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predict_resistivity_log10.py
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predict_resistivity_log10.py
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"""Use trained neural network to predict log10 scale resistivity."""
import importlib
import os
import re
from functools import partial
import tensorflow as tf
from tqdm import tqdm
from erinn.tf_dataset import tf_read_dataset
from erinn.utils.io_utils import get_pkl_list
from erinn.utils.io_utils import read_config_file
from erinn.utils.io_utils import read_pkl
from erinn.utils.io_utils import write_pkl
# TODO: Organize reusable code snippets into functions
FILEDIR = os.path.dirname(__file__)
# read config
config_file = os.path.join(FILEDIR, '..', '..', 'config', 'for_predict_resistivity.yml')
config = read_config_file(config_file)
# parse config and setting
custom_NN = config['custom_NN']
dataset_rootdir = os.path.join(FILEDIR, config['dataset_rootdir'])
testing_dir = os.path.join(dataset_rootdir, 'testing')
testing_resistance_dir = os.path.join(testing_dir, 'resistance', config['resistance_dirname'])
testing_resistivity_dir = os.path.join(testing_dir, 'resistivity', config['resistivity_dirname'])
raw_resistance_dir = os.path.join(testing_dir, 'resistance', "raw")
model_dir = config['model_dir']
simulator_pkl = os.path.join(FILEDIR, model_dir, 'simulator.pkl')
simulator = read_pkl(simulator_pkl)
weights_dir = os.path.join(FILEDIR, model_dir, 'weights')
trained_weights = os.path.join(FILEDIR, weights_dir, 'trained_weight.h5')
save_predictions_dir = os.path.join(FILEDIR, model_dir, 'predictions')
preprocess = config['preprocess']
gpus = config['num_gpu']
# Allowing GPU memory growth and set visible GPU
# References:
# https://www.tensorflow.org/guide/gpu
# https://qiita.com/studio_haneya/items/4dfaf2fb2ac44818e7e0
if tf.__version__.startswith('1.'):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
tf.keras.backend.set_session(session)
else:
physical_gpus = tf.config.experimental.list_physical_devices('GPU')
if physical_gpus:
try:
# Set visible gpus in TensorFlow
tf.config.experimental.set_visible_devices(physical_gpus[0:gpus], 'GPU')
visible_gpus = tf.config.experimental.get_visible_devices('GPU')
# Currently, memory growth needs to be the same across GPUs
for visible_gpu in visible_gpus:
tf.config.experimental.set_memory_growth(visible_gpu, True)
print('The memory growth of', visible_gpu, ':', tf.config.experimental.get_memory_growth(visible_gpu))
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(physical_gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Visible devices/Memory growth/Virtual devices must be set before GPUs have been initialized
print(e)
else:
print("Not enough GPU hardware devices available")
# Multi-GPU
# NOTE: compile keras model within MirroredStrategy.scope()
# References:
# https://www.tensorflow.org/guide/distributed_training
# https://www.tensorflow.org/tutorials/distribute/keras
# https://github.com/tensorflow/tensorflow/blob/919dfc3d066e72ee02baa11fbf7b035d9944daa9/tensorflow/python/distribute/mirrored_strategy.py#L339
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
# load custom keras model
# References:
# https://stackoverflow.com/questions/19009932/import-arbitrary-python-source-file-python-3-3
pattern = re.compile(r'\'([^\']+)\'')
module_name, py_file = re.findall(pattern, custom_NN)
py_file = os.path.join(FILEDIR, py_file)
loader = importlib.machinery.SourceFileLoader(module_name, py_file)
spec = importlib.util.spec_from_loader(module_name, loader)
module = importlib.util.module_from_spec(spec)
loader.exec_module(module)
model = getattr(module, module_name)()
# load weights
model.load_weights(trained_weights)
# Use tf.data.Dataset
# References:
# https://www.tensorflow.org/guide/data
# https://stackoverflow.com/questions/60496435/how-to-convert-tensor-to-string
# https://github.com/tensorflow/tensorflow/issues/24520#issuecomment-532958834
testing_resistance_pkl_list = get_pkl_list(testing_resistance_dir)
testing_resistivity_pkl_list = get_pkl_list(testing_resistivity_dir)
raw_resistance_pkl_list = get_pkl_list(raw_resistance_dir)
list_dataset_testing_inputs = tf.data.Dataset.from_tensor_slices(testing_resistance_pkl_list)
list_dataset_testing_targets = tf.data.Dataset.from_tensor_slices(testing_resistivity_pkl_list)
list_dataset_testing = tf.data.Dataset.zip((list_dataset_testing_inputs, list_dataset_testing_targets))
# use custom keras model to define shape of inputs and outputs
input_shape = model.input_shape[1:]
output_shape = model.output_shape[1:]
# read transmittor/receiver locations
Tx_locations = simulator.urf.abmn_locations[:, :4]
Rx_locations = simulator.urf.abmn_locations[:, 4:]
# read number of cell centor mesh in the x/z direction
nCx = simulator.mesh.nCx
nCy = simulator.mesh.nCy
read_dataset_info = {
'preprocess': preprocess,
'Tx_locations': Tx_locations,
'Rx_locations': Rx_locations,
'nCx': nCx, 'nCy': nCy,
'input_shape': input_shape,
'output_shape': output_shape
}
# use partial to assign read_dataset_info
par = partial(tf_read_dataset, read_dataset_info=read_dataset_info)
# read data => take mini-batch => prefetch
dataset_test = list_dataset_testing.map(par).batch(1).prefetch(8)
# Prediction
print('\nPredict.')
predict = model.predict(dataset_test, verbose=True)
# Save
os.makedirs(save_predictions_dir, exist_ok=True)
for i, dataset_testing_targets in tqdm(enumerate(list_dataset_testing_targets.as_numpy_iterator()), desc="write pkl"):
raw_resistance = read_pkl(raw_resistance_pkl_list[i])
resistivity_log10 = read_pkl(dataset_testing_targets)
data ={
"synthetic_resistance": raw_resistance,
"synthetic_resistivity_log10": resistivity_log10.reshape(output_shape[0:2]),
"predicted_resistivity_log10": predict[i].reshape(output_shape[0:2])
}
filename = re.findall(r'\d+.pkl', testing_resistance_pkl_list[i])[0]
write_pkl(data, os.path.join(save_predictions_dir, filename))
# save simulator
simulator.config['testing'] = config
write_pkl(simulator, simulator_pkl)