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train_segmentation.py
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train_segmentation.py
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import tensorflow as tf
import numpy as np
import time
import os
from dataset.craft_datasets import craft_datasets, py_read_data_and_label, crop_to_shape
from tools.categorical_metrics import CategoricalMetric, CategoricalF1, CustomCounter, CustomReduceMetric
from tools.craft_network import craft_network
import tools.config as config
def get_tensorboard_log_dir():
root_log_dir = os.path.join(os.curdir, "logs")
run_id = time.strftime("run_%Y_%m_%d_%H_%M_%S")
return os.path.join(root_log_dir, run_id)
def get_csv_dir():
root_log_dir = os.path.join(os.curdir, "csv_logs")
run_id = time.strftime("run_%Y_%m_%d_%H_%M_%S")
return os.path.join(root_log_dir, run_id)
def __custom_loss(y_true, y_pred):
y_true = tf.cast(y_true, dtype = tf.float32)
y_pred = tf.cast(y_pred, dtype = tf.float32)
# count_0 = tf.reduce_sum(tf.cast(y_true == 0.0, y_true.dtype))
# count_1 = tf.reduce_sum(tf.cast(y_true == 1.0, y_true.dtype))
# background_weight = (1 - count_0 / (count_0 + count_1)) * config.LOSS_SCALER
# foreground_weight = (1 - count_1 / (count_0 + count_1)) * config.LOSS_SCALER / 5
background_weight = config.BACKGROUND_WEIGHT
foreground_weight = config.FOREGROUND_WEIGHT
foreground_weight -= background_weight
scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False)
loss = scce(
tf.maximum(y_true, 0.0), # remove -1 values from mask,
y_pred,
sample_weight = tf.maximum(y_true * foreground_weight + background_weight, 0.0)
)
return loss
def main():
ds_train = craft_datasets(os.path.join(config.TFRECORD_FOLDER, "train"))
ds_valid = craft_datasets(os.path.join(config.TFRECORD_FOLDER, "valid"))
ds_train = ds_train.prefetch(1).repeat(2)
model = craft_network(config.MODEL_CHECKPOINT)
# predict_on_random_data(model)
checkpoint_cb = tf.keras.callbacks.ModelCheckpoint(config.MODEL_CHECKPOINT, monitor = config.MONITOR_METRIC,
mode = config.MONITOR_MODE, verbose = 2, save_best_only = True)
csv_logger = tf.keras.callbacks.CSVLogger(get_csv_dir(), separator = ',', append = True)
tensorboard_cb = tf.keras.callbacks.TensorBoard(get_tensorboard_log_dir())
reduce_lr_on_plateau = tf.keras.callbacks.ReduceLROnPlateau(factor = 0.1,
monitor = config.MONITOR_METRIC,
patience = 30,
cooldown = 10,
min_lr = 0.000001,
verbose = 1,
mode = config.MONITOR_MODE)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor = config.MONITOR_METRIC, mode = config.MONITOR_MODE, patience = 200,
verbose = 1)
model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = config.INITIAL_LEARNING_RATE),
loss = __custom_loss,
metrics = [
'accuracy',
CategoricalMetric(tf.keras.metrics.TruePositives(), name = 'custom_tp'),
CategoricalMetric(tf.keras.metrics.FalsePositives(), name = 'custom_fp'),
CategoricalMetric(tf.keras.metrics.TrueNegatives(), name = 'custom_tn'),
CategoricalMetric(tf.keras.metrics.FalseNegatives(), name = 'custom_fn'),
CategoricalMetric(tf.keras.metrics.Accuracy(), name = 'custom_accuracy'),
CategoricalMetric(tf.keras.metrics.Precision(), name = 'custom_precision'),
CategoricalMetric(tf.keras.metrics.Recall(), name = 'custom_recall'),
CategoricalF1(name = 'custom_f1'),
CustomReduceMetric(what = "y_true", reduce = "max", name = 'custom_max_y_true'),
CustomReduceMetric(what = "y_pred", reduce = "max", name = 'custom_max_y_pred'),
CustomReduceMetric(what = "y_true", reduce = "min", name = 'custom_min_y_true'),
CustomReduceMetric(what = "y_pred", reduce = "min", name = 'custom_min_y_pred'),
CustomReduceMetric(what = "y_true", reduce = "sum", name = 'custom_sum_y_true'),
CustomReduceMetric(what = "y_pred", reduce = "sum", name = 'custom_sum_y_pred'),
CustomCounter(name = 'custom_counter'),
])
history = model.fit(
ds_train,
epochs = config.EPOCHS,
validation_data = ds_valid,
callbacks = [
checkpoint_cb,
tensorboard_cb,
reduce_lr_on_plateau,
csv_logger,
# early_stopping,
tf.keras.callbacks.TerminateOnNaN()],
verbose = 1,
# workers = 2
)
def test_loss():
y_true = np.array([[[[[ 1], [ 0]], [[ 0], [ 0]]], [[[ 0], [ 0]], [[ 0], [ 0]]]]])
y_pred = np.array([[[[[0.1, 4.9], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]], [[[0.9, 0.1], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]]]])
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
print("shape of y_true: ", y_true.shape)
print("shape of y_pred: ", y_pred.shape)
loss = __custom_loss(y_true, y_pred)
print("expect 1, predict 1: loss {:.4f}".format(loss.numpy()))
y_true = np.array([[[[[ 1], [ 0]], [[ 0], [ 0]]], [[[ 0], [ 0]], [[ 0], [ 0]]]]])
y_pred = np.array([[[[[0.1, 0.9], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]], [[[0.9, 0.1], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]]]])
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
loss = __custom_loss(y_true, y_pred)
print("expect 1, predict 1: loss {:.4f}".format(loss.numpy()))
y_true = np.array([[[[[ 1], [ 0]], [[ 0], [ 0]]], [[[ 0], [ 0]], [[ 0], [ 0]]]]])
y_pred = np.array([[[[[4.9, 0.1], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]], [[[0.9, 0.1], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]]]])
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
loss = __custom_loss(y_true, y_pred)
print("expect 1, predict 0: loss {:.4f}".format(loss.numpy()))
y_true = np.array([[[[[ 1], [ 0]], [[ 0], [ 0]]], [[[ 0], [ 0]], [[ 0], [ 0]]]]])
y_pred = np.array([[[[[0.1, 0.9], [0.1, 4.9]], [[0.9, 0.1], [0.9, 0.1]]], [[[0.9, 0.1], [0.9, 0.1]], [[0.9, 0.1], [0.9, 0.1]]]]])
y_true = tf.convert_to_tensor(y_true)
y_pred = tf.convert_to_tensor(y_pred)
loss = __custom_loss(y_true, y_pred)
print("expect 0, predict 1: loss {:.4f}".format(loss.numpy()))
if __name__ == "__main__":
main()
# test_loss()