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train.py
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"""
@Author Sujith Umapathy
Script to train a semantic segmentation model
Train on manually generated floor plans and their corresponding labels
"""
import os
import tensorflow as tf
from utils.read_images import read_images
from configuration import Config as conf
from utils.visualise import visualize
import numpy as np
from utils.patch_images import patch_images
import keras.backend as k
import math
k.set_image_data_format('channels_last')
# need to choose keras backend for the library to work
os.environ['SM_FRAMEWORK'] = 'tf.keras'
import segmentation_models as sm
def clean_list(list_data):
mod_list = []
for im, ma in list_data:
if ma.any():
mod_list.append((im, ma))
return mod_list
if __name__ == '__main__':
# Read train images
train_images = read_images(conf.train_folder_images)
train_mask = read_images(conf.train_folder_mask)
# Read validate images
val_images = read_images(conf.validation_folder_images)
val_mask = read_images(conf.validation_folder_mask)
print(f'Length of Train images {len(train_images)}')
print(f'Length of Validation images {len(val_images)}')
visualize(train_images, train_mask)
visualize(val_images, val_mask)
train_images = np.array(train_images)
train_mask = np.array(train_mask)
val_images = np.array(val_images)
val_mask = np.array(val_mask)
# patch_images
t_train_images = patch_images(train_images)
t_train_masks = patch_images(train_mask)
t_val_images = patch_images(val_images)
t_val_masks = patch_images(val_mask)
t_train_images = np.array(t_train_images)
t_train_masks = np.array(t_train_masks)
t_val_images = np.array(t_val_images)
t_val_masks = np.array(t_val_masks)
# remove useless frames
t_list = clean_list(zip(t_train_images, t_train_masks))
v_list = clean_list(zip(t_val_images, t_val_masks))
t_train_images, t_train_masks = zip(*t_list)
t_val_images, t_val_masks = zip(*v_list)
t_train_images = np.array(t_train_images)
t_train_masks = np.array(t_train_masks)
t_val_images = np.array(t_val_images)
t_val_masks = np.array(t_val_masks)
# stack layers for aligning to segmentation model - Keras requirement of 4 dimensions
t_train_images = np.stack((t_train_images,) * 3, axis=-1)
t_val_images = np.stack((t_val_images,) * 3, axis=-1)
t_train_masks = np.expand_dims(t_train_masks, axis=-1)
t_val_masks = np.expand_dims(t_val_masks, axis=-1)
n_classes = np.unique(t_train_masks)
backbone = conf.backbone
pre_process_ip = sm.get_preprocessing(backbone)
t_train_images = pre_process_ip(t_train_images)
t_val_images = pre_process_ip(t_val_images)
# Train model
model = sm.Unet(backbone, encoder_weights='imagenet')
model.compile(optimizer='Adam',
loss=sm.losses.binary_focal_jaccard_loss,
metrics=sm.metrics.IOUScore(threshold=0.5))
model.summary()
callbacks = [
tf.keras.callbacks.ModelCheckpoint(
f'{conf.model_path}model_resnet_18.h5',
save_weights_only=True,
save_best_only=True,
mode='auto',
monitor='loss'),
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=3, min_lr=0.00001)
]
history = model.fit(
t_train_images, t_train_masks,
steps_per_epoch=200,
epochs=20,
callbacks=callbacks,
validation_data=(t_val_images, t_val_masks),
validation_steps=math.ceil(t_val_images.shape[0] / 10),
batch_size=10
)