def main(args, return_dict={}):
config = get_config(args.config, overrides=args.override, show=True)
config.mode = "valid"
# assign place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
if config["use_data_parallel"]:
net = paddle.DataParallel(net)
init_model(config, net, optimizer=None)
valid_dataloader = Reader(config, 'valid', places=place)()
net.eval()
with paddle.no_grad():
top1_acc = program.run(valid_dataloader, config, net, None, None, 0,
'valid')
return_dict["top1_acc"] = top1_acc
return top1_acc
def main(args):
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
config = get_config(args.config, overrides=args.override, show=True)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
startup_prog = fluid.Program()
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(
config, valid_prog, startup_prog, is_train=False)
valid_prog = valid_prog.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(config, valid_prog, exe)
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, place)
compiled_valid_prog = program.compile(config, valid_prog)
program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, -1,
'eval', config)
def main(args):
config = get_config(args.config, overrides=args.override, show=True)
use_gpu = config.get("use_gpu", True)
places = fluid.cuda_places() if use_gpu else fluid.cpu_places()
startup_prog = fluid.Program()
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(config,
valid_prog,
startup_prog,
is_train=False,
is_distributed=False)
valid_prog = valid_prog.clone(for_test=True)
exe = fluid.Executor(places[0])
exe.run(startup_prog)
init_model(config, valid_prog, exe)
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, places)
compiled_valid_prog = program.compile(config, valid_prog)
program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, -1,
'eval')
def main(args):
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = fluid.Program()
train_prog = fluid.Program()
train_dataloader, train_fetchs = program.build(config,
train_prog,
startup_prog,
is_train=True)
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(config,
valid_prog,
startup_prog,
is_train=False)
# clone to prune some content which is irrelevant in valid_prog
valid_prog = valid_prog.clone(for_test=True)
# create the "Executor" with the statement of which place
exe = fluid.Executor(place=place)
# only run startup_prog once to init
exe.run(startup_prog)
# load model from checkpoint or pretrained model
init_model(config, train_prog, exe)
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, place)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, place)
compiled_valid_prog = program.compile(config, valid_prog)
compiled_train_prog = fleet.main_program
for epoch_id in range(config.epochs):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
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')
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, epoch_id)
def main(args, return_dict={}):
config = get_config(args.config, overrides=args.override, show=True)
config.mode = "valid"
# assign place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
multilabel = config.get("multilabel", False)
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
init_model(config, net, optimizer=None)
valid_dataloader = Reader(config, 'valid', places=place)()
net.eval()
with paddle.no_grad():
if not multilabel:
top1_acc = program.run(valid_dataloader, config, net, None, None,
0, 'valid')
return_dict["top1_acc"] = top1_acc
return top1_acc
else:
all_outs = []
targets = []
for _, batch in enumerate(valid_dataloader()):
feeds = program.create_feeds(batch, False, config.classes_num,
multilabel)
out = net(feeds["image"])
out = F.sigmoid(out)
use_distillation = config.get("use_distillation", False)
if use_distillation:
out = out[1]
all_outs.extend(list(out.numpy()))
targets.extend(list(feeds["label"].numpy()))
all_outs = np.array(all_outs)
targets = np.array(targets)
mAP = mean_average_precision(all_outs, targets)
return_dict["mean average precision"] = mAP
return mAP
def main(args):
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
config = get_config(args.config, overrides=args.override, show=True)
place = env.place()
startup_prog = fluid.Program()
train_prog = fluid.Program()
train_dataloader, train_fetchs = program.build(
config, train_prog, startup_prog, is_train=True)
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(
config, valid_prog, startup_prog, is_train=False)
valid_prog = valid_prog.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(config, train_prog, exe)
