def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['fp16'] = FLAGS.fp16
cfg['fleet'] = FLAGS.fleet
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
cfg['save_prediction_only'] = FLAGS.save_prediction_only
cfg['save_proposals'] = FLAGS.save_proposals
cfg['proposals_path'] = FLAGS.proposals_path
merge_config(FLAGS.opt)
place = paddle.set_device('gpu' if cfg.use_gpu else 'cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config)
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_version()
run(FLAGS, cfg)
def init_model(self):
parser = ArgsParser()
parser.add_argument("-c",
"--config",
type=str,
default=self.cfg_path,
help="configuration file to use")
parser.add_argument(
"--draw_threshold",
type=float,
default=self.conf_thres,
help="Threshold to reserve the result for visualization.")
args = parser.parse_args()
cfg = load_config(args.config)
cfg.weights = self.weights
cfg.use_gpu = self.use_gpu
paddle.set_device('gpu' if cfg.use_gpu else 'cpu')
if 'norm_type' in cfg and cfg[
'norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
check_config(cfg)
check_gpu(cfg.use_gpu)
check_version()
""" build trainer """
trainer = Trainer(cfg, mode='test')
""" load weights """
trainer.load_weights(cfg.weights)
return trainer
def main():
"""
Main evaluate function
"""
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)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, feed_vars = create_feed(eval_feed, iterable=True)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, eval_prog, cfg.weights)
assert cfg.metric in ['WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
annotation_file = getattr(eval_feed.dataset, 'annotation', None)
dataset_dir = FLAGS.dataset_dir if FLAGS.dataset_dir else \
getattr(eval_feed.dataset, 'dataset_dir', None)
img_root_dir = dataset_dir
if FLAGS.eval_mode == "widerface":
image_dir = getattr(eval_feed.dataset, 'image_dir', None)
img_root_dir = os.path.join(dataset_dir, image_dir)
gt_file = os.path.join(dataset_dir, annotation_file)
pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
face_eval_run(exe,
eval_prog,
fetches,
img_root_dir,
gt_file,
pred_dir=pred_dir,
eval_mode=FLAGS.eval_mode,
multi_scale=FLAGS.multi_scale)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
# disable xpu in config by default
if 'use_xpu' not in cfg:
cfg.use_xpu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
elif cfg.use_xpu:
place = paddle.set_device('xpu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_xpu(cfg.use_xpu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
# TODO: bias should be unified
cfg['bias'] = 1 if FLAGS.bias else 0
cfg['classwise'] = True if FLAGS.classwise else False
cfg['output_eval'] = FLAGS.output_eval
cfg['save_prediction_only'] = FLAGS.save_prediction_only
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='eval')
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
check_config(cfg)
check_gpu(cfg.use_gpu)
check_npu(cfg.use_npu)
check_version()
run(FLAGS, cfg)
def main():
"""
Main evaluate function
"""
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)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['use_dataloader'] = False
feed_vars, _ = model.build_inputs(**inputs_def)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, eval_prog, cfg.weights)
assert cfg.metric in ['WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
dataset = cfg['EvalReader']['dataset']
annotation_file = dataset.get_anno()
dataset_dir = dataset.dataset_dir
image_dir = os.path.join(
dataset_dir,
dataset.image_dir) if FLAGS.eval_mode == 'widerface' else dataset_dir
pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
face_eval_run(exe,
eval_prog,
fetches,
image_dir,
annotation_file,
pred_dir=pred_dir,
eval_mode=FLAGS.eval_mode,
multi_scale=FLAGS.multi_scale)
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 '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)
_, test_feed_vars = create_feed(eval_feed, False)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
#eval_pyreader.decorate_sample_list_generator(eval_reader, place)
test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
assert os.path.exists(FLAGS.model_path)
infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
dirname=FLAGS.model_path,
executor=exe,
model_filename=FLAGS.model_name,
params_filename=FLAGS.params_name)
eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
eval_values = [
'multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult'
]
eval_cls = []
eval_values[0] = fetch_targets[0]
results = eval_run(exe, infer_prog, eval_reader, eval_keys, eval_values,
eval_cls, test_data_feed)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
False, FLAGS.output_eval)
def main():
paddle.set_device("cpu")
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
check_gpu(cfg.use_gpu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
check_gpu(cfg.use_gpu)
check_version()
place = 'gpu:{}'.format(ParallelEnv().dev_id) if cfg.use_gpu else 'cpu'
place = paddle.set_device(place)
run(FLAGS, cfg)
def main():
## 配置
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)
check_gpu(cfg.use_gpu)
