def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
import random
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
if 'log_iter' not in cfg:
cfg.log_iter = 20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if not FLAGS.dist or trainer_id == 0:
print_total_cfg(cfg)
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if 'train_feed' not in cfg:
train_feed = create(main_arch + 'TrainFeed')
else:
train_feed = create(cfg.train_feed)
if FLAGS.eval:
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
train_pyreader, feed_vars = create_feed(train_feed)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
eval_pyreader, feed_vars = create_feed(eval_feed)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
eval_pyreader.decorate_sample_list_generator(eval_reader, place)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_box']
eval_keys, eval_values, eval_cls = parse_fetches(
fetches, eval_prog, extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if FLAGS.eval:
compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog)
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(exe,
train_prog,
cfg.pretrain_weights,
ignore_params=ignore_params)
train_reader = create_reader(train_feed,
(cfg.max_iters - start_iter) * devices_num,
FLAGS.dataset_dir)
train_pyreader.decorate_sample_list_generator(train_reader, place)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
train_pyreader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_smooth_window)
best_box_ap_list = [0.0, 0] #[map, iter]
# use tb-paddle to log data
if FLAGS.use_tb:
from tb_paddle import SummaryWriter
tb_writer = SummaryWriter(FLAGS.tb_log_dir)
tb_loss_step = 0
tb_mAP_step = 0
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use tb-paddle to log loss
if FLAGS.use_tb:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
tb_writer.add_scalar(loss_name, loss_value, tb_loss_step)
tb_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
results = eval_run(exe, compiled_eval_prog, eval_pyreader,
eval_keys, eval_values, eval_cls)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
box_ap_stats = eval_results(results, eval_feed, cfg.metric,
cfg.num_classes, resolution,
is_bbox_normalized,
FLAGS.output_eval, map_type)
# use tb_paddle to log mAP
if FLAGS.use_tb:
tb_writer.add_scalar("mAP", box_ap_stats[0], tb_mAP_step)
tb_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_pyreader.reset()
def main():
role = role_maker.PaddleCloudRoleMaker(is_collective=True) # new line 3
fleet.init(role) # new line 4
env = os.environ
num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 0))
assert num_trainers != 0, "multi-machine training process must be started using distributed.launch..."
trainer_id = int(env.get("PADDLE_TRAINER_ID", 0))
# set different seeds for different trainers
random.seed(trainer_id)
np.random.seed(trainer_id)
if FLAGS.enable_ce:
random.seed(0)
np.random.seed(0)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
save_only = getattr(cfg, 'save_prediction_only', False)
if save_only:
raise NotImplementedError('The config file only support prediction,'
' training stage is not implemented now')
main_arch = cfg.architecture
assert cfg.use_gpu == True, "GPU must be supported for multi-machine training..."
devices_num = fluid.core.get_cuda_device_count()
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
if FLAGS.enable_ce:
startup_prog.random_seed = 1000
train_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
dist_strategy = DistributedStrategy()
sync_bn = getattr(model.backbone, 'norm_type',
None) == 'sync_bn'
dist_strategy.sync_batch_norm = sync_bn
dist_strategy.nccl_comm_num = 1
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 3
exec_strategy.num_iteration_per_drop_scope = 30
dist_strategy.exec_strategy = exec_strategy
dist_strategy.fuse_all_reduce_ops = True
optimizer = fleet.distributed_optimizer(
optimizer, strategy=dist_strategy) # new line 5
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
if 'use_ema' in cfg and cfg['use_ema']:
global_steps = _decay_step_counter()
ema = ExponentialMovingAverage(cfg['ema_decay'],
thres_steps=global_steps)
ema.update()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader, devices_num=1)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(
fetches, eval_prog, extra_keys)
exe.run(startup_prog)
compiled_train_prog = fleet.main_program
if FLAGS.eval:
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(exe,
train_prog,
cfg.pretrain_weights,
ignore_params=ignore_params)
train_reader = create_reader(cfg.TrainReader,
(cfg.max_iters - start_iter) * devices_num,
cfg,