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, place)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, place)
compiled_valid_prog = program.compile(config, valid_prog)
compiled_train_prog = fleet.main_program
for epoch_id in range(config.epochs):
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
epoch_id, 'train')
if config.validate and epoch_id % config.valid_interval == 0:
program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid')
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.architecture)
save_model(train_prog, model_path, epoch_id)
def main():
args = conf.parse_args()
config = conf.get_config(args.config, overrides=args.override, show=False)
assert os.path.exists(
os.path.join(config["Global"]["save_inference_dir"],
'inference.pdmodel')) and os.path.exists(
os.path.join(config["Global"]["save_inference_dir"],
'inference.pdiparams'))
config["DataLoader"]["Eval"]["sampler"]["batch_size"] = 1
config["DataLoader"]["Eval"]["loader"]["num_workers"] = 0
init_logger()
device = paddle.set_device("cpu")
train_dataloader = build_dataloader(config["DataLoader"], "Eval", device,
False)
def sample_generator(loader):
def __reader__():
for indx, data in enumerate(loader):
images = np.array(data[0])
yield images
return __reader__
paddle.enable_static()
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
paddleslim.quant.quant_post_static(
executor=exe,
model_dir=config["Global"]["save_inference_dir"],
model_filename='inference.pdmodel',
params_filename='inference.pdiparams',
quantize_model_path=os.path.join(
config["Global"]["save_inference_dir"], "quant_post_static_model"),
sample_generator=sample_generator(train_dataloader),
batch_nums=10)
def main(args, return_dict={}):
config = get_config(args.config, overrides=args.override, show=True)
config.mode = "valid"
# assign place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
if config["use_data_parallel"]:
net = paddle.DataParallel(net)
init_model(config, net, optimizer=None)
save_model(net,
None,
model_path='./pretrained/10w/',
epoch_id=0,
prefix='ppcls')
def main(args):
"""
all the config of training paradigm should be in config["Global"]
"""
config = get_config(args.config, overrides=args.override, show=False)
global_config = config["Global"]
mode = "train"
log_file = os.path.join(global_config['output_dir'],
config["Arch"]["name"], f"{mode}.log")
init_logger(log_file=log_file)
print_config(config)
if global_config.get("is_distributed", True):
fleet.init(is_collective=True)
# assign the device
use_gpu = global_config.get("use_gpu", True)
# amp related config
if 'AMP' in config:
AMP_RELATED_FLAGS_SETTING = {
'FLAGS_cudnn_exhaustive_search': 1,
'FLAGS_conv_workspace_size_limit': 1500,
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
'FLAGS_max_inplace_grad_add': 8,
}
os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1'
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
use_xpu = global_config.get("use_xpu", False)
use_npu = global_config.get("use_npu", False)
use_mlu = global_config.get("use_mlu", False)
assert (
use_gpu and use_xpu and use_npu and use_mlu
) is not True, "gpu, xpu, npu and mlu can not be true in the same time in static mode!"
if use_gpu:
device = paddle.set_device('gpu')
elif use_xpu:
device = paddle.set_device('xpu')
elif use_npu:
device = paddle.set_device('npu')
elif use_mlu:
device = paddle.set_device('mlu')
else:
device = paddle.set_device('cpu')
# visualDL
vdl_writer = None
if global_config["use_visualdl"]:
vdl_dir = os.path.join(global_config["output_dir"], "vdl")
vdl_writer = LogWriter(vdl_dir)
# build dataloader
eval_dataloader = None
use_dali = global_config.get('use_dali', False)
class_num = config["Arch"].get("class_num", None)
config["DataLoader"].update({"class_num": class_num})
train_dataloader = build_dataloader(config["DataLoader"],
"Train",
device=device,
use_dali=use_dali)
if global_config["eval_during_train"]:
eval_dataloader = build_dataloader(config["DataLoader"],
"Eval",
device=device,
use_dali=use_dali)
step_each_epoch = len(train_dataloader)
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = paddle.static.Program()
train_prog = paddle.static.Program()
best_top1_acc = 0.0 # best top1 acc record
train_fetchs, lr_scheduler, train_feeds, optimizer = program.build(
config,
train_prog,
startup_prog,
class_num,
step_each_epoch=step_each_epoch,
is_train=True,
is_distributed=global_config.get("is_distributed", True))
if global_config["eval_during_train"]:
eval_prog = paddle.static.Program()
eval_fetchs, _, eval_feeds, _ = program.build(
