def __init__(self):
super(Actor, self).__init__(
h1 = L.Linear(O_DIM, 400),
h2 = L.Linear(400, 300),
h3 = L.Linear(300, A_DIM, initialW=initializers.Uniform(scale=0.003)),
)
self.optimizer = optimizers.Adam(alpha=ACTOR_LEARNING_RATE)
self.optimizer.setup(self)
self.optimizer.add_hook(optimizer_hooks.GradientClipping(2.0))
def __init__(self):
super(Critic, self).__init__(
h1=L.Linear(O_DIM, 400),
h2_s=L.Linear(400, 300, nobias=True),
h2_a=L.Linear(A_DIM, 300),
h3=L.Linear(300, 1, initialW=initializers.Uniform(scale=0.003)),
)
self.optimizer = optimizers.Adam(alpha=CRITIC_LEARNING_RATE)
self.optimizer.setup(self)
#self.optimizer.add_hook(optimizer_hooks.WeightDecay(CRITIC_WEIGHT_DECAY))
self.optimizer.add_hook(optimizer_hooks.GradientClipping(2.0))
def get_trainer_and_reporter(
trial:Trial,
model:CbLossClassifier,
iter_test:iterators.SerialIterator,
iter_train:iterators.SerialIterator,
batch_converter,
args,
device=0,
best_params={}):
if best_params != {}:# 過去の best_param 使う場合
learning_rate = best_params['learning_rate']
else:
learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)
grad_clipping = trial.suggest_uniform('grad_clipping',0,1.0)
optimizer = optimizers.SGD(lr=learning_rate)
optimizer.setup(model)
optimizer.add_hook(optimizer_hooks.GradientClipping(threshold=grad_clipping))
updater = training.StandardUpdater(
iter_train,
optimizer,
device=device,
converter=batch_converter
)
early_trigger = training.triggers.EarlyStoppingTrigger(
check_trigger=(1, "epoch"),
monitor="validation/main/accuracy",
patients=3,
mode="max",
max_trigger=(args.epoch, "epoch")
)
trainer = training.Trainer(updater,early_trigger,out='optuna')
trainer.extend(extensions.Evaluator(iter_test, model,device=device,converter=batch_converter))
snapshot_writer = training.extensions.snapshot_writers.ThreadQueueWriter()
trainer.extend(training.extensions.snapshot_object(
target=model,
filename='model_{}.npz'.format(args.desc),
writer=snapshot_writer),trigger=(10,'epoch'))
reporter = extensions.LogReport()
trainer.extend(reporter)
trainer.extend(integration.ChainerPruningExtension(
trial,args.pruning_key,(args.pruning_trigger_epoch,'epoch')))
iter_test.reset()
iter_train.reset()
return trainer,reporter
def __init__(self,
state_dim,
action_num,
lr=1.0 * 1e-3,
batch_size=100,
device=-1,
shared_model=False,
clip_grads=False,
use_batch_norm=True,
double_q=False):
super(NAF, self).__init__()
self._q_optimizer = optimizers.Adam(alpha=lr)
self._batch_size = batch_size
self._double_q = double_q
if shared_model:
self._q = NafSharedQFunction(state_dim=state_dim,
action_num=action_num,
use_batch_norm=use_batch_norm)
self._target_q = NafSharedQFunction(state_dim=state_dim,
action_num=action_num,
use_batch_norm=use_batch_norm)
else:
self._q = NafQFunction(state_dim=state_dim,
action_num=action_num,
use_batch_norm=use_batch_norm)
self._target_q = NafQFunction(state_dim=state_dim,
action_num=action_num,
use_batch_norm=use_batch_norm)
if not device < 0:
self._q.to_gpu()
self._target_q.to_gpu()
self._q_optimizer.setup(self._q)
if clip_grads:
self._q_optimizer.add_hook(optimizer_hooks.GradientClipping(1.0))
mean = np.zeros(shape=(action_num), dtype=np.float32)
sigma = np.ones(shape=(action_num), dtype=np.float32)
self._exploration_noise = Normal(loc=mean, scale=sigma * 0.1)
self._device = device
self._initialized = False
self._action_num = action_num
def check_clipping(self, multiplier):
w = self.target.param.data
g = self.target.param.grad
xp = backend.get_array_module(w)
norm = xp.sqrt(gradient_clipping._sum_sqnorm(g))
threshold = norm * multiplier
if multiplier < 1:
expect = w - g * multiplier
else:
expect = w - g
opt = optimizers.SGD(lr=1)
opt.setup(self.target)
opt.add_hook(optimizer_hooks.GradientClipping(threshold))
opt.update()
testing.assert_allclose(expect, w)
def create_optimizer(model):
