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matching.py
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matching.py
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# matching.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app
from absl import flags
from absl import logging
from datasets.matching_dataset import MatchingDataset
from evaluators.matching_evaluator import MatchingEvaluator
from loggers.logger import Logger
from models.dizygotic_net import DizygoticNet
from optimizers.lookahead import Lookahead
from optimizers.radam import RectifiedAdam
from trainers.matching_trainer import MatchingTrainer
from utils.config import process_config
import tensorflow as tf
# Network entries
flags.DEFINE_float("learning_rate", 2e-4, "Initial learning rate for the chosen optimizer")
flags.DEFINE_integer("batch_size", 32, "The size of the batch to use while training the network.", lower_bound=1)
flags.DEFINE_integer("filters", 4, "A parameter that scales the depth of the neural network.", lower_bound=1)
flags.DEFINE_integer("num_epochs", 100, "Number of epochs to train the network for.", lower_bound=1)
# Data entries
flags.DEFINE_list("input_shape", [128, 256, 1], "The shape of the data to input in the neural network.")
flags.DEFINE_list("satellite_shape", [128, 256, 1], "The shape of the satellite image to input in the network.")
flags.DEFINE_list("output_shape", [128, 256, 1], "The shape of the data that will be output from the neural network.")
def main(argv) -> None:
del argv
# Process the configuration from flags.
config = process_config()
if config.mode != "evaluate":
# Define the datasets.
train_dataset = MatchingDataset(batch_size=config.batch_size,
folder="datasets/matching_aracati/train",
x_shape=config.input_shape,
y_shape=config.output_shape,
is_evaluating=False)
valid_dataset = MatchingDataset(batch_size=config.batch_size,
folder="datasets/matching_aracati/validation",
x_shape=config.input_shape,
y_shape=config.output_shape,
is_evaluating=False)
# Define the model.
loss = tf.keras.losses.BinaryCrossentropy()
ranger = Lookahead(RectifiedAdam(learning_rate=config.learning_rate), sync_period=6, slow_step_size=0.5)
model = DizygoticNet(filters=config.filters, loss=loss, optimizer=ranger)
# Define the logger.
logger = Logger()
# Define the trainer.
trainer = MatchingTrainer(model=model, logger=logger, train_dataset=train_dataset, valid_dataset=valid_dataset)
if config.mode == "restore":
trainer.load_checkpoint()
trainer.train()
else:
# Define the test dataset.
test_dataset = MatchingDataset(batch_size=1,
folder="datasets/matching_aracati/test",
x_shape=config.input_shape,
y_shape=config.output_shape,
is_evaluating=True)
# Define the model.
model = DizygoticNet(filters=config.filters, loss=None, optimizer=None)
# Define the evaluator.
evaluator = MatchingEvaluator(model=model, dataset=test_dataset)
evaluator.load_checkpoint()
evaluator.evaluate()
if __name__ == '__main__':
logging.set_verbosity(logging.INFO)
app.run(main)