config,
eval_prog,
startup_prog,
is_train=False,
is_distributed=global_config.get("is_distributed", True))
# clone to prune some content which is irrelevant in eval_prog
eval_prog = eval_prog.clone(for_test=True)
# create the "Executor" with the statement of which device
exe = paddle.static.Executor(device)
# Parameter initialization
exe.run(startup_prog)
# load pretrained models or checkpoints
init_model(global_config, train_prog, exe)
if 'AMP' in config and config.AMP.get("level", "O1") == "O2":
optimizer.amp_init(device,
scope=paddle.static.global_scope(),
test_program=eval_prog
if global_config["eval_during_train"] else None)
if not global_config.get("is_distributed", True):
compiled_train_prog = program.compile(
config, train_prog, loss_name=train_fetchs["loss"][0].name)
else:
compiled_train_prog = train_prog
if eval_dataloader is not None:
compiled_eval_prog = program.compile(config, eval_prog)
for epoch_id in range(global_config["epochs"]):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_feeds,
train_fetchs, epoch_id, 'train', config, vdl_writer,
lr_scheduler, args.profiler_options)
# 2. evaate with eval dataset
if global_config["eval_during_train"] and epoch_id % global_config[
"eval_interval"] == 0:
top1_acc = program.run(eval_dataloader, exe, compiled_eval_prog,
eval_feeds, eval_fetchs, epoch_id, "eval",
config)
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info(message)
if epoch_id % global_config["save_interval"] == 0:
model_path = os.path.join(global_config["output_dir"],
config["Arch"]["name"])
save_model(train_prog, model_path, "best_model")
# 3. save the persistable model
if epoch_id % global_config["save_interval"] == 0:
model_path = os.path.join(global_config["output_dir"],
config["Arch"]["name"])
save_model(train_prog, model_path, epoch_id)
def main(args):
paddle.seed(12345)
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
optimizer, lr_scheduler = program.create_optimizer(
config, parameter_list=net.parameters())
if config["use_data_parallel"]:
net = paddle.DataParallel(net)
# load model from checkpoint or pretrained model
init_model(config, net, optimizer)
train_dataloader = Reader(config, 'train', places=place)()
if config.validate:
valid_dataloader = Reader(config, 'valid', places=place)()
last_epoch_id = config.get("last_epoch", -1)
best_top1_acc = 0.0 # best top1 acc record
best_top1_epoch = last_epoch_id
for epoch_id in range(last_epoch_id + 1, config.epochs):
net.train()
# 1. train with train dataset
program.run(train_dataloader, config, net, optimizer, lr_scheduler,
epoch_id, 'train')
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
net.eval()
with paddle.no_grad():
top1_acc = program.run(valid_dataloader, config, net, None,
None, epoch_id, 'valid')
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
best_top1_epoch = epoch_id
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(net, optimizer, model_path, "best_model")
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, best_top1_epoch)
logger.info("{:s}".format(logger.coloring(message, "RED")))
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(net, optimizer, model_path, epoch_id)
def main(args):
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = fluid.Program()
train_prog = fluid.Program()
best_top1_acc = 0.0 # best top1 acc record
if not config.get('use_ema'):
train_dataloader, train_fetchs = program.build(config,
train_prog,
startup_prog,
is_train=True)
else:
train_dataloader, train_fetchs, ema = program.build(config,
train_prog,
startup_prog,
is_train=True)
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(config,
valid_prog,
startup_prog,
is_train=False)
# clone to prune some content which is irrelevant in valid_prog
valid_prog = valid_prog.clone(for_test=True)
# create the "Executor" with the statement of which place
exe = fluid.Executor(place)
# Parameter initialization
exe.run(startup_prog)
# load model from 1. checkpoint to resume training, 2. pretrained model to finetune
init_model(config, train_prog, exe)
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, place)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, place)
compiled_valid_prog = program.compile(config, valid_prog)
compiled_train_prog = fleet.main_program
vdl_writer = LogWriter(args.vdl_dir) if args.vdl_dir else None
for epoch_id in range(config.epochs):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
epoch_id, 'train', vdl_writer)
if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
if config.get('use_ema'):
logger.info(logger.coloring("EMA validate start..."))