devices_num=devices_num)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
# use VisualDL to log data
if FLAGS.use_vdl:
assert six.PY3, "VisualDL requires Python >= 3.5"
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_loss_step = 0
vdl_mAP_step = 0
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use vdl-paddle to log loss
if FLAGS.use_vdl:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
vdl_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and trainer_id == 0:
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
# NOTE : profiler tools, used for benchmark
if FLAGS.is_profiler and it == 5:
profiler.start_profiler("All")
elif FLAGS.is_profiler and it == 10:
profiler.stop_profiler("total", FLAGS.profiler_path)
return
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and trainer_id == 0:
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.apply_program)
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg,
resolution=resolution)
box_ap_stats = eval_results(results, cfg.metric,
cfg.num_classes, resolution,
is_bbox_normalized,
FLAGS.output_eval, map_type,
cfg['EvalReader']['dataset'])
# use vdl_paddle to log mAP
if FLAGS.use_vdl:
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
vdl_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.restore_program)
train_loader.reset()
def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
import random
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.print_params:
param_delimit_str = '-' * 20 + "All parameters in current graph" + '-' * 20
print(param_delimit_str)
for block in train_prog.blocks:
for param in block.all_parameters():
print("parameter name: {}\tshape: {}".format(param.name,
param.shape))
print('-' * len(param_delimit_str))
return
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
start_iter = 0
if cfg.pretrain_weights:
checkpoint.load_params(exe, train_prog, cfg.pretrain_weights)
pruned_params = FLAGS.pruned_params
assert FLAGS.pruned_params is not None, \
"FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option."
pruned_params = FLAGS.pruned_params.strip().split(",")
logger.info("pruned params: {}".format(pruned_params))
pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(",")]
logger.info("pruned ratios: {}".format(pruned_ratios))
assert len(pruned_params) == len(pruned_ratios), \
"The length of pruned params and pruned ratios should be equal."
assert (pruned_ratios > [0] * len(pruned_ratios) and
pruned_ratios < [1] * len(pruned_ratios)
), "The elements of pruned ratios should be in range (0, 1)."
assert FLAGS.prune_criterion in ['l1_norm', 'geometry_median'], \
"unsupported prune criterion {}".format(FLAGS.prune_criterion)
pruner = Pruner(criterion=FLAGS.prune_criterion)
train_prog = pruner.prune(
train_prog,
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=False)[0]
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if FLAGS.eval:
base_flops = flops(eval_prog)
eval_prog = pruner.prune(
eval_prog,
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=True)[0]
pruned_flops = flops(eval_prog)
logger.info("FLOPs -{}; total FLOPs: {}; pruned FLOPs: {}".format(
float(base_flops - pruned_flops) / base_flops, base_flops,
pruned_flops))
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
train_reader = create_reader(cfg.TrainReader, (cfg.max_iters - start_iter) *
devices_num, cfg)
train_loader.set_sample_list_generator(train_reader, place)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
# use VisualDL to log data
if FLAGS.use_vdl:
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_loss_step = 0
vdl_mAP_step = 0
if FLAGS.eval:
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
# evaluation
results = eval_run(
exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg,
resolution=resolution)
dataset = cfg['EvalReader']['dataset']
box_ap_stats = eval_results(
results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset)
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use VisualDL to log loss
if FLAGS.use_vdl:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
vdl_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(
exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg=cfg,
resolution=resolution)
box_ap_stats = eval_results(
results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset)
# use VisualDL to log mAP
if FLAGS.use_vdl:
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
vdl_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_loader.reset()
def main():
if FLAGS.eval is False:
raise ValueError(
"Currently only supports `--eval==True` while training in `quantization`."
