def eval(eval_file_list, batch_size, data_args, model_path):
'''
评估脚本，用于评估训好模型
:param eval_file_list:要测试的图像列表
:param batch_size:batch的大小
:param data_args:数据的设置参数
:param model_path:模型的路径
:return:
'''
# 通过神经网络模型获取损失函数和额外层
cost, detect_out = vgg_ssd_net.net_conf(mode='eval')
# 检查模型模型路径是否正确
assert os.path.isfile(model_path), 'Invalid model.'
# 通过训练好的模型生成参数
parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))
# 创建优化方法
optimizer = paddle.optimizer.Momentum()
# 创建训练器
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
extra_layers=[detect_out],
update_equation=optimizer)
# 定义数据层之间的关系
feeding = {'image': 0, 'bbox': 1}
# 生成要训练的数据
reader = paddle.batch(data_provider.test(data_args, eval_file_list),
batch_size=batch_size)
# 获取测试结果
result = trainer.test(reader=reader, feeding=feeding)
# 打印模型的测试信息
print "TestCost: %f, Detection mAP=%g" % \
(result.cost, result.metrics['detection_evaluator'])
def train(train_file_list, dev_file_list, data_args, init_model_path):
optimizer = paddle.optimizer.Momentum(
momentum=cfg.TRAIN.MOMENTUM,
learning_rate=cfg.TRAIN.LEARNING_RATE,
regularization=paddle.optimizer.L2Regularization(
rate=cfg.TRAIN.L2REGULARIZATION),
learning_rate_decay_a=cfg.TRAIN.LEARNING_RATE_DECAY_A,
learning_rate_decay_b=cfg.TRAIN.LEARNING_RATE_DECAY_B,
learning_rate_schedule=cfg.TRAIN.LEARNING_RATE_SCHEDULE)
cost, detect_out = vgg_ssd_net.net_conf('train')
parameters = paddle.parameters.create(cost)
if not (init_model_path is None):
assert os.path.isfile(init_model_path), 'Invalid model.'
parameters.init_from_tar(gzip.open(init_model_path))
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
extra_layers=[detect_out],
update_equation=optimizer)
feeding = {'image': 0, 'bbox': 1}
train_reader = paddle.batch(
data_provider.train(data_args, train_file_list),
batch_size=cfg.TRAIN.BATCH_SIZE) # generate a batch image each time
dev_reader = paddle.batch(
data_provider.test(data_args, dev_file_list),
batch_size=cfg.TRAIN.BATCH_SIZE)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1 == 0:
print "\nPass %d, Batch %d, TrainCost %f, Detection mAP=%f" % \
(event.pass_id,
event.batch_id,
event.cost,
event.metrics['detection_evaluator'])
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
with gzip.open('checkpoints/params_pass_%05d.tar.gz' % \
event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=dev_reader, feeding=feeding)
print "\nTest with Pass %d, TestCost: %f, Detection mAP=%g" % \
(event.pass_id,
result.cost,
result.metrics['detection_evaluator'])
trainer.train(
reader=train_reader,
event_handler=event_handler,
num_passes=cfg.TRAIN.NUM_PASS,
feeding=feeding)
def eval(eval_file_list, batch_size, data_args, model_path):
cost, detect_out = vgg_ssd_net.net_conf(mode='eval')
assert os.path.isfile(model_path), 'Invalid model.'
parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))
optimizer = paddle.optimizer.Momentum()
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
extra_layers=[detect_out],
update_equation=optimizer)
feeding = {'image': 0, 'bbox': 1}
reader = paddle.batch(data_provider.test(data_args, eval_file_list),
batch_size=batch_size)
result = trainer.test(reader=reader, feeding=feeding)
print "TestCost: %f, Detection mAP=%g" % \
(result.cost, result.metrics['detection_evaluator'])
def train(train_file_list, dev_file_list, data_args, init_model_path=None):
# 创建优化方法
optimizer = paddle.optimizer.Momentum(
momentum=cfg.TRAIN.MOMENTUM,
learning_rate=cfg.TRAIN.LEARNING_RATE,
regularization=paddle.optimizer.L2Regularization(
rate=cfg.TRAIN.L2REGULARIZATION),
learning_rate_decay_a=cfg.TRAIN.LEARNING_RATE_DECAY_A,
learning_rate_decay_b=cfg.TRAIN.LEARNING_RATE_DECAY_B,
learning_rate_schedule=cfg.TRAIN.LEARNING_RATE_SCHEDULE)
# 通过神经网络模型获取损失函数和额外层
cost, detect_out = vgg_ssd_net.net_conf('train')
# 通过损失函数创建训练参数
parameters = paddle.parameters.create(cost)
# 如果有训练好的模型，可以使用训练好的模型再训练
if not (init_model_path is None):
assert os.path.isfile(init_model_path), 'Invalid model.'
parameters.init_from_tar(gzip.open(init_model_path))
# 创建训练器
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
extra_layers=[detect_out],
update_equation=optimizer)
# 定义数据层之间的关系
feeding = {'image': 0, 'bbox': 1}
# 创建训练数据
train_reader = paddle.batch(data_provider.train(data_args,
train_file_list),
batch_size=cfg.TRAIN.BATCH_SIZE)
# 创建测试数据
dev_reader = paddle.batch(data_provider.test(data_args, dev_file_list),
batch_size=cfg.TRAIN.BATCH_SIZE)
# 定义训练事件
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, TrainCost %f, Detection mAP=%f" % \
(event.pass_id,
event.batch_id,
event.cost,
event.metrics['detection_evaluator'])
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
with gzip.open('../models/params_pass.tar.gz', 'w') as f:
trainer.save_parameter_to_tar(f)
result = trainer.test(reader=dev_reader, feeding=feeding)
print "\nTest with Pass %d, TestCost: %f, Detection mAP=%g" % \
(event.pass_id,
result.cost,
result.metrics['detection_evaluator'])
# 开始训练
trainer.train(reader=train_reader,
event_handler=event_handler,
num_passes=cfg.TRAIN.NUM_PASS,
feeding=feeding)