with train_fetchs('ema').apply(exe):
top1_acc = program.run(valid_dataloader, exe,
compiled_valid_prog,
valid_fetchs, epoch_id, 'valid')
logger.info(logger.coloring("EMA validate over!"))
top1_acc = program.run(valid_dataloader, exe,
compiled_valid_prog, valid_fetchs,
epoch_id, 'valid')
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info("{:s}".format(logger.coloring(message, "RED")))
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path,
"best_model_in_epoch_" + str(epoch_id))
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, epoch_id)
def main(args):
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
config = get_config(args.config, overrides=args.override, show=True)
use_fp16 = config.get('use_fp16', False)
if use_fp16:
AMP_RELATED_FLAGS_SETTING = {
'FLAGS_cudnn_exhaustive_search': 1,
'FLAGS_conv_workspace_size_limit': 4000,
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
'FLAGS_max_inplace_grad_add': 8,
}
os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1'
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
# assign the place
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = fluid.Program()
train_prog = fluid.Program()
best_top1_acc = 0.0 # best top1 acc record
if not config.get('use_ema'):
train_dataloader, train_fetchs = program.build(
config, train_prog, startup_prog, is_train=True)
else:
train_dataloader, train_fetchs, ema = program.build(
config, train_prog, startup_prog, is_train=True)
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(
config, valid_prog, startup_prog, is_train=False)
# clone to prune some content which is irrelevant in valid_prog
valid_prog = valid_prog.clone(for_test=True)
# create the "Executor" with the statement of which place
exe = fluid.Executor(place)
# Parameter initialization
exe.run(startup_prog)
# load model from 1. checkpoint to resume training, 2. pretrained model to finetune
init_model(config, train_prog, exe)
if not config.get('use_dali', False):
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, place)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, place)
compiled_valid_prog = program.compile(config, valid_prog)
else:
import dali
train_dataloader = dali.train(config)
if config.validate and int(os.getenv("PADDLE_TRAINER_ID", 0)):
if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
valid_dataloader = dali.val(config)
compiled_valid_prog = program.compile(config, valid_prog)
compiled_train_prog = fleet.main_program
vdl_writer = None
if args.vdl_dir:
if version_info.major == 2:
logger.info(
"visualdl is just supported for python3, so it is disabled in python2..."
)
else:
from visualdl import LogWriter
vdl_writer = LogWriter(args.vdl_dir)
for epoch_id in range(config.epochs):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
epoch_id, 'train', config, vdl_writer)
if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
if config.get('use_ema'):
logger.info(logger.coloring("EMA validate start..."))
with ema.apply(exe):
top1_acc = program.run(
valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid', config)
logger.info(logger.coloring("EMA validate over!"))