check_version()
# json评价模式
if FLAGS.json_file:
logger.info("start evalute in json mode")
dataset = cfg.EvalReader['dataset']
if FLAGS.dataset == 'train':
dataset = cfg.TrainReader['dataset']
eval_json_results(FLAGS.json_file,
dataset=dataset, num_classes=cfg.num_classes)
return
## 模型
model = create(main_arch) ####
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg.EvalReader['inputs_def']
feed_vars, 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']
keys, values, _ = parse_fetches(fetches, eval_prog, extra_keys)
## 执行器
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
## 数据
reader = create_reader(cfg.EvalReader) ####
loader.set_sample_list_generator(reader, place)
#### 运行 ####
exe.run(startup_prog)
## 加载参数
assert 'weights' in cfg, \
'model can not load weights'
checkpoint.load_params(exe, eval_prog, cfg.weights)
## 评价
results = eval_run(exe, eval_prog, loader, keys, values)
eval_results(results, cfg.num_classes)
def main():
paddle.set_device("cpu")
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
# TODO: to be refined in the future
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn':
FLAGS.opt['norm_type'] = 'bn'
merge_config(FLAGS.opt)
check_config(cfg)
check_gpu(cfg.use_gpu)
check_version()
run(FLAGS, cfg)
def init_cfg(self):
self.cfg = load_config(self.config_file)
if 'architecture' in self.cfg:
self.main_arch = self.cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
self.cfg.use_gpu = self.use_gpu
check_gpu(self.use_gpu)
self.extra_keys = []
if self.cfg['metric'] == 'COCO':
self.extra_keys = ['im_info', 'im_id', 'im_shape']
if self.cfg['metric'] == 'VOC':
self.extra_keys = ['im_id', 'im_shape']
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
if FLAGS.slim_config:
slim_cfg = load_config(FLAGS.slim_config)
merge_config(slim_cfg)
if 'weight_type' not in cfg:
cfg.weight_type = FLAGS.weight_type
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_version()
place = 'gpu:{}'.format(ParallelEnv().dev_id) if cfg.use_gpu else 'cpu'
place = paddle.set_device(place)
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
# TODO: to be refined in the future
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn':
FLAGS.opt['norm_type'] = 'bn'
merge_config(FLAGS.opt)
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check_config(cfg)
check_gpu(cfg.use_gpu)
check_version()
run(FLAGS, cfg)
def main():
FLAGS = parse_args()
cfg = load_config(FLAGS.config)
cfg['amp'] = FLAGS.amp
cfg['fleet'] = FLAGS.fleet
cfg['use_vdl'] = FLAGS.use_vdl
cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
cfg['save_prediction_only'] = FLAGS.save_prediction_only
cfg['profiler_options'] = FLAGS.profiler_options
cfg['save_proposals'] = FLAGS.save_proposals
cfg['proposals_path'] = FLAGS.proposals_path
merge_config(FLAGS.opt)
# disable npu in config by default
if 'use_npu' not in cfg:
cfg.use_npu = False
# disable xpu in config by default
if 'use_xpu' not in cfg:
cfg.use_xpu = False
if cfg.use_gpu:
place = paddle.set_device('gpu')
elif cfg.use_npu:
place = paddle.set_device('npu')
elif cfg.use_xpu:
place = paddle.set_device('xpu')
else:
place = paddle.set_device('cpu')
if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn' and not cfg.use_gpu:
cfg['norm_type'] = 'bn'
if FLAGS.slim_config:
cfg = build_slim_model(cfg, FLAGS.slim_config)
# FIXME: Temporarily solve the priority problem of FLAGS.opt
merge_config(FLAGS.opt)
check.check_config(cfg)
check.check_gpu(cfg.use_gpu)
check.check_npu(cfg.use_npu)
check.check_version()
run(FLAGS, cfg)
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)
# check if paddlepaddle version is satisfied
check_version()
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)
eval_loader.set_sample_list_generator(eval_reader, place)
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.compiler.CompiledProgram(eval_prog)
train_loader.set_sample_list_generator(train_reader, place)
train_stats = TrainingStats(cfg.log_smooth_window, 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_smooth_window)
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
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':
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 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]
# 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
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))
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():
"""
Main evaluate function
"""
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
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, loader = model.build_inputs(**inputs_def)
if multi_scale_test is None:
fetches = model.eval(feed_vars)
else:
fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True)
reader = create_reader(cfg.EvalReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
dataset = cfg['EvalReader']['dataset']
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(cfg.metric,
json_directory=FLAGS.output_eval,
dataset=dataset)
return
compile_program = fluid.compiler.CompiledProgram(
eval_prog).with_data_parallel()
assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported."