)
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
and 'PADDLE_TRAINERS_NUM' in env \
and int(env['PADDLE_TRAINERS_NUM']) > 1
num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 1))
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
import random
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
if FLAGS.use_pact:
feed_vars['image'].stop_gradient = False
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(
fetches, eval_prog, extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
build_strategy.fuse_all_reduce_ops = False
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
sync_bn = False
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
not_quant_pattern = []
if FLAGS.not_quant_pattern:
not_quant_pattern = FLAGS.not_quant_pattern
config = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
'not_quant_pattern': not_quant_pattern
}
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
if cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(exe,
train_prog,
cfg.pretrain_weights,
ignore_params=ignore_params)
if FLAGS.use_pact:
act_preprocess_func = pact
optimizer_func = get_optimizer
executor = exe
else:
act_preprocess_func = None
optimizer_func = None
executor = None
# insert quantize op in train_prog, return type is CompiledProgram
train_prog_quant = quant_aware(train_prog,
place,
config,
scope=None,
act_preprocess_func=act_preprocess_func,
optimizer_func=optimizer_func,
executor=executor,
for_test=False)
compiled_train_prog = train_prog_quant.with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if FLAGS.eval:
# insert quantize op in eval_prog
eval_prog = quant_aware(eval_prog,
place,
config,
scope=None,
act_preprocess_func=act_preprocess_func,
optimizer_func=optimizer_func,
executor=executor,
for_test=True)
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
start_iter = 0
train_reader = create_reader(cfg.TrainReader,
(cfg.max_iters - start_iter) * devices_num,
cfg,
devices_num=devices_num,
num_trainers=num_trainers)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
if FLAGS.eval:
# evaluation
results = eval_run(exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg=cfg)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
box_ap_stats = eval_results(results, cfg.metric,
cfg.num_classes, resolution,
is_bbox_normalized,
FLAGS.output_eval, map_type,
cfg['EvalReader']['dataset'])
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
save_checkpoint(exe, eval_prog,
os.path.join(save_dir, "best_model"),
train_prog)
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_loader.reset()
def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
import random
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# add NAS
config = ([(cfg.search_space)])
server_address = (cfg.server_ip, cfg.server_port)
load_checkpoint = FLAGS.resume_checkpoint if FLAGS.resume_checkpoint else None
sa_nas = SANAS(
config,
server_addr=server_address,
init_temperature=cfg.init_temperature,
reduce_rate=cfg.reduce_rate,
search_steps=cfg.search_steps,
save_checkpoint=cfg.save_dir,
load_checkpoint=load_checkpoint,
is_server=cfg.is_server)
start_iter = 0
train_reader = create_reader(cfg.TrainReader, (cfg.max_iters - start_iter) *
devices_num, cfg)
eval_reader = create_reader(cfg.EvalReader)
constraint = create('Constraint')
for step in range(cfg.search_steps):
logger.info('----->>> search step: {} <<<------'.format(step))
archs = sa_nas.next_archs()[0]
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale,
FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = archs(feed_vars, 'train', cfg)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
current_constraint = constraint.compute_constraint(train_prog)
logger.info('current steps: {}, constraint {}'.format(
step, current_constraint))
if (constraint.max_constraint != None and
current_constraint > constraint.max_constraint) or (
constraint.min_constraint != None and
current_constraint < constraint.min_constraint):
continue
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = archs(feed_vars, 'eval', cfg)
eval_prog = eval_prog.clone(True)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
build_strategy.fuse_elewise_add_act_ops = True
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy,
startup_prog, train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
compiled_train_prog = fluid.CompiledProgram(
train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if FLAGS.eval:
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
ap = 0
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {
k: np.array(v).mean()
for k, v in zip(train_keys, outs[:-1])
}
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
if (it > 0 and it == cfg.max_iters - 1) and (not FLAGS.dist or
trainer_id == 0):
save_name = str(