top1_acc = program.run(valid_dataloader, exe,
compiled_valid_prog, valid_fetchs,
epoch_id, 'valid', config)
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info("{:s}".format(logger.coloring(message, "RED")))
if epoch_id % config.save_interval == 0:
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:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, epoch_id)
def main(args):
paddle.seed(12345)
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
use_gpu = config.get("use_gpu", True)
place = paddle.set_device('gpu' if use_gpu else 'cpu')
trainer_num = paddle.distributed.get_world_size()
use_data_parallel = trainer_num != 1
config["use_data_parallel"] = use_data_parallel
if config["use_data_parallel"]:
paddle.distributed.init_parallel_env()
net = program.create_model(config.ARCHITECTURE, config.classes_num)
optimizer, lr_scheduler = program.create_optimizer(
config, parameter_list=net.parameters())
dp_net = net
if config["use_data_parallel"]:
find_unused_parameters = config.get("find_unused_parameters", False)
dp_net = paddle.DataParallel(
net, find_unused_parameters=find_unused_parameters)
# load model from checkpoint or pretrained model
init_model(config, net, optimizer)
train_dataloader = Reader(config, 'train', places=place)()
if config.validate:
valid_dataloader = Reader(config, 'valid', places=place)()
last_epoch_id = config.get("last_epoch", -1)
best_top1_acc = 0.0 # best top1 acc record
best_top1_epoch = last_epoch_id
vdl_writer_path = config.get("vdl_dir", None)
vdl_writer = None
if vdl_writer_path:
from visualdl import LogWriter
vdl_writer = LogWriter(vdl_writer_path)
# Ensure that the vdl log file can be closed normally
try:
for epoch_id in range(last_epoch_id + 1, config.epochs):
net.train()
# 1. train with train dataset
program.run(train_dataloader, config, dp_net, optimizer,
lr_scheduler, epoch_id, 'train', vdl_writer)
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
net.eval()
with paddle.no_grad():
top1_acc = program.run(valid_dataloader, config, net, None,
None, epoch_id, 'valid', vdl_writer)
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
best_top1_epoch = epoch_id
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(net, optimizer, model_path, "best_model")
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, best_top1_epoch)
logger.info(message)
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(net, optimizer, model_path, epoch_id)
except Exception as e:
logger.error(e)
finally:
vdl_writer.close() if vdl_writer else None
def main(args):
config = get_config(args.config, overrides=args.override, show=True)
if config.get("is_distributed", True):
fleet.init(is_collective=True)
# assign the place
use_gpu = config.get("use_gpu", True)
# amp related config
use_amp = config.get('use_amp', False)
use_pure_fp16 = config.get('use_pure_fp16', False)
if use_amp or use_pure_fp16:
AMP_RELATED_FLAGS_SETTING = {
'FLAGS_cudnn_exhaustive_search': 1,
'FLAGS_conv_workspace_size_limit': 4000,
'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
'FLAGS_max_inplace_grad_add': 8,
}
os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1'
paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
use_xpu = config.get("use_xpu", False)
assert (
use_gpu and use_xpu
) is not True, "gpu and xpu can not be true in the same time in static mode!"
if use_gpu:
place = paddle.set_device('gpu')
elif use_xpu:
place = paddle.set_device('xpu')
else:
place = paddle.set_device('cpu')
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = paddle.static.Program()
train_prog = paddle.static.Program()
best_top1_acc = 0.0 # best top1 acc record
train_fetchs, lr_scheduler, train_feeds = program.build(
config,
train_prog,
startup_prog,
is_train=True,
is_distributed=config.get("is_distributed", True))
if config.validate:
valid_prog = paddle.static.Program()
valid_fetchs, _, valid_feeds = program.build(config,
valid_prog,
startup_prog,
is_train=False,
is_distributed=config.get(
"is_distributed",
True))
# clone to prune some content which is irrelevant in valid_prog
valid_prog = valid_prog.clone(for_test=True)
# create the "Executor" with the statement of which place
exe = paddle.static.Executor(place)
# Parameter initialization
exe.run(startup_prog)
if config.get("use_pure_fp16", False):
cast_parameters_to_fp16(place, train_prog, fluid.global_scope())
# load pretrained models or checkpoints
init_model(config, train_prog, exe)
if not config.get("is_distributed", True):
compiled_train_prog = program.compile(
config, train_prog, loss_name=train_fetchs["loss"][0].name)
else:
compiled_train_prog = train_prog
if not config.get('use_dali', False):
train_dataloader = Reader(config, 'train', places=place)()
if config.validate and paddle.distributed.get_rank() == 0:
valid_dataloader = Reader(config, 'valid', places=place)()
if use_xpu:
compiled_valid_prog = valid_prog
else:
compiled_valid_prog = program.compile(config, valid_prog)
else:
assert use_gpu is True, "DALI only support gpu, please set use_gpu to True!"
import dali
train_dataloader = dali.train(config)
if config.validate and paddle.distributed.get_rank() == 0:
valid_dataloader = dali.val(config)
compiled_valid_prog = program.compile(config, valid_prog)
vdl_writer = None
if args.vdl_dir:
if version_info.major == 2:
logger.info(
"visualdl is just supported for python3, so it is disabled in python2..."
