add dynamic train
WuHaobo
parent
166c3a88fe
commit
798fe1aa61
100
tools/train.py
100
tools/train.py
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@ -20,8 +20,6 @@ import argparse
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import os
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import os
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import paddle.fluid as fluid
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import paddle.fluid as fluid
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from paddle.fluid.incubate.fleet.base import role_maker
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from paddle.fluid.incubate.fleet.collective import fleet
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from ppcls.data import Reader
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from ppcls.data import Reader
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from ppcls.utils.config import get_config
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from ppcls.utils.config import get_config
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@ -49,73 +47,63 @@ def parse_args():
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def main(args):
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def main(args):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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config = get_config(args.config, overrides=args.override, show=True)
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config = get_config(args.config, overrides=args.override, show=True)
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# assign the place
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# assign the place
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gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
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gpu_id = fluid.dygraph.parallel.Env().dev_id
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place = fluid.CUDAPlace(gpu_id)
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place = fluid.CUDAPlace(gpu_id)
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# startup_prog is used to do some parameter init work,
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with fluid.dygraph.guard(place):
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# and train prog is used to hold the network
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strategy = fluid.dygraph.parallel.prepare_context()
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startup_prog = fluid.Program()
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net = program.create_model(config.ARCHITECTURE, config.classes_num)
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train_prog = fluid.Program()
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net = fluid.dygraph.parallel.DataParallel(net, strategy)
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best_top1_acc = 0.0 # best top1 acc record
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optimizer = program.create_optimizer(
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config, parameter_list=net.parameters())
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train_dataloader, train_fetchs = program.build(
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# load model from checkpoint or pretrained model
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config, train_prog, startup_prog, is_train=True)
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init_model(config, net, optimizer)
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if config.validate:
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train_dataloader = program.create_dataloader()
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valid_prog = fluid.Program()
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train_reader = Reader(config, 'train')()
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valid_dataloader, valid_fetchs = program.build(
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train_dataloader.set_sample_list_generator(train_reader, place)
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config, valid_prog, startup_prog, is_train=False)
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# clone to prune some content which is irrelevant in valid_prog
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valid_prog = valid_prog.clone(for_test=True)
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# create the "Executor" with the statement of which place
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if config.validate:
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exe = fluid.Executor(place=place)
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valid_dataloader = program.create_dataloader()
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# only run startup_prog once to init
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valid_reader = Reader(config, 'valid')()
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exe.run(startup_prog)
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valid_dataloader.set_sample_list_generator(valid_reader, place)
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# load model from checkpoint or pretrained model
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best_top1_acc = 0.0 # best top1 acc record
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init_model(config, train_prog, exe)
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for epoch_id in range(config.epochs):
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net.train()
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# 1. train with train dataset
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program.run(train_dataloader, config, net, optimizer, epoch_id,
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'train')
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train_reader = Reader(config, 'train')()
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if fluid.dygraph.parallel.Env().local_rank == 0:
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train_dataloader.set_sample_list_generator(train_reader, place)
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# 2. validate with validate dataset
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if config.validate and epoch_id % config.valid_interval == 0:
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net.eval()
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top1_acc = program.run(valid_dataloader, config, net, None,
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epoch_id, 'valid')
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if top1_acc > best_top1_acc:
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best_top1_acc = top1_acc
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message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
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best_top1_acc, epoch_id)
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logger.info("{:s}".format(
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logger.coloring(message, "RED")))
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if epoch_id % config.save_interval == 0:
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if config.validate:
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model_path = os.path.join(
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valid_reader = Reader(config, 'valid')()
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config.model_save_dir,
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valid_dataloader.set_sample_list_generator(valid_reader, place)
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config.ARCHITECTURE["name"])
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compiled_valid_prog = program.compile(config, valid_prog)
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save_model(net, optimizer, model_path,
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"best_model_in_epoch_" + str(epoch_id))
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compiled_train_prog = fleet.main_program
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# 3. save the persistable model
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for epoch_id in range(config.epochs):
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if epoch_id % config.save_interval == 0:
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# 1. train with train dataset
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model_path = os.path.join(config.model_save_dir,
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program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
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epoch_id, 'train')
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if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
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# 2. validate with validate dataset
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if config.validate and epoch_id % config.valid_interval == 0:
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top1_acc = program.run(valid_dataloader, exe,
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compiled_valid_prog, valid_fetchs,
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epoch_id, 'valid')
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if top1_acc > best_top1_acc:
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best_top1_acc = top1_acc
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message = "The best top1 acc {:.5f}, in epoch: {:d}".format(best_top1_acc, epoch_id)
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logger.info("{:s}".format(logger.coloring(message, "RED")))
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if epoch_id % config.save_interval==0:
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model_path = os.path.join(config.model_save_dir,
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config.ARCHITECTURE["name"])
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config.ARCHITECTURE["name"])
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save_model(train_prog, model_path, "best_model_in_epoch_"+str(epoch_id))
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save_model(net, optimizer, model_path, epoch_id)
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# 3. save the persistable model
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if epoch_id % config.save_interval == 0:
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model_path = os.path.join(config.model_save_dir,
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config.ARCHITECTURE["name"])
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save_model(train_prog, model_path, epoch_id)
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if __name__ == '__main__':
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if __name__ == '__main__':
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