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train.py
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train.py
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# Title: RADDet
# Authors: Ao Zhang, Erlik Nowruzi, Robert Laganiere
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import shutil
import cv2
import numpy as np
import tensorflow as tf
import tensorflow.keras as K
import matplotlib.pyplot as plt
from glob import glob
from tqdm import tqdm
import model.model as M
from dataset.batch_data_generator import DataGenerator
import metrics.mAP as mAP
import util.loader as loader
import util.helper as helper
import util.drawer as drawer
def main():
### NOTE: GPU manipulation, you may can print this out if necessary ###
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 0:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
config = loader.readConfig()
config_data = config["DATA"]
config_radar = config["RADAR_CONFIGURATION"]
config_model = config["MODEL"]
config_train = config["TRAIN"]
anchor_boxes = loader.readAnchorBoxes() # load anchor boxes with order
num_classes = len(config_data["all_classes"])
### NOTE: using the yolo head shape out from model for data generator ###
model = M.RADDet(config_model, config_data, config_train, anchor_boxes)
model.build([None] + config_model["input_shape"])
model.summary()
### NOTE: preparing data ###
data_generator = DataGenerator(config_data, config_train, config_model, \
model.features_shape, anchor_boxes)
train_generator = data_generator.trainGenerator()
validate_generator = data_generator.validateGenerator()
### NOTE: training settings ###
logdir = os.path.join(config_train["log_dir"], \
"b_" + str(config_train["batch_size"]) + \
"lr_" + str(config_train["learningrate_init"]))
if not os.path.exists(logdir):
os.makedirs(logdir)
global_steps = tf.Variable(1, trainable=False, dtype=tf.int64)
optimizer = K.optimizers.Adam(learning_rate=config_train["learningrate_init"])
writer = tf.summary.create_file_writer(logdir)
ckpt = tf.train.Checkpoint(optimizer=optimizer, model=model, step=global_steps)
log_specific_dir = os.path.join(logdir, "ckpt")
manager = tf.train.CheckpointManager(ckpt, log_specific_dir, max_to_keep=3)
### NOTE: restore from last checkpoint ###
ckpt.restore(manager.latest_checkpoint)
if manager.latest_checkpoint:
print("Restored from {}".format(manager.latest_checkpoint))
global_steps.assign(ckpt.step.numpy())
### NOTE: define training step ###
@tf.function
def train_step(data, label):
""" define train step for training """
with tf.GradientTape() as tape:
feature = model(data)
pred_raw, pred = model.decodeYolo(feature)
total_loss, box_loss, conf_loss, category_loss = \
model.loss(pred_raw, pred, label, raw_boxes[..., :6])
gradients = tape.gradient(total_loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
### NOTE: writing summary data ###
with writer.as_default():
tf.summary.scalar("lr", optimizer.lr, step=global_steps)
tf.summary.scalar("loss/total_loss", total_loss, step=global_steps)
tf.summary.scalar("loss/box_loss", box_loss, step=global_steps)
tf.summary.scalar("loss/conf_loss", conf_loss, step=global_steps)
tf.summary.scalar("loss/category_loss", category_loss, step=global_steps)
writer.flush()
return total_loss, box_loss, conf_loss, category_loss
### NOTE: define validate step ###
# @tf.function
def validate_step():
mean_ap_test = 0.0
ap_all_class_test = []
ap_all_class = []
total_losstest = []
box_losstest = []
conf_losstest = []
category_losstest = []
for class_id in range(num_classes):
ap_all_class.append([])
for data, label, raw_boxes in validate_generator.\
batch(data_generator.batch_size).take(data_generator.total_validate_batches):
feature = model(data)
pred_raw, pred = model.decodeYolo(feature)
total_loss_b, box_loss_b, conf_loss_b, category_loss_b = \
model.loss(pred_raw, pred, label, raw_boxes[..., :6])
total_losstest.append(total_loss_b)
box_losstest.append(box_loss_b)
conf_losstest.append(conf_loss_b)
category_losstest.append(category_loss_b)
for batch_id in range(raw_boxes.shape[0]):
raw_boxes_frame = raw_boxes[batch_id]
pred_frame = pred[batch_id]
predicitons = helper.yoloheadToPredictions(pred_frame, \
conf_threshold=config_model["confidence_threshold"])
nms_pred = helper.nms(predicitons, config_model["nms_iou3d_threshold"], \
config_model["input_shape"], sigma=0.3, method="nms")
mean_ap, ap_all_class = mAP.mAP(nms_pred, raw_boxes_frame.numpy(), \
config_model["input_shape"], ap_all_class, \
tp_iou_threshold=config_model["mAP_iou3d_threshold"])
mean_ap_test += mean_ap
for ap_class_i in ap_all_class:
if len(ap_class_i) == 0:
class_ap = 0.