)
else:
from visualdl import LogWriter
vdl_writer = LogWriter(args.vdl_dir)
for epoch_id in range(config.epochs):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_feeds,
train_fetchs, epoch_id, 'train', config, vdl_writer,
lr_scheduler)
if paddle.distributed.get_rank() == 0:
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
top1_acc = program.run(valid_dataloader, exe,
compiled_valid_prog, valid_feeds,
valid_fetchs, epoch_id, 'valid', config)
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info("{:s}".format(logger.coloring(message, "RED")))
if epoch_id % config.save_interval == 0:
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:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, epoch_id)
def main(args):
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
use_gpu = config.get("use_gpu", True)
places = fluid.cuda_places() if use_gpu else fluid.cpu_places()
# startup_prog is used to do some parameter init work,
# and train prog is used to hold the network
startup_prog = fluid.Program()
train_prog = fluid.Program()
best_top1_acc = 0.0 # best top1 acc record
if not config.get('use_ema'):
train_dataloader, train_fetchs = program.build(config,
train_prog,
startup_prog,
is_train=True,
is_distributed=False)
else:
train_dataloader, train_fetchs, ema = program.build(
config,
train_prog,
startup_prog,
is_train=True,
is_distributed=False)
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs = program.build(config,
valid_prog,
startup_prog,
is_train=False,
is_distributed=False)
# clone to prune some content which is irrelevant in valid_prog
valid_prog = valid_prog.clone(for_test=True)
# create the "Executor" with the statement of which place
exe = fluid.Executor(places[0])
# Parameter initialization
exe.run(startup_prog)
# load model from 1. checkpoint to resume training, 2. pretrained model to finetune
init_model(config, train_prog, exe)
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, places)
compiled_train_prog = program.compile(config, train_prog,
train_fetchs['loss'][0].name)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, places)
compiled_valid_prog = program.compile(config,
valid_prog,
share_prog=compiled_train_prog)
if args.vdl_dir:
from visualdl import LogWriter
vdl_writer = LogWriter(args.vdl_dir)
else:
vdl_writer = None
for epoch_id in range(config.epochs):
# 1. train with train dataset
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
epoch_id, 'train', vdl_writer)
# 2. validate with validate dataset
if config.validate and epoch_id % config.valid_interval == 0:
if config.get('use_ema'):
logger.info(logger.coloring("EMA validate start..."))
with ema.apply(exe):
top1_acc = program.run(valid_dataloader, exe,
compiled_valid_prog, valid_fetchs,
epoch_id, 'valid')
logger.info(logger.coloring("EMA validate over!"))
top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid')
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info("{:s}".format(logger.coloring(message, "RED")))
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path,
"best_model_in_epoch_" + str(epoch_id))
# 3. save the persistable model
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, epoch_id)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
from ppcls.utils import config
from ppcls.engine.engine import Engine
if __name__ == "__main__":
args = config.parse_args()
config = config.get_config(args.config,
overrides=args.override,
show=False)
engine = Engine(config, mode="infer")
engine.infer()
def main(args):
config = get_config(args.config, overrides=args.override, show=True)
# 如果需要量化训练，就必须开启评估
if not config.validate and args.use_quant:
logger.error("=====>Train quant model must use validate!")
sys.exit(1)
if args.use_quant:
config.epochs = config.epochs + 5
gpu_count = get_gpu_count()
if gpu_count != 1:
logger.error(
"=====>`Train quant model must use only one GPU. "
"Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` ."