if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric))
assert cfg.metric in ['COCO', 'VOC', 'traffic'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
elif cfg.metric == 'VOC':
extra_keys = ['gt_bbox', 'gt_class', 'is_difficult']
else:
extra_keys = ['gt_bbox', 'gt_class', 'im_id']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
sub_eval_prog = None
sub_keys = None
sub_values = None
# build sub-program
if 'Mask' in main_arch and multi_scale_test:
sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['mask_branch'] = True
feed_vars, eval_loader = model.build_inputs(**inputs_def)
sub_fetches = model.eval(feed_vars,
multi_scale_test,
mask_branch=True)
assert cfg.metric == 'COCO'
extra_keys = ['im_id', 'im_shape']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys)
sub_eval_prog = sub_eval_prog.clone(True)
#if 'weights' in cfg:
# checkpoint.load_params(exe, sub_eval_prog, cfg.weights)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, startup_prog, cfg.weights)
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
sub_eval_prog, sub_keys, sub_values, resolution)
save_only = getattr(cfg, 'save_prediction_only', False)
if cfg.metric == 'traffic':
from ppdet.utils.traffic_eval import get_category_info, bbox2out, write_output
with_background = dataset.with_background
dataset_dir = dataset.dataset_dir
im_info_file = os.path.join(dataset_dir, 'data_info.txt')
clsid2catid, catid2name = get_category_info(
with_background=with_background)
xywh_results = bbox2out(results,
clsid2catid,
is_bbox_normalized=is_bbox_normalized)
if save_only:
write_output(xywh_results,
im_info_file,
catid2name,
dataset.get_anno(),
threshold=0.63,
outpath='output/detect')
return
# evaluation
# 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'
eval_results(results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset,
save_only=save_only)
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 = False
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():
"""
Main evaluate function
"""
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)
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'
main_arch = cfg.architecture
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor
if cfg.use_gpu:
place = fluid.CUDAPlace(0)
elif use_xpu:
place = fluid.XPUPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, loader = model.build_inputs(**inputs_def)
if multi_scale_test is None:
fetches = model.eval(feed_vars)
else:
fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True)
reader = create_reader(cfg.EvalReader, devices_num=1)
# When iterable mode, set set_sample_list_generator(reader, place)
loader.set_sample_list_generator(reader)
dataset = cfg['EvalReader']['dataset']
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(
cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset)
return
compile_program = fluid.CompiledProgram(eval_prog).with_data_parallel()
if use_xpu:
compile_program = eval_prog
assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported."
