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train.py
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train.py
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import os
import argparse
import sys
import logging
import math
import numpy as np
import time
import random
# Importing Keras
import tensorflow as tf
import keras
from keras.optimizers import Adam
from keras import backend as K
from keras.callbacks import ModelCheckpoint, TensorBoard
from keras.models import load_model
from src import metric, model, io, util, dataGenerator, loss
from src.bf_grid import bf_grid
import config
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
_config = tf.config.experimental.set_memory_growth(physical_devices[0], True)
timing = {}
util.check_dir(config.path_logs)
util.set_logger(os.path.join(config.path_logs, 'train.log'))
parser = argparse.ArgumentParser(
description='See description below to see all available options')
parser.add_argument('-pt', '--pretrained',
help='Continuining training from the given model. \
[Default] is no model given',
default=None,
type=str,
required=False)
parser.add_argument('-w', '--weight',
help='If model provided is Model Weight or not. \
True - It is Weight, False- Complete Model',
default=None,
type=bool,
required=False)
# Parsing arguments
args = parser.parse_args()
# Checking directories
util.check_dir(config.path_model)
util.check_dir(config.path_weight)
util.check_dir(config.path_tiled)
util.check_dir(config.path_tiled_image)
util.check_dir(config.path_tiled_label)
# Checking if image or images path exist in data folder
if not os.path.exists(config.path_image):
msg = '{} does not exist. Ensure that directory exist'.format(
config.path_image)
logging.error(msg)
raise(msg)
# Checking if label or labes path exist in data folder
if not os.path.exists(config.path_label):
msg = '{} does not exist. Ensure that directory exist'.format(
config.path_label)
logging.error(msg)
raise(msg)
# Writing all parameters into configuration file
configuration = {}
# Training Data Set
training_dataList = dataGenerator.getData(
path_tile_image=config.path_tiled_image,
path_tile_label=config.path_tiled_label)
training_list_ids, training_imageMap, training_labelMap = training_dataList.getList()
# Validation Data Set
validation_dataList = dataGenerator.getData(
path_tile_image=config.path_vali_tiled_image,
path_tile_label=config.path_vali_tiled_label)
validation_list_ids, validation_imageMap, validation_labelMap = validation_dataList.getList()
# Training DataGenerator
training_generator = dataGenerator.DataGenerator(
list_IDs=training_list_ids, imageMap=training_imageMap,
labelMap=training_labelMap,
batch_size=config.batch, n_classes=None,
image_channels=config.num_image_channels,
label_channels=config.num_label_channels,
image_size=config.image_size, shuffle=True)
# Validation DataGenerator
validation_generator = dataGenerator.DataGenerator(
list_IDs=validation_list_ids, imageMap=validation_imageMap,
labelMap=validation_labelMap,
batch_size=config.batch, n_classes=None,
image_channels=config.num_image_channels,
label_channels=config.num_label_channels,
image_size=config.image_size, shuffle=False)
""" Data Generator Ends"""
st_time = time.time()
# Logging input data
logging.info('path_tiled_image: {}'.format(config.path_tiled_image))
logging.info('path_tiled_label: {}'.format(config.path_tiled_label))
logging.info('image_size: {}'.format(config.image_size))
logging.info('num_image_channels: {}'.format(config.num_image_channels))
logging.info('num_epoch: {}'.format(config.epoch))
unet_model = model.unet(config.image_size)
# loading model from model file or weights file
logging.info('Loading trained model')
if args.weight is True:
unet_model = model.unet(config.image_size)
try:
unet_model.load_weights(args.pretrained)
except Exception as e:
msg = 'Unable to load model weights: {}'.format(args.pretrained)
logging.error(msg)
raise('{}. Error : {}'.format(msg, e))
elif args.weight is False:
try:
unet_model = load_model(args.pretrained, custom_objects={
'dice_coef': metric.dice_coef, 'jaccard_coef': metric.jaccard_coef})
except Exception as e:
msg = 'Unable to load model: {}'.format(args.pretrained)
logging.error(msg)
raise('{}. Error : {}'.format(msg, e))
# Compiling model
unet_model.compile(optimizer=Adam(lr=1e-4),
loss=loss.weighted_binary_crossentropy, # 'binary_crossentropy', #
metrics=[metric.dice_coef, metric.jaccard_coef])
# create a UNet (512,512)
# look at the summary of the unet
unet_model.summary()
# Logging accuracies
csv_logger = keras.callbacks.CSVLogger(
os.path.join(config.path_logs, 'keras_training.log'))
# Creating model callbacks
path_save_callback = os.path.join(
config.path_weight, 'weights.{epoch:02d}-{val_loss:.2f}.hdf5')
saving_model = keras.callbacks.ModelCheckpoint(path_save_callback,
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=True,
mode='auto',
period=5)
# fit the unet with the actual image, train_image
# and the output, train_label
history = unet_model.fit_generator(generator=training_generator,
epochs=config.epoch,
workers=3,
validation_data=validation_generator,
callbacks=[csv_logger, saving_model])
# Saving path of weigths saved
logging.info('Saving model')
unet_model.save(os.path.join(config.path_weight, 'final.hdf5'))
# Getting timings
end_time = time.time() - st_time
timing['Total Time'] = str(end_time)
# Saving to JSON
io.tojson(timing, os.path.join(config.path_model, 'Timing.json'))
logging.info('Completed')
sys.exit()