it) if it != cfg.max_iters - 1 else "model_final"
checkpoint.save(exe, train_prog,
os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
results = eval_run(exe, compiled_eval_prog, eval_loader,
eval_keys, eval_values, eval_cls)
ap = calculate_ap_py(results)
train_loader.reset()
eval_loader.reset()
logger.info('rewards: ap is {}'.format(ap))
sa_nas.reward(float(ap))
current_best_tokens = sa_nas.current_info()['best_tokens']
logger.info("All steps end, the best BlazeFace-NAS structure is: ")
sa_nas.tokens2arch(current_best_tokens)
def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
and 'PADDLE_TRAINERS_NUM' in env \
and int(env['PADDLE_TRAINERS_NUM']) > 1
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
if FLAGS.enable_ce:
random.seed(0)
np.random.seed(0)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
save_only = getattr(cfg, 'save_prediction_only', False)
if save_only:
raise NotImplementedError('The config file only support prediction,'
' training stage is not implemented now')
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
else:
device_id = 0
place = fluid.CUDAPlace(device_id) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
if FLAGS.enable_ce:
startup_prog.random_seed = 1000
train_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
if 'use_ema' in cfg and cfg['use_ema']:
global_steps = _decay_step_counter()
ema = ExponentialMovingAverage(cfg['ema_decay'],
thres_steps=global_steps)
ema.update()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader, devices_num=1)
eval_loader.set_sample_list_generator(eval_reader, place)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
# 为了在eval取结果时拿到im_size，这里加上了im_size
extra_keys = ['im_info', 'im_id', 'im_shape', 'im_size']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(
fetches, eval_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import get_category_info
eval_dataset = cfg.EvalReader['dataset']
anno_file = eval_dataset.get_anno()
with_background = eval_dataset.with_background
use_default_label = eval_dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if FLAGS.eval:
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(exe,
train_prog,
cfg.pretrain_weights,
ignore_params=ignore_params)
train_reader = create_reader(cfg.TrainReader,
(cfg.max_iters - start_iter) * devices_num,
cfg,
devices_num=devices_num)
train_loader.set_sample_list_generator(train_reader, place)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_smooth_window)
best_box_ap_list = [0.0, 0] #[map, iter]
best_ap_list = [0.0, 0] #[map, iter]
print(
'============================ asasasaaawws ============================'
)
print(clsid2catid)
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use vdl-paddle to log loss
if FLAGS.use_vdl:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
vdl_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
# NOTE : profiler tools, used for benchmark
if FLAGS.is_profiler and it == 5:
profiler.start_profiler("All")
elif FLAGS.is_profiler and it == 10:
profiler.stop_profiler("total", FLAGS.profiler_path)
return
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.apply_program)
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
clear_model(save_dir)
# ========================= eval ===========================
if FLAGS.eval:
if (it > 0 and it % FLAGS.eval_iter == 0
or it == cfg.max_iters - 1):
eval_run(exe, compiled_eval_prog, eval_loader, clsid2catid,
catid2name, eval_keys, eval_values, eval_cls)
box_ap, mask_ap = eval_results(cfg.metric,
cfg['EvalReader']['dataset'])
logger.info("box ap: %.3f, mask ap: %.3f" %
(box_ap[0], mask_ap[0]))
# 以mask_ap作为标准而不是box_ap
ap = mask_ap
if ap[0] > best_ap_list[0]:
best_ap_list[0] = ap[0]
best_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
clear_model(save_dir)
logger.info("Best test ap: {}, in iter: {}".format(
best_ap_list[0], best_ap_list[1]))
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.restore_program)
train_loader.reset()
def main():
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
if 'log_iter' not in cfg:
cfg.log_iter = 20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
if 'train_feed' not in cfg:
train_feed = create(main_arch + 'TrainFeed')
else:
train_feed = create(cfg.train_feed)
if FLAGS.eval:
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
train_pyreader, feed_vars = create_feed(train_feed)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
train_reader = create_reader(train_feed, cfg.max_iters * devices_num,
FLAGS.dataset_dir)
train_pyreader.decorate_sample_list_generator(train_reader, place)
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
eval_pyreader, feed_vars = create_feed(eval_feed)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
eval_pyreader.decorate_sample_list_generator(eval_reader, place)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult']