if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric))
assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric)
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']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
sub_eval_prog = None
sub_keys = None
sub_values = None
# build sub-program
if 'Mask' in main_arch and multi_scale_test:
sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['mask_branch'] = True
feed_vars, eval_loader = model.build_inputs(**inputs_def)
sub_fetches = model.eval(
feed_vars, multi_scale_test, mask_branch=True)
assert cfg.metric == 'COCO'
extra_keys = ['im_id', 'im_shape']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys)
sub_eval_prog = sub_eval_prog.clone(True)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, startup_prog, cfg.weights)
resolution = None
if 'Mask' in cfg.architecture or cfg.architecture == 'HybridTaskCascade':
resolution = model.mask_head.resolution
results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
sub_eval_prog, sub_keys, sub_values, resolution)
# evaluation
# 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'
save_only = getattr(cfg, 'save_prediction_only', False)
eval_results(
results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset,
save_only=save_only)
def main():
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
dataset = cfg.TestReader['dataset']
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
model = create(main_arch)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['iterable'] = True
feed_vars, loader = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
reader = create_reader(cfg.TestReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_params(exe, infer_prog, cfg.weights)
# parse infer fetches
assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg['metric'] in ['COCO', 'OID']:
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape']
keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import bbox2out, mask2out, segm2out, get_category_info
if cfg.metric == 'OID':
from ppdet.utils.oid_eval import bbox2out, get_category_info
if cfg.metric == "VOC":
from ppdet.utils.voc_eval import bbox2out, get_category_info
if cfg.metric == "WIDERFACE":
from ppdet.utils.widerface_eval_utils import bbox2out, lmk2out, get_category_info
anno_file = dataset.get_anno()
with_background = dataset.with_background
use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# 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()
# use VisualDL to log image
if FLAGS.use_vdl:
assert six.PY3, "VisualDL requires Python >= 3.5"
from visualdl import LogWriter
vdl_writer = LogWriter(FLAGS.vdl_log_dir)
vdl_image_step = 0
vdl_image_frame = 0 # each frame can display ten pictures at most.
imid2path = dataset.get_imid2path()
resultBBox = []
for iter_id, data in enumerate(loader()):
outs = exe.run(infer_prog,
feed=data,
fetch_list=values,
return_numpy=False)
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
logger.info('Infer iter {}'.format(iter_id))
if 'TTFNet' in cfg.architecture:
res['bbox'][1].append([len(res['bbox'][0])])
if 'CornerNet' in cfg.architecture:
from ppdet.utils.post_process import corner_post_process
post_config = getattr(cfg, 'PostProcess', None)
corner_post_process(res, post_config, cfg.num_classes)
bbox_results = None
mask_results = None
segm_results = None
lmk_results = None
if 'bbox' in res:
bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
if 'mask' in res:
mask_results = mask2out([res], clsid2catid,
model.mask_head.resolution)
if 'segm' in res:
segm_results = segm2out([res], clsid2catid)
if 'landmark' in res:
lmk_results = lmk2out([res], is_bbox_normalized)
# bbox 四个值：左上角坐标 + 宽度 + 高度
# {'image_id': 0, 'category_id': 0, 'bbox': [695.04443359375, 723.8153686523438, 128.288818359375, 61.5987548828125], 'score': 0.9990022778511047}
im_ids = res['im_id'][0]
image_path = imid2path[int(im_ids[0])]
prefix = image_path.split('/')[-1]
imageName = prefix.split('.')[0]
for i, result in enumerate(bbox_results):
score = result["score"]
bbox = result["bbox"]
x1 = str(int(bbox[0]))
y1 = str(int(bbox[1]))
x2 = str(int(bbox[2] + bbox[0]))
y2 = str(int(bbox[3] + bbox[1]))
if (score > 0.01):
resStr = imageName + ' ' + str(round(
score,
3)) + ' ' + x1 + ' ' + y1 + ' ' + x2 + ' ' + y2 + '\n'
resultBBox.append(resStr)
# visualize result
for im_id in im_ids:
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
image = ImageOps.exif_transpose(image)
# use VisualDL to log original image
if FLAGS.use_vdl:
original_image_np = np.array(image)
vdl_writer.add_image(
"original/frame_{}".format(vdl_image_frame),
original_image_np, vdl_image_step)
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results, segm_results, lmk_results)
# use VisualDL to log image with bbox
if FLAGS.use_vdl:
infer_image_np = np.array(image)
vdl_writer.add_image("bbox/frame_{}".format(vdl_image_frame),
infer_image_np, vdl_image_step)
vdl_image_step += 1
if vdl_image_step % 10 == 0:
vdl_image_step = 0
vdl_image_frame += 1
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info("Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
resulttxtPath = "/home/aistudio/work/PaddleDetection-release-2.0-beta/output/test_result.txt"
f = open(resulttxtPath, 'w+', encoding='utf-8')
for i, p in enumerate(resultBBox):
f.write(p)
f.close()
def main():
"""
Main evaluate function
"""
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)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
# check if paddlepaddle version is satisfied
check_version()
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
loader, feed_vars = create_feed(eval_feed)
if multi_scale_test is None:
fetches = model.eval(feed_vars)
else:
fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True)
reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
loader.set_sample_list_generator(reader, place)
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(eval_feed,
cfg.metric,
json_directory=FLAGS.output_eval)
return
compile_program = fluid.compiler.CompiledProgram(
eval_prog).with_data_parallel()
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, eval_prog, cfg.weights)
assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported."
