def main(model_name, model=models.wnet_connected, num_epochs=5, batch_size=2):
'''Trains depth estimation model.'''
segmentation_models.set_framework('tf.keras')
print(segmentation_models.framework())
#Build list of training filenames
X_folderpath = r"G:\Documents\KITTI\data\train\X\\"
y_folderpath = r"G:\Documents\KITTI\data\train\y\\"
X_filelist = glob(X_folderpath + '*.png')
y_filelist = glob(y_folderpath + '*.png')
#Build list of validation filenames
X_val_folderpath = r"G:\Documents\KITTI\data\val\X\\"
y_val_folderpath = r"G:\Documents\KITTI\data\val\y\\"
X_val_filelist = glob(X_val_folderpath + '*.png')
y_val_filelist = glob(y_val_folderpath + '*.png')
model = model()
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=1e-4)) #,metrics=['mse']
#Save best model weights checkpoint
filepath = f"{model_name}_weights_best.hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min')
#Tensorboard setup
log_dir = f"logs\\{model_name}\\" + datetime.datetime.now().strftime(
"%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(log_dir=log_dir)
callbacks_list = [checkpoint, tensorboard_callback]
model.fit_generator(
_batchGenerator(X_filelist, y_filelist, batch_size),
epochs=num_epochs,
steps_per_epoch=len(X_filelist) // batch_size,
#validation_data=(X_test,y_test),
validation_data=_valBatchGenerator(X_val_filelist, y_val_filelist,
batch_size),
validation_steps=len(X_val_filelist) // batch_size,
max_queue_size=1,
callbacks=callbacks_list,
verbose=2)
return model
import geopandas as gpd
import numpy as np
import rasterio as rio
from rasterio.windows import Window
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
import segmentation_models as sm
from segmentation_models.metrics import iou_score
from segmentation_models.losses import jaccard_loss, categorical_focal_jaccard_loss
from sklearn.preprocessing import MinMaxScaler
from models import regression_head, unet_model, unet_reg
import argparse
sm.set_framework('tf.keras')
sm.framework()
# indices https://www.sciencedirect.com/science/article/pii/S0303243422000290
## look into switching loss function to categorical_cross_entropy and maybe built in keras metric IOU from keras (done)
### change training data file to csv files (done mostly)
## change to 256 chip size, see if the csv files make a difference (done)
## if not get generators working (not needed yet)
### generate thresholded impoervious data maybe across all datasets (all years) (done)
#### create indices (ASI, NVDI, .....) (in progress)
### look into dice joccard loss as improvement
### when reading impervious mask REMEMBER data[data == 127] = 0 (done)
## Do I calculate indices before scaling by 16 bit scaler???
import pytest
import numpy as np
import segmentation_models as sm
from segmentation_models.metrics import IOUScore, FScore
from segmentation_models.losses import JaccardLoss, DiceLoss
if sm.framework() == sm._TF_KERAS_FRAMEWORK_NAME:
from tensorflow import keras
elif sm.framework() == sm._KERAS_FRAMEWORK_NAME:
import keras
else:
raise ValueError('Incorrect framework {}'.format(sm.framework()))
METRICS = [
IOUScore,
FScore,
]
LOSSES = [
JaccardLoss,
DiceLoss,
]
GT0 = np.array(
[
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
],
dtype='float32',
def _build_model(self, lr):
self.model = self.model(backbone_name=self.backbone_name,
input_shape=self.input_shape,
classes=self.classes,
encoder_weights=self.encoder_weights,
activation=self.activation)
optimizer = Adam(lr=lr)
self.model.compile(optimizer, categorical_focal_dice_loss,
[iou_score, precision, recall])
def train(self,
lr,
train_gen,
val_gen,
epochs,
workers=4,
use_multiprocessing=False):
self._build_model(lr)
self.model.fit_generator(train_gen,
validation_data=val_gen,
epochs=epochs,
workers=workers,
use_multiprocessing=use_multiprocessing)
if __name__ == "__main__":
print(sm.framework())