eval_keys, eval_values, eval_cls = parse_fetches(
fetches, eval_prog, extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = True
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
# only enable sync_bn in multi GPU devices
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
train_compile_program = fluid.compiler.CompiledProgram(
train_prog).with_data_parallel(loss_name=loss.name,
build_strategy=build_strategy)
if FLAGS.eval:
eval_compile_program = fluid.compiler.CompiledProgram(eval_prog)
exe.run(startup_prog)
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_pretrain(exe, train_prog, cfg.pretrain_weights)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
train_pyreader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(train_compile_program, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0:
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
if it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1:
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
results = eval_run(exe, eval_compile_program, eval_pyreader,
eval_keys, eval_values, eval_cls)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
box_ap_stats = eval_results(results, eval_feed, cfg.metric,
cfg.num_classes, resolution,
is_bbox_normalized,
FLAGS.output_eval)
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_pyreader.reset()
def main():
env = os.environ
FLAGS.dist = 'PADDLE_TRAINER_ID' in env \
and 'PADDLE_TRAINERS_NUM' in env \
and int(env['PADDLE_TRAINERS_NUM']) > 1
num_trainers = int(env.get('PADDLE_TRAINERS_NUM', 1))
if FLAGS.dist:
trainer_id = int(env['PADDLE_TRAINER_ID'])
local_seed = (99 + trainer_id)
random.seed(local_seed)
np.random.seed(local_seed)
if FLAGS.enable_ce:
random.seed(0)
np.random.seed(0)
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# disable npu in config by default and check use_npu
if 'use_npu' not in cfg:
cfg.use_npu = False
check_npu(cfg.use_npu)
use_xpu = False
if hasattr(cfg, 'use_xpu'):
check_xpu(cfg.use_xpu)
use_xpu = cfg.use_xpu
# check if paddlepaddle version is satisfied
check_version()
assert not (use_xpu and cfg.use_gpu), \
'Can not run on both XPU and GPU'
assert not (cfg.use_npu and cfg.use_gpu), \
'Can not run on both NPU and GPU'
save_only = getattr(cfg, 'save_prediction_only', False)
if save_only:
raise NotImplementedError('The config file only support prediction,'
' training stage is not implemented now')
main_arch = cfg.architecture
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
elif cfg.use_npu:
devices_num = fluid.core.get_npu_device_count()
elif use_xpu:
# ToDo(qingshu): XPU only support single card now
devices_num = 1
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
if cfg.use_gpu and 'FLAGS_selected_gpus' in env:
device_id = int(env['FLAGS_selected_gpus'])
elif cfg.use_npu and 'FLAGS_selected_npus' in env:
device_id = int(env['FLAGS_selected_npus'])
elif use_xpu and 'FLAGS_selected_xpus' in env:
device_id = int(env['FLAGS_selected_xpus'])
else:
device_id = 0
if cfg.use_gpu:
place = fluid.CUDAPlace(device_id)
elif cfg.use_npu:
place = fluid.NPUPlace(device_id)
elif use_xpu:
place = fluid.XPUPlace(device_id)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
if FLAGS.enable_ce:
startup_prog.random_seed = 1000
train_prog.random_seed = 1000
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
if FLAGS.fp16:
assert (getattr(model.backbone, 'norm_type', None)
!= 'affine_channel'), \
'--fp16 currently does not support affine channel, ' \
' please modify backbone settings to use batch norm'
with mixed_precision_context(FLAGS.loss_scale, FLAGS.fp16) as ctx:
inputs_def = cfg['TrainReader']['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
if FLAGS.fp16:
loss *= ctx.get_loss_scale_var()
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
if FLAGS.fp16:
loss /= ctx.get_loss_scale_var()
if 'use_ema' in cfg and cfg['use_ema']:
global_steps = _decay_step_counter()
ema = ExponentialMovingAverage(
cfg['ema_decay'], thres_steps=global_steps)
ema.update()
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(cfg.EvalReader, devices_num=1)
# When iterable mode, set set_sample_list_generator(eval_reader, place)
eval_loader.set_sample_list_generator(eval_reader)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
if cfg.metric == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape', 'gt_bbox']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# compile program for multi-devices
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_optimizer_ops = False
# only enable sync_bn in multi GPU devices
sync_bn = getattr(model.backbone, 'norm_type', None) == 'sync_bn'
build_strategy.sync_batch_norm = sync_bn and devices_num > 1 \
and cfg.use_gpu
exec_strategy = fluid.ExecutionStrategy()