if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric))
assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric)
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']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
sub_eval_prog = None
sub_keys = None
sub_values = None
# build sub-program
if 'Mask' in main_arch and multi_scale_test:
sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard():
_, feed_vars = create_feed(eval_feed,
False,
sub_prog_feed=True)
sub_fetches = model.eval(feed_vars,
multi_scale_test,
mask_branch=True)
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys)
sub_eval_prog = sub_eval_prog.clone(True)
if 'weights' in cfg:
checkpoint.load_params(exe, sub_eval_prog, cfg.weights)
results = eval_run(exe, compile_program, loader, keys, values, cls, cfg,
sub_eval_prog, sub_keys, sub_values)
# evaluation
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
# 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'
eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
is_bbox_normalized, FLAGS.output_eval, map_type)
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 '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)
_, feed_vars = create_feed(train_feed, True)
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, FLAGS.dataset_dir)
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_keys.append("lr")
train_values.append(lr.name)
train_fetch_list = []
for k, v in zip(train_keys, train_values):
train_fetch_list.append((k, v))
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
_, test_feed_vars = create_feed(eval_feed, True)
fetches = model.eval(test_feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
test_data_feed = fluid.DataFeeder(test_feed_vars.values(), 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)
eval_fetch_list = []
for k, v in zip(eval_keys, eval_values):
eval_fetch_list.append((k, v))
exe.run(startup_prog)
checkpoint.load_params(exe, train_prog, cfg.pretrain_weights)
best_box_ap_list = []
def eval_func(program, scope):
#place = fluid.CPUPlace()
#exe = fluid.Executor(place)
results = eval_run(exe, program, eval_reader, eval_keys, eval_values,
eval_cls, test_data_feed)
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, False,
FLAGS.output_eval)
if len(best_box_ap_list) == 0:
best_box_ap_list.append(box_ap_stats[0])
elif box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
logger.info("Best test box ap: {}".format(best_box_ap_list[0]))
return best_box_ap_list[0]
test_feed = [('image', test_feed_vars['image'].name),
('im_size', test_feed_vars['im_size'].name)]
com = Compressor(
place,
fluid.global_scope(),
train_prog,
train_reader=train_reader,
train_feed_list=[(key, value.name) for key, value in feed_vars.items()],
train_fetch_list=train_fetch_list,
eval_program=eval_prog,
eval_reader=eval_reader,
eval_feed_list=test_feed,
eval_func={'map': eval_func},
eval_fetch_list=[eval_fetch_list[0]],
save_eval_model=True,
prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]],
train_optimizer=None)
com.config(FLAGS.slim_file)
com.run()
def main():
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
dataset = cfg.TestReader['dataset']
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
model = create(main_arch)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['iterable'] = True
feed_vars, loader = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
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)."