cp: Dict[str, Any] = copy(config.train.optimizer)
n = cp.pop("name").lower()
if n == "adam":
optimizer = optimizers.Adam(**cp)
elif n == "sgd":
optimizer = optimizers.SGD(**cp)
else:
raise ValueError(n)
optimizer.setup(model)
if config.train.optimizer_gradient_clipping is not None:
optimizer.add_hook(
optimizer_hooks.GradientClipping(
config.train.optimizer_gradient_clipping))
return optimizer
def check_clipping(self, backend_configs, rate):
target = self.target
norm = self.norm
assert len(backend_configs) == len(list(target.params()))
devices = [bc.device for bc in backend_configs]
threshold = norm * rate
expects = []
for param, device in zip(target.params(), devices):
expects.append(param.array - param.grad * min(1, rate))
param.to_device(device)
opt = optimizers.SGD(lr=1)
opt.setup(target)
opt.add_hook(optimizer_hooks.GradientClipping(threshold))
opt.update()
for expect, param in zip(expects, target.params()):
testing.assert_allclose(expect, param.array)
def add_hooks(optimizer, configs):
"""
:param optimizer: chainer.Optimizer, chainerのオプティマイザ
:param configs: pyner.util.config.ConfigParser
"""
if "optimizer" not in configs:
raise Exception("Optimizer configurations are not found")
optimizer_configs = configs["optimizer"]
if optimizer_configs.get("weight_decay"):
logger.debug("\x1b[31mSet weight decay\x1b[0m")
optimizer.add_hook(
optimizer_hooks.WeightDecay(optimizer_configs["weight_decay"])
)
if "gradient_clipping" in optimizer_configs:
clipping_threshold = optimizer_configs["gradient_clipping"]
msg = "Enable gradient clipping:"
msg += f" threshold \x1b[31m{clipping_threshold}\x1b[0m"
logger.debug(msg)
optimizer.add_hook(optimizer_hooks.GradientClipping(clipping_threshold))
return optimizer
def add_hooks(optimizer, configs):
"""
:param optimizer: chainer.Optimizer, chainerのオプティマイザ
:param configs: pyner.util.config.ConfigParser
"""
if 'optimizer' not in configs:
raise Exception('Optimizer configurations are not found')
optimizer_configs = configs['optimizer']
if optimizer_configs.get('weight_decay'):
logger.debug('\x1b[31mSet weight decay\x1b[0m')
optimizer.add_hook(
optimizer_hooks.WeightDecay(optimizer_configs['weight_decay']))
if 'gradient_clipping' in optimizer_configs:
clipping_threshold = optimizer_configs['gradient_clipping']
msg = 'Enable gradient clipping:'
msg += f' threshold \x1b[31m{clipping_threshold}\x1b[0m'
logger.debug(msg)
optimizer.add_hook(
optimizer_hooks.GradientClipping(clipping_threshold))
return optimizer
def main():
parser = argparse.ArgumentParser(description='Chainer YOLOv3 Train')
parser.add_argument('--names')
parser.add_argument('--train')
parser.add_argument('--valid', default='')
parser.add_argument('--detection', default='')
parser.add_argument('--batchsize', '-b', type=int, default=8)
parser.add_argument('--iteration', '-i', type=int, default=50200)
parser.add_argument('--gpus', '-g', type=int, nargs='*', default=[])
parser.add_argument('--out', '-o', default='yolov3-result')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--display_interval', type=int, default=100)
parser.add_argument('--snapshot_interval', type=int, default=100)
parser.add_argument('--ignore_thresh', type=float, default=0.5)
parser.add_argument('--thresh', type=float, default=0.5)
parser.add_argument('--darknet', default='')
parser.add_argument('--darknet_class', type=int, default=-1)
parser.add_argument('--steps', type=int, nargs='*', default=[-10200, -5200])
parser.add_argument('--scales', type=float, nargs='*', default=[0.1, 0.1])
args = parser.parse_args()
print('GPUs: {}'.format(args.gpus))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# iteration: {}'.format(args.iteration))
class_names = load_list(args.names)
random.seed(args.seed)
np.random.seed(args.seed)
base = None
if len(args.darknet) > 0:
darknet_class = args.darknet_class if args.darknet_class > 0 else len(class_names)
