def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if not val_dataset:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
para_state_dict = paddle.load(args.model_path)
model.set_dict(para_state_dict)
logger.info('Loaded trained params of model successfully')
evaluate(model, val_dataset)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
# Only support for the DeepLabv3+ model
if args.data_format == 'NHWC':
if cfg.dic['model']['type'] != 'DeepLabV3P':
raise ValueError(
'The "NHWC" data format only support the DeepLabV3P model!')
cfg.dic['model']['data_format'] = args.data_format
cfg.dic['model']['backbone']['data_format'] = args.data_format
loss_len = len(cfg.dic['loss']['types'])
for i in range(loss_len):
cfg.dic['loss']['types'][i]['data_format'] = args.data_format
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
skip_quant(model)
quantizer = QAT(config=quant_config)
quant_model = quantizer.quantize(model)
logger.info('Quantize the model successfully')
if args.model_path:
utils.load_entire_model(quant_model, args.model_path)
logger.info('Loaded trained params of model successfully')
test_config = get_test_config(cfg, args)
config_check(cfg, val_dataset=val_dataset)
evaluate(quant_model,
val_dataset,
num_workers=args.num_workers,
**test_config)
def main(args):
env_info = get_sys_env()
place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[
'GPUs used'] else 'cpu'
paddle.set_device(place)
if not args.cfg:
raise RuntimeError('No configuration file specified.')
cfg = Config(args.cfg)
val_dataset = cfg.val_dataset
if val_dataset is None:
raise RuntimeError(
'The verification dataset is not specified in the configuration file.'
)
elif len(val_dataset) == 0:
raise ValueError(
'The length of val_dataset is 0. Please check if your dataset is valid'
)
msg = '\n---------------Config Information---------------\n'
msg += str(cfg)
msg += '------------------------------------------------'
logger.info(msg)
model = cfg.model
if args.model_path:
utils.load_entire_model(model, args.model_path)
logger.info('Loaded trained params of model successfully')
config_check(cfg, val_dataset=val_dataset)
evaluate(
model,
val_dataset,
aug_eval=args.aug_eval,
scales=args.scales,
flip_horizontal=args.flip_horizontal,
flip_vertical=args.flip_vertical,
is_slide=args.is_slide,
crop_size=args.crop_size,
stride=args.stride,
num_workers=args.num_workers,
)
save_dir = 'my_save_model', # 训练模型保存路径
iters = 2500, # 总迭代次数
batch_size = 2, # 每批处理图片张数
save_interval = 250, # 保存和评估间隔
log_iters = 20, # 训练日志打印间隔
use_vdl = True # 使用VisualDL
)
# 8.0评估测试集
from paddleseg.core import evaluate
model = UNet(num_classes=3)
#换自己保存的模型文件
model_path = '/home/aistudio/my_save_model/best_model/model.pdparams'
para_state_dict = paddle.load(model_path)
model.set_dict(para_state_dict)
evaluate(model,val_dataset)
from paddleseg.core import predict
transforms = T.Compose([
T.Resize(target_size=(512, 512)),
T.Normalize()
])
model = UNet(num_classes=3)
#生成图片列表
image_list = []
with open('/home/aistudio/work/newdata/test_list.txt' ,'r') as f:
for line in f.readlines():
image_list.append(line.split()[0])
predict(