base_flops = flops(infer_prog)
pruner = Pruner()
infer_prog, _, _ = pruner.prune(infer_prog,
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=True)
pruned_flops = flops(infer_prog)
logger.info("pruned FLOPS: {}".format(
float(base_flops - pruned_flops) / base_flops))
reader = create_reader(cfg.TestReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_checkpoint(exe, infer_prog, cfg.weights)
# parse infer fetches
assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg['metric'] in ['COCO', 'OID']:
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape']
keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
if cfg.metric == 'OID':
from ppdet.utils.oid_eval import bbox2out, get_category_info
if cfg.metric == "VOC":
from ppdet.utils.voc_eval import bbox2out, get_category_info
if cfg.metric == "WIDERFACE":
from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
anno_file = dataset.get_anno()
with_background = dataset.with_background
use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# 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()
imid2path = dataset.get_imid2path()
for iter_id, data in enumerate(loader()):
outs = exe.run(infer_prog,
feed=data,
fetch_list=values,
return_numpy=False)
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
logger.info('Infer iter {}'.format(iter_id))
bbox_results = None
mask_results = None
if 'bbox' in res:
bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
if 'mask' in res:
mask_results = mask2out([res], clsid2catid,
model.mask_head.resolution)
# visualize result
im_ids = res['im_id'][0]
for im_id in im_ids:
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info("Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
def main():
env = os.environ
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_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)
# build program
model = create(main_arch)
inputs_def = cfg['TrainReader']['inputs_def']
train_feed_vars, train_loader = model.build_inputs(**inputs_def)
train_fetches = model.train(train_feed_vars)
loss = train_fetches['loss']
start_iter = 0
train_reader = create_reader(cfg.TrainReader,
(cfg.max_iters - start_iter) * devices_num,
cfg)
# When iterable mode, set set_sample_list_generator(train_reader, place)
train_loader.set_sample_list_generator(train_reader)
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, fluid.default_startup_program()):
with fluid.unique_name.guard():
model = create(main_arch)
inputs_def = cfg['EvalReader']['inputs_def']
test_feed_vars, eval_loader = model.build_inputs(**inputs_def)
fetches = model.eval(test_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)
teacher_cfg = load_config(FLAGS.teacher_config)
merge_config(FLAGS.opt)
teacher_arch = teacher_cfg.architecture
teacher_program = fluid.Program()
teacher_startup_program = fluid.Program()
with fluid.program_guard(teacher_program, teacher_startup_program):
with fluid.unique_name.guard():
teacher_feed_vars = OrderedDict()
for name, var in train_feed_vars.items():
teacher_feed_vars[name] = teacher_program.global_block(
)._clone_variable(var, force_persistable=False)
model = create(teacher_arch)
train_fetches = model.train(teacher_feed_vars)
teacher_loss = train_fetches['loss']
exe.run(teacher_startup_program)
assert FLAGS.teacher_pretrained, "teacher_pretrained should be set"
checkpoint.load_params(exe, teacher_program, FLAGS.teacher_pretrained)
teacher_program = teacher_program.clone(for_test=True)
target_number = len(model.yolo_head.anchor_masks)
data_name_map = {
'image': 'image',
'gt_bbox': 'gt_bbox',
'gt_class': 'gt_class',
'gt_score': 'gt_score'
}
for i in range(target_number):
data_name_map['target{}'.format(i)] = 'target{}'.format(i)
merge(teacher_program, fluid.default_main_program(), data_name_map, place)
output_names = [
[
'strided_slice_0.tmp_0', 'strided_slice_1.tmp_0',
'strided_slice_2.tmp_0', 'strided_slice_3.tmp_0',
'strided_slice_4.tmp_0', 'transpose_0.tmp_0'
],
[
'strided_slice_5.tmp_0', 'strided_slice_6.tmp_0',
'strided_slice_7.tmp_0', 'strided_slice_8.tmp_0',
'strided_slice_9.tmp_0', 'transpose_2.tmp_0'
],
[
'strided_slice_10.tmp_0', 'strided_slice_11.tmp_0',
'strided_slice_12.tmp_0', 'strided_slice_13.tmp_0',
'strided_slice_14.tmp_0', 'transpose_4.tmp_0'
],
]
yolo_output_names = []
for i in range(target_number):
yolo_output_names.extend(output_names[i])
assert cfg.use_fine_grained_loss, \
"Only support use_fine_grained_loss=True, Please set it in config file or '-o use_fine_grained_loss=true'"
distill_loss = split_distill(yolo_output_names, 1000, target_number)
loss = distill_loss + loss
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
lr = lr_builder()
opt = optim_builder(lr)
opt.minimize(loss)
exe.run(fluid.default_startup_program())
checkpoint.load_params(exe, fluid.default_main_program(),
cfg.pretrain_weights)
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)
distill_prog = pruner.prune(fluid.default_main_program(),