else:
class_ap = np.mean(ap_class_i)
ap_all_class_test.append(class_ap)
mean_ap_test /= data_generator.batch_size*data_generator.total_validate_batches
tf.print("-------> ap: %.6f"%(mean_ap_test))
### writing summary data ###
with writer.as_default():
tf.summary.scalar("ap/ap_all", mean_ap_test, step=global_steps)
tf.summary.scalar("ap/ap_person", ap_all_class_test[0], step=global_steps)
tf.summary.scalar("ap/ap_bicycle", ap_all_class_test[1], step=global_steps)
tf.summary.scalar("ap/ap_car", ap_all_class_test[2], step=global_steps)
tf.summary.scalar("ap/ap_motorcycle", ap_all_class_test[3], step=global_steps)
tf.summary.scalar("ap/ap_bus", ap_all_class_test[4], step=global_steps)
tf.summary.scalar("ap/ap_truck", ap_all_class_test[5], step=global_steps)
### NOTE: validate loss ###
tf.summary.scalar("validate_loss/total_loss", \
np.mean(total_losstest), step=global_steps)
tf.summary.scalar("validate_loss/box_loss", \
np.mean(box_losstest), step=global_steps)
tf.summary.scalar("validate_loss/conf_loss", \
np.mean(conf_losstest), step=global_steps)
tf.summary.scalar("validate_loss/category_loss", \
np.mean(category_losstest), step=global_steps)
writer.flush()
###---------------------------- TRAIN SET -------------------------###
for data, label, raw_boxes in train_generator.repeat().\
batch(data_generator.batch_size).take(data_generator.total_train_batches):
total_loss, box_loss, conf_loss, category_loss = train_step(data, label)
tf.print("=======> train step: %4d, lr: %.6f, total_loss: %4.2f, \
box_loss: %4.2f, conf_loss: %4.2f, category_loss: %4.2f" % \
(global_steps, optimizer.lr.numpy(), total_loss, box_loss, \
conf_loss, category_loss))
### NOTE: learning rate decay ###
global_steps.assign_add(1)
if global_steps < config_train["warmup_steps"]:
# lr = config_train["learningrate_init"]
if global_steps < config_train["startup_steps"]:
lr = config_train["learningrate_startup"]
else:
lr = config_train["learningrate_init"]
optimizer.lr.assign(lr)
elif global_steps % config_train["learningrate_decay_gap"] == 0:
lr = optimizer.lr.numpy()
lr = config_train["learningrate_end"] + \
config_train["learningrate_decay"] * \
(lr - config_train["learningrate_end"])
optimizer.lr.assign(lr)
###---------------------------- VALIDATE SET -------------------------###
if global_steps.numpy() >= config_train["validate_start_steps"] and \
global_steps.numpy() % config_train["validate_gap"] == 0:
validate_step()
save_path = manager.save()
print("Saved checkpoint for step {}: {}".format(int(ckpt.step), save_path))
if __name__ == "__main__":
main()