromer(target="rgb") | ||
sf_list.extend([sf_norm, sf_pad, sf_crop, sf_chromer]) | ||
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# Define parameters for DataGenerator | ||
paras_datagen = { | ||
"path_imagedir": config["path_imagedir"], | ||
"batch_size": config["batch_size"], | ||
"img_aug": None, | ||
"subfunctions": sf_list, | ||
"prepare_images": False, | ||
"sample_weights": None, | ||
"seed": None, | ||
"image_format": image_format, | ||
"workers": config["workers"], | ||
"shuffle": False, | ||
"grayscale": False, | ||
} | ||
if meta_training["two_dim"] : paras_datagen["loader"] = image_loader | ||
else : paras_datagen["loader"] = sitk_loader | ||
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# Apply MIC pipelines | ||
if meta_training["analysis"] == "minimal": | ||
# Setup neural network | ||
if meta_training["two_dim"]: | ||
arch_dim = "2D." + meta_training["architecture"] | ||
else : arch_dim = "3D." + meta_training["architecture"] | ||
model = NeuralNetwork(architecture=arch_dim, **nn_paras) | ||
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# Build DataGenerator | ||
pred_gen = DataGenerator(samples=index_list, | ||
labels=None, | ||
resize=model.meta_input, | ||
standardize_mode=model.meta_standardize, | ||
**paras_datagen) | ||
# Load model | ||
path_model = os.path.join(config["input"], "model.last.hdf5") | ||
model.load(path_model) | ||
# Start model inference | ||
preds = model.predict(prediction_generator=pred_gen) | ||
elif meta_training["analysis"] == "standard": | ||
# Setup neural network | ||
if meta_training["two_dim"]: | ||
arch_dim = "2D." + meta_training["architecture"] | ||
else : arch_dim = "3D." + meta_training["architecture"] | ||
model = NeuralNetwork(architecture=arch_dim, **nn_paras) | ||
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# Build DataGenerator | ||
pred_gen = DataGenerator(samples=index_list, | ||
labels=None, | ||
resize=model.meta_input, | ||
standardize_mode=model.meta_standardize, | ||
**paras_datagen) | ||
# Load model | ||
path_model = os.path.join(config["input"], "model.best_loss.hdf5") | ||
model.load(path_model) | ||
# Start model inference via Augmenting | ||
preds = predict_augmenting(model, pred_gen) | ||
else: | ||
# Build multi-model list | ||
model_list = [] | ||
for arch in meta_training["architecture"]: | ||
if meta_training["two_dim"] : arch_dim = "2D." + arch | ||
else : arch_dim = "3D." + arch | ||
model_part = NeuralNetwork(architecture=arch_dim, **nn_paras) | ||
model_list.append(model_part) | ||
el = Composite(model_list, metalearner=meta_training["metalearner"], | ||
k_fold=len(meta_training["architecture"])) | ||
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# Build DataGenerator | ||
pred_gen = DataGenerator(samples=index_list, | ||
labels=None, | ||
resize=None, | ||
standardize_mode=None, | ||
**paras_datagen) | ||
# Load composite model directory | ||
el.load(config["input"]) | ||
# Start model inference via ensemble learning | ||
preds = el.predict(pred_gen) | ||
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# Create prediction dataset | ||
df_index = pd.DataFrame(data={"SAMPLE": index_list}) | ||
df_pd = pd.DataFrame(data=preds, columns=meta_training["class_names"]) | ||
df_merged = pd.concat([df_index, df_pd], axis=1, sort=False) | ||
df_merged.sort_values(by=["SAMPLE"], inplace=True) | ||
# Store predictions to disk | ||
df_merged.to_csv(config["output"], index=False) |