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=False)[0]
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))
build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_reduce_ops = False
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
parallel_main = fluid.CompiledProgram(distill_prog).with_data_parallel(
loss_name=loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
compiled_eval_prog = fluid.CompiledProgram(eval_prog)
# 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']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# 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()
map_type = cfg.map_type if 'map_type' in cfg else '11point'
best_box_ap_list = [0.0, 0] #[map, iter]
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(cfg.save_dir, cfg_name)
train_loader.start()
for step_id in range(start_iter, cfg.max_iters):
teacher_loss_np, distill_loss_np, loss_np, lr_np = exe.run(
parallel_main,
fetch_list=[
'teacher_' + teacher_loss.name, distill_loss.name, loss.name,
lr.name
])
if step_id % cfg.log_iter == 0:
logger.info(
"step {} lr {:.6f}, loss {:.6f}, distill_loss {:.6f}, teacher_loss {:.6f}"
.format(step_id, lr_np[0], loss_np[0], distill_loss_np[0],
teacher_loss_np[0]))
if step_id % cfg.snapshot_iter == 0 and step_id != 0 or step_id == cfg.max_iters - 1:
save_name = str(
step_id) if step_id != cfg.max_iters - 1 else "model_final"
checkpoint.save(exe, distill_prog,
os.path.join(save_dir, save_name))
# eval
results = eval_run(exe, compiled_eval_prog, eval_loader, eval_keys,
eval_values, eval_cls, cfg)
resolution = None
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] = step_id
checkpoint.save(exe, distill_prog,
os.path.join("./", "best_model"))
logger.info("Best test box ap: {}, in step: {}".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
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)
# 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():
"""
Main evaluate function
"""
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)
# 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 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
pyreader, feed_vars = create_feed(eval_feed)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
pyreader.decorate_sample_list_generator(reader, place)
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(eval_feed,
cfg.metric,
json_directory=FLAGS.output_eval)
return
# compile program for multi-devices
if devices_num <= 1:
compile_program = fluid.compiler.CompiledProgram(eval_prog)
else:
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
build_strategy.enable_inplace = False
compile_program = fluid.compiler.CompiledProgram(
eval_prog).with_data_parallel(build_strategy=build_strategy)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_pretrain(exe, eval_prog, cfg.weights)
assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric)
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']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
results = eval_run(exe, compile_program, pyreader, keys, values, cls)
# evaluation
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
eval_results(results, eval_feed, cfg.metric, cfg.num_classes, resolution,
is_bbox_normalized, FLAGS.output_eval, cfg.map_type)
def main():
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
dataset = cfg.TestReader['dataset']
test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img)
dataset.set_images(test_images)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
model = create(main_arch)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['iterable'] = True
feed_vars, loader = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
reader = create_reader(cfg.TestReader, devices_num=1)
loader.set_sample_list_generator(reader, place)
exe.run(startup_prog)
if cfg.weights:
checkpoint.load_params(exe, infer_prog, cfg.weights)
# parse infer fetches
assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
extra_keys = []
if cfg['metric'] in ['COCO', 'OID']:
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE':
extra_keys = ['im_id', 'im_shape']
keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys)
# parse dataset category
if cfg.metric == 'COCO':
from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info
if cfg.metric == 'OID':
from ppdet.utils.oid_eval import bbox2out, get_category_info
if cfg.metric == "VOC":
from ppdet.utils.voc_eval import bbox2out, get_category_info
if cfg.metric == "WIDERFACE":
from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info
anno_file = dataset.get_anno()
with_background = dataset.with_background
use_default_label = dataset.use_default_label
clsid2catid, catid2name = get_category_info(anno_file, with_background,
use_default_label)
# 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()
# use tb-paddle to log image
if FLAGS.use_tb:
from tb_paddle import SummaryWriter
tb_writer = SummaryWriter(FLAGS.tb_log_dir)
tb_image_step = 0
tb_image_frame = 0 # each frame can display ten pictures at most.