darknet53 = Darknet53(darknet_class)
serializers.load_npz(args.darknet, darknet53)
base = darknet53.base
yolov3 = YOLOv3(len(class_names), base, ignore_thresh=args.ignore_thresh)
model = YOLOv3Loss(yolov3)
device = -1
if len(args.gpus) > 0:
device = args.gpus[0]
cuda.cupy.random.seed(args.seed)
cuda.get_device_from_id(args.gpus[0]).use()
if len(args.gpus) == 1:
model.to_gpu()
optimizer = chainer.optimizers.MomentumSGD(lr=0.001)
optimizer.setup(model)
optimizer.add_hook(optimizer_hooks.WeightDecay(0.0005), 'hook_decay')
optimizer.add_hook(optimizer_hooks.GradientClipping(10.0), 'hook_grad_clip')
train = YOLODataset(args.train, train=True, classifier=False,
jitter=0.3, hue=0.1, sat=1.5, val=1.5)
#train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
train_iter = chainer.iterators.MultiprocessIterator(train, args.batchsize,
shared_mem=(448**2*3+(1+4)*100)*4)
if len(args.gpus) <= 1:
updater = training.StandardUpdater(
train_iter, optimizer, converter=concat_yolo, device=device)
else:
devices = {'main': args.gpus[0]}
for gpu in args.gpus[1:]:
devices['gpu{}'.format(gpu)] = gpu
updater = training.ParallelUpdater(
train_iter, optimizer, converter=concat_yolo, devices=devices)
trainer = training.Trainer(
updater, (args.iteration, 'iteration'), out=args.out)
display_interval = (args.display_interval, 'iteration')
snapshot_interval = (args.snapshot_interval, 'iteration')
print_entries = ['epoch', 'iteration', 'main/loss', 'elapsed_time']
plot_keys = ['main/loss']
snapshot_key = 'main/loss'
if len(args.valid) > 0:
print_entries = ['epoch', 'iteration',
'main/loss', 'validation/main/loss', 'elapsed_time']
plot_keys = ['main/loss', 'validation/main/loss']
snapshot_key = 'validation/main/loss'
test = YOLODataset(args.valid, train=False, classifier=False)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
trainer.extend(extensions.Evaluator(
test_iter, model, converter=concat_yolo,
device=device), trigger=display_interval)
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.LogReport(trigger=display_interval))
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(
plot_keys, 'iteration',
display_interval, file_name='loss.png'))
trainer.extend(extensions.PrintReport(print_entries),
trigger=display_interval)
trainer.extend(extensions.ProgressBar(update_interval=1))
trainer.extend(extensions.snapshot_object(
yolov3, 'yolov3_snapshot.npz'),
trigger=training.triggers.MinValueTrigger(
snapshot_key, snapshot_interval))
trainer.extend(extensions.snapshot_object(
yolov3, 'yolov3_backup.npz'),
trigger=snapshot_interval)
trainer.extend(extensions.snapshot_object(
yolov3, 'yolov3_final.npz'),
trigger=(args.iteration, 'iteration'))
steps = args.steps
for i in range(len(steps)):
if steps[i] < 0:
steps[i] = args.iteration + steps[i]
scales = args.scales
print('# steps: {}'.format(steps))
print('# scales: {}'.format(scales))
trainer.extend(DarknetShift(
optimizer, 'steps', args.iteration, burn_in=1000,
steps=steps, scales=scales
))
trainer.extend(CropSizeUpdater(train,
[(10+i)*32 for i in range(0,5)],
args.iteration - 200))
if len(args.detection):
detector = YOLOv3Predictor(yolov3, thresh=args.thresh)
trainer.extend(YOLODetection(
detector,
load_list(args.detection),
class_names, (416, 416),args.thresh,
trigger=display_interval, device=device
))
print('')
print('RUN')
print('')
trainer.run()
def main():
parser = argparse.ArgumentParser(description='Chainer YOLOv3 VOC Train')
parser.add_argument('--batchsize', '-b', type=int, default=8)
parser.add_argument('--iteration', '-i', type=int, default=50200)
parser.add_argument('--gpus', '-g', type=int, nargs='*', default=[])
parser.add_argument('--out', '-o', default='yolov3-voc-result')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--display_interval', type=int, default=100)
parser.add_argument('--snapshot_interval', type=int, default=100)