# iteration number when CompiledProgram tries to drop local execution scopes.
# Set it to be 1 to save memory usages, so that unused variables in
# local execution scopes can be deleted after each iteration.
exec_strategy.num_iteration_per_drop_scope = 1
if FLAGS.dist:
dist_utils.prepare_for_multi_process(exe, build_strategy, startup_prog,
train_prog)
exec_strategy.num_threads = 1
exe.run(startup_prog)
compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
if use_xpu or cfg.use_npu:
compiled_train_prog = train_prog
if FLAGS.eval:
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
if use_xpu or cfg.use_npu:
compiled_eval_prog = eval_prog
fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel'
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step()
elif cfg.pretrain_weights and fuse_bn and not ignore_params:
checkpoint.load_and_fusebn(exe, train_prog, cfg.pretrain_weights)
elif cfg.pretrain_weights:
checkpoint.load_params(
exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
train_reader = create_reader(
cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num,
cfg,
devices_num=devices_num,
num_trainers=num_trainers)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
# whether output bbox is normalized in model output layer
is_bbox_normalized = False
if hasattr(model, 'is_bbox_normalized') and \
callable(model.is_bbox_normalized):
is_bbox_normalized = model.is_bbox_normalized()
# if map_type not set, use default 11point, only use in VOC eval
map_type = cfg.map_type if 'map_type' in cfg else '11point'
train_stats = TrainingStats(cfg.log_iter, train_keys)
train_loader.start()
start_time = time.time()
end_time = time.time()
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
time_stat = deque(maxlen=cfg.log_iter)
best_box_ap_list = [0.0, 0] #[map, iter]
# use VisualDL to log data
if FLAGS.use_vdl:
assert six.PY3, "VisualDL requires Python >= 3.5"
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_loss_step = 0
vdl_mAP_step = 0
for it in range(start_iter, cfg.max_iters):
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
outs = exe.run(compiled_train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# use vdl-paddle to log loss
if FLAGS.use_vdl:
if it % cfg.log_iter == 0:
for loss_name, loss_value in stats.items():
vdl_writer.add_scalar(loss_name, loss_value, vdl_loss_step)
vdl_loss_step += 1
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0 and (not FLAGS.dist or trainer_id == 0):
ips = float(cfg['TrainReader']['batch_size']) / time_cost
strs = 'iter: {}, lr: {:.6f}, {}, eta: {}, batch_cost: {:.5f} sec, ips: {:.5f} images/sec'.format(
it, np.mean(outs[-1]), logs, eta, time_cost, ips)
logger.info(strs)
# NOTE : profiler tools, used for benchmark
if FLAGS.is_profiler and it == 5:
profiler.start_profiler("All")
elif FLAGS.is_profiler and it == 10:
profiler.stop_profiler("total", FLAGS.profiler_path)
return
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1) \
and (not FLAGS.dist or trainer_id == 0):
save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.apply_program)
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
if FLAGS.eval:
# evaluation
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(
exe,
compiled_eval_prog,
eval_loader,
eval_keys,
eval_values,
eval_cls,
cfg,
resolution=resolution)
box_ap_stats = eval_results(
results, cfg.metric, cfg.num_classes, resolution,
is_bbox_normalized, FLAGS.output_eval, map_type,
cfg['EvalReader']['dataset'])
# use vdl_paddle to log mAP
if FLAGS.use_vdl:
vdl_writer.add_scalar("mAP", box_ap_stats[0], vdl_mAP_step)
vdl_mAP_step += 1