imid2path = dataset.get_imid2path()
for iter_id, data in enumerate(loader()):
outs = exe.run(infer_prog,
feed=data,
fetch_list=values,
return_numpy=False)
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
logger.info('Infer iter {}'.format(iter_id))
bbox_results = None
mask_results = None
if 'bbox' in res:
bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
if 'mask' in res:
mask_results = mask2out([res], clsid2catid,
model.mask_head.resolution)
# visualize result
im_ids = res['im_id'][0]
for im_id in im_ids:
image_path = imid2path[int(im_id)]
image = Image.open(image_path).convert('RGB')
# use tb-paddle to log original image
if FLAGS.use_tb:
original_image_np = np.array(image)
tb_writer.add_image("original/frame_{}".format(tb_image_frame),
original_image_np,
tb_image_step,
dataformats='HWC')
image = visualize_results(image, int(im_id), catid2name,
FLAGS.draw_threshold, bbox_results,
mask_results)
# use tb-paddle to log image with bbox
if FLAGS.use_tb:
infer_image_np = np.array(image)
tb_writer.add_image("bbox/frame_{}".format(tb_image_frame),
infer_image_np,
tb_image_step,
dataformats='HWC')
tb_image_step += 1
if tb_image_step % 10 == 0:
tb_image_step = 0
tb_image_frame += 1
save_name = get_save_image_name(FLAGS.output_dir, image_path)
logger.info("Detection bbox results save in {}".format(save_name))
image.save(save_name, quality=95)
def main():
"""
Main evaluate function
"""
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
multi_scale_test = getattr(cfg, 'MultiScaleTEST', None)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
feed_vars, loader = model.build_inputs(**inputs_def)
if multi_scale_test is None:
fetches = model.eval(feed_vars)
else:
fetches = model.eval(feed_vars, multi_scale_test)
eval_prog = eval_prog.clone(True)
exe.run(startup_prog)
reader = create_reader(cfg.EvalReader)
# When iterable mode, set set_sample_list_generator(reader, place)
loader.set_sample_list_generator(reader)
dataset = cfg['EvalReader']['dataset']
# eval already exists json file
if FLAGS.json_eval:
logger.info(
"In json_eval mode, PaddleDetection will evaluate json files in "
"output_eval directly. And proposal.json, bbox.json and mask.json "
"will be detected by default.")
json_eval_results(
cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset)
return
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)."
base_flops = flops(eval_prog)
pruner = Pruner()
eval_prog, _, _ = pruner.prune(
eval_prog,
fluid.global_scope(),
params=pruned_params,
ratios=pruned_ratios,
place=place,
only_graph=False)
pruned_flops = flops(eval_prog)
logger.info("pruned FLOPS: {}".format(
float(base_flops - pruned_flops) / base_flops))
compile_program = fluid.CompiledProgram(eval_prog).with_data_parallel()
assert cfg.metric != 'OID', "eval process of OID dataset \
is not supported."
if cfg.metric == "WIDERFACE":
raise ValueError("metric type {} does not support in tools/eval.py, "
"please use tools/face_eval.py".format(cfg.metric))
assert cfg.metric in ['COCO', 'VOC'], \
"unknown metric type {}".format(cfg.metric)
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']
keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys)
# 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()
sub_eval_prog = None
sub_keys = None
sub_values = None
# build sub-program
if 'Mask' in main_arch and multi_scale_test:
sub_eval_prog = fluid.Program()
with fluid.program_guard(sub_eval_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['EvalReader']['inputs_def']
inputs_def['mask_branch'] = True
feed_vars, eval_loader = model.build_inputs(**inputs_def)
sub_fetches = model.eval(
feed_vars, multi_scale_test, mask_branch=True)
assert cfg.metric == 'COCO'
extra_keys = ['im_id', 'im_shape']
sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog,
extra_keys)
sub_eval_prog = sub_eval_prog.clone(True)
# load model
if 'weights' in cfg:
checkpoint.load_checkpoint(exe, eval_prog, cfg.weights)
resolution = None
if 'Mask' in cfg.architecture:
resolution = model.mask_head.resolution
results = eval_run(
exe,
compile_program,
loader,
keys,
values,
cls,
cfg,
sub_eval_prog,
sub_keys,
sub_values,
resolution=resolution)
# 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'
eval_results(
results,
cfg.metric,
cfg.num_classes,
resolution,
is_bbox_normalized,
FLAGS.output_eval,
map_type,
dataset=dataset)
