提交 2cf8014d 编写于 作者: littletomatodonkey's avatar littletomatodonkey

save best model

上级 07796136
......@@ -383,17 +383,21 @@ def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
tic = time.time()
for i, m in enumerate(metrics):
metric_list[i].update(m[0], len(batch[0]))
fetchs_str = ''.join([str(m.value)+' '
for m in metric_list]+ [batch_time.value])
fetchs_str = ''.join([str(m.value) + ' '
for m in metric_list] + [batch_time.value])
if epoch != -1:
logger.info("epoch:{:<3d} {:s} step:{:<4d} {:s}s".format(
epoch, mode, idx, fetchs_str))
epoch, mode, idx, fetchs_str))
else:
logger.info("{:s} step:{:<4d} {:s}s".format(
mode, idx, fetchs_str))
logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx, fetchs_str))
end_str = ''.join([str(m.mean)+' ' for m in metric_list] + [batch_time.total])
if epoch!= -1:
end_str = ''.join([str(m.mean) + ' '
for m in metric_list] + [batch_time.total])
if epoch != -1:
logger.info("END epoch:{:<3d} {:s} {:s}s".format(epoch, mode, end_str))
else:
logger.info("END {:s} {:s}s".format(mode, end_str))
# save the best model
top1_acc = fetchs["top1"][1].avg
return top1_acc
......@@ -26,6 +26,7 @@ from paddle.fluid.incubate.fleet.collective import fleet
from ppcls.data import Reader
from ppcls.utils.config import get_config
from ppcls.utils.save_load import init_model, save_model
from ppcls.utils import logger
import program
......@@ -61,6 +62,10 @@ def main(args):
startup_prog = fluid.Program()
train_prog = fluid.Program()
# best_top1_acc_list[0]: top1 acc
# best_top1_acc_list[1]: epoch id
best_top1_acc_list = [0.0, 0]
train_dataloader, train_fetchs = program.build(
config, train_prog, startup_prog, is_train=True)
......@@ -94,8 +99,16 @@ def main(args):
epoch_id, 'train')
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid')
top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid')
if top1_acc > best_top1_acc_list[0]:
best_top1_acc_list[0] = top1_acc
best_top1_acc_list[1] = epoch_id
logger.info("Best top1 acc: {}, in epoch: {}".format(
best_top1_acc_list[0], best_top1_acc_list[1]))
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, "best_model")
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
......
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