if box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog,
os.path.join(save_dir, "best_model"))
logger.info("Best test box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
if 'use_ema' in cfg and cfg['use_ema']:
exe.run(ema.restore_program)
train_loader.reset()
def main():
# 配置
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
check_gpu(cfg.use_gpu)
check_version()
# 执行器
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# 模型
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg.TrainReader['inputs_def']
feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
if FLAGS.eval:
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg.EvalReader['inputs_def']
feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
eval_keys, eval_values, _ = parse_fetches(fetches, eval_prog, extra_keys)
eval_reader = create_reader(cfg.EvalReader)
eval_loader.set_sample_list_generator(eval_reader, place)
##### 运行 ####
exe.run(startup_prog)
## 恢复与迁移
ignore_params = cfg.finetune_exclude_pretrained_params \
if 'finetune_exclude_pretrained_params' in cfg else []
start_iter = 0
if FLAGS.resume_checkpoint:
checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint)
start_iter = checkpoint.global_step() + 1
elif cfg.pretrain_weights:
checkpoint.load_params(
exe, train_prog, cfg.pretrain_weights, ignore_params=ignore_params)
## 数据迭代器
train_reader = create_reader(cfg.TrainReader, cfg.max_iters - start_iter, cfg)
train_loader.set_sample_list_generator(train_reader, place)
## 训练循环
train_loader.start()
# 过程跟踪
train_stats = TrainingStats(cfg.log_smooth_window, train_keys)
start_time = time.time()
end_time = time.time()
time_stat = deque(maxlen=cfg.log_smooth_window)
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
best_box_ap_list = [0.0, 0]
if FLAGS.use_vdl:
log_writter = LogWriter(FLAGS.vdl_log_dir, sync_cycle=5)
with log_writter.mode("train") as vdl_logger:
train_scalar_loss = vdl_logger.scalar(tag="loss")
with log_writter.mode("val") as vdl_logger:
val_scalar_map = vdl_logger.scalar(tag="map")
for it in range(start_iter, cfg.max_iters):
# 运行程序
outs = exe.run(train_prog, fetch_list=train_values)
stats = {k: np.array(v).mean() for k, v in zip(train_keys, outs[:-1])}
# 日志与可视化窗口
start_time = end_time
end_time = time.time()
time_stat.append(end_time - start_time)
time_cost = np.mean(time_stat)
eta_sec = (cfg.max_iters - it) * time_cost
eta = str(datetime.timedelta(seconds=int(eta_sec)))
train_stats.update(stats)
logs = train_stats.log()
if it % cfg.log_iter == 0:
# log
strs = 'iter: {}, lr: {:.6f}, {}, time: {:.3f}, eta: {}'.format(
it, np.mean(outs[-1]), logs, time_cost, eta)
logger.info(strs)
# vdl
if FLAGS.use_vdl:
train_scalar_loss.add_record(it//cfg.log_iter, stats['loss'])
# 模型保存与评价窗口
if (it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1):
# 模型保存
save_name = str(it) if it != cfg.max_iters - 1 else "final"
checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
## 模型评价
if FLAGS.eval:
current_step = it//cfg.snapshot_iter if it % cfg.snapshot_iter == 0 \
else it//cfg.snapshot_iter+1
## 训练集评价
## 验证集评价
results = eval_run(exe, eval_prog, eval_loader,
eval_keys, eval_values)
box_ap_stats = eval_results(results, cfg.num_classes)
logger.info("eval box op: {}, in iter: {}".format(
box_ap_stats, it))
if FLAGS.use_vdl:
val_scalar_map.add_record(current_step, box_ap_stats)
## 保存最佳模型
if box_ap_stats > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats
best_box_ap_list[1] = it
checkpoint.save(exe, train_prog, os.path.join(save_dir, "best_model"))
# 日志
logger.info("Best eval box ap: {}, in iter: {}".format(
best_box_ap_list[0], best_box_ap_list[1]))
train_loader.reset()