)
sys.exit(1)
# 设置是否使用 GPU
use_gpu = config.get("use_gpu", True)
places = fluid.cuda_places() if use_gpu else fluid.cpu_places()
startup_prog = fluid.Program()
train_prog = fluid.Program()
best_top1_acc = 0.0
# 获取训练数据和模型输出
if not config.get('use_ema'):
train_dataloader, train_fetchs, out, softmax_out = program.build(
config,
train_prog,
startup_prog,
is_train=True,
is_distributed=False)
else:
train_dataloader, train_fetchs, ema, out, softmax_out = program.build(
config,
train_prog,
startup_prog,
is_train=True,
is_distributed=False)
# 获取评估数据和模型输出
if config.validate:
valid_prog = fluid.Program()
valid_dataloader, valid_fetchs, _, _ = program.build(
config,
valid_prog,
startup_prog,
is_train=False,
is_distributed=False)
# 克隆评估程序，可以去掉与评估无关的计算
valid_prog = valid_prog.clone(for_test=True)
# 创建执行器
exe = fluid.Executor(places[0])
exe.run(startup_prog)
# 加载模型，可以是预训练模型，也可以是检查点
init_model(config, train_prog, exe)
train_reader = Reader(config, 'train')()
train_dataloader.set_sample_list_generator(train_reader, places)
compiled_train_prog = program.compile(config, train_prog,
train_fetchs['loss'][0].name)
if config.validate:
valid_reader = Reader(config, 'valid')()
valid_dataloader.set_sample_list_generator(valid_reader, places)
compiled_valid_prog = program.compile(config,
valid_prog,
share_prog=compiled_train_prog)
vdl_writer = LogWriter(args.vdl_dir)
for epoch_id in range(config.epochs - 5):
# 训练一轮
program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
epoch_id, 'train', config, vdl_writer)
# 执行一次评估
if config.validate and epoch_id % config.valid_interval == 0:
if config.get('use_ema'):
logger.info(logger.coloring("EMA validate start..."))
with ema.apply(exe):
_ = program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid', config)
logger.info(logger.coloring("EMA validate over!"))
top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog,
valid_fetchs, epoch_id, 'valid', config)
if vdl_writer:
logger.scaler('valid_avg', top1_acc, epoch_id, vdl_writer)
if top1_acc > best_top1_acc:
best_top1_acc = top1_acc
message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
best_top1_acc, epoch_id)
logger.info("{:s}".format(logger.coloring(message, "RED")))
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
save_model(train_prog, model_path, "best_model")
# 保存模型
if epoch_id % config.save_interval == 0:
model_path = os.path.join(config.model_save_dir,
config.ARCHITECTURE["name"])
if epoch_id >= 3 and os.path.exists(
os.path.join(model_path, str(epoch_id - 3))):
shutil.rmtree(os.path.join(model_path, str(epoch_id - 3)),
ignore_errors=True)
save_model(train_prog, model_path, epoch_id)
# 量化训练
if args.use_quant and config.validate:
# 执行量化训练
quant_program = slim.quant.quant_aware(train_prog,
exe.place,
for_test=False)
# 评估量化的结果
val_quant_program = slim.quant.quant_aware(valid_prog,
exe.place,
for_test=True)
fetch_list = [f[0] for f in train_fetchs.values()]
metric_list = [f[1] for f in train_fetchs.values()]
for i in range(5):
for idx, batch in enumerate(train_dataloader()):
metrics = exe.run(program=quant_program,
feed=batch,
fetch_list=fetch_list)
for i, m in enumerate(metrics):
metric_list[i].update(np.mean(m), len(batch[0]))
fetchs_str = ''.join([str(m.value) + ' ' for m in metric_list])
if idx % 10 == 0:
logger.info("quant train : " + fetchs_str)
fetch_list = [f[0] for f in valid_fetchs.values()]
metric_list = [f[1] for f in valid_fetchs.values()]
for idx, batch in enumerate(valid_dataloader()):
metrics = exe.run(program=val_quant_program,
feed=batch,
fetch_list=fetch_list)
for i, m in enumerate(metrics):
metric_list[i].update(np.mean(m), len(batch[0]))
fetchs_str = ''.join([str(m.value) + ' ' for m in metric_list])
if idx % 10 == 0:
logger.info("quant valid: " + fetchs_str)
# 保存量化训练模型
float_prog, int8_prog = slim.quant.convert(val_quant_program,
exe.place,
save_int8=True)
fluid.io.save_inference_model(dirname=args.output_path,
feeded_var_names=['feed_image'],
target_vars=[softmax_out],
executor=exe,
main_program=float_prog,
model_filename='__model__',
params_filename='__params__')