parser.add_argument('--ignore_thresh', type=float, default=0.5)
parser.add_argument('--thresh', type=float, default=0.4)
parser.add_argument('--darknet', default='')
parser.add_argument('--validation_size', type=int, default=32)
args = parser.parse_args()
print('GPUs: {}'.format(args.gpus))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# iteration: {}'.format(args.iteration))
print('')
random.seed(args.seed)
np.random.seed(args.seed)
base = None
if len(args.darknet) > 0:
darknet53 = Darknet53(20)
serializers.load_npz(args.darknet, darknet53)
base = darknet53.base
yolov3 = YOLOv3(20, base, ignore_thresh=args.ignore_thresh)
model = YOLOv3Loss(yolov3)
device = -1
if len(args.gpus) > 0:
device = args.gpus[0]
cuda.cupy.random.seed(args.seed)
cuda.get_device_from_id(args.gpus[0]).use()
if len(args.gpus) == 1:
model.to_gpu()
optimizer = chainer.optimizers.MomentumSGD(lr=0.001)
optimizer.setup(model)
optimizer.add_hook(optimizer_hooks.WeightDecay(0.0005), 'hook_decay')
optimizer.add_hook(optimizer_hooks.GradientClipping(10.0), 'hook_grad_clip')
train = VOCBboxDataset(split='train')
test = VOCBboxDataset(split='val')
train = YOLOVOCDataset(train, classifier=False, jitter=0.3,
hue=0.1, sat=1.5, val=1.5)
#train = train[np.arange(args.batchsize)]
test = YOLOVOCDataset(test, classifier=False)
test = test[np.random.permutation(np.arange(len(test)))[:min(args.validation_size, len(test))]]
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
repeat=False, shuffle=False)
if len(args.gpus) <= 1:
updater = training.StandardUpdater(
train_iter, optimizer, converter=concat_yolo, device=device)
else:
devices = {'main': args.gpus[0]}
for gpu in args.gpus[1:]:
devices['gpu{}'.format(gpu)] = gpu
updater = training.ParallelUpdater(
train_iter, optimizer, converter=concat_yolo, devices=devices)
trainer = training.Trainer(
updater, (args.iteration, 'iteration'), out=args.out)
display_interval = (args.display_interval, 'iteration')
snapshot_interval = (args.snapshot_interval, 'iteration')
trainer.extend(extensions.Evaluator(
test_iter, model, converter=concat_yolo,
device=device), trigger=display_interval)
trainer.extend(extensions.dump_graph('main/loss'))
trainer.extend(extensions.LogReport(trigger=display_interval))
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(
['main/loss', 'validation/main/loss'], 'iteration',
display_interval, file_name='loss.png'))
trainer.extend(extensions.PrintReport(
['epoch', 'iteration',
'main/loss', 'validation/main/loss', 'elapsed_time']),
trigger=display_interval)
trainer.extend(extensions.ProgressBar(update_interval=1))
trainer.extend(extensions.snapshot_object(
yolov3, 'yolov3_snapshot.npz'),
trigger=training.triggers.MinValueTrigger(
'validation/main/loss', snapshot_interval))
trainer.extend(extensions.snapshot_object(
yolov3, 'yolov3_final.npz'),
trigger=snapshot_interval)
trainer.extend(DarknetShift(
optimizer, 'steps', args.iteration, burn_in=1000,
steps=[args.iteration-10200,args.iteration-5200], scales=[0.1,0.1]
))
trainer.extend(CropSizeUpdater(train,
[(10+i)*32 for i in range(0,5)],
args.iteration - 200))
detector = YOLOv3Predictor(yolov3, thresh=args.thresh)
class_names = load_list('./data/voc.names')
trainer.extend(YOLODetection(
detector,
['./data/image/dog.jpg'],
class_names, size=(416, 416) ,thresh=args.thresh,
trigger=display_interval, device=device
))
trainer.run()
def main():
#hyper parameter
z_dim = 100
dis_ln = 1
grad_clip = 0.1
adam_alpha = 1e-4
adam_beta1 = 0.5
adam_beta2 = 0.9
#training option
is_random = False #whether conditional
npz_interval = 100
max_epoch = 200
out_dir = 'result-dcgan'
batch_size = 128
device = 0
gen_npz = None
dis_npz = None
#gen_npz = 'gen_snapshot_epoch-200.npz'
#dis_npz = 'dis_snapshot_epoch-200.npz'
train, _ = datasets.mnist.get_mnist(ndim=3)
train_iter = iterators.SerialIterator(train, batch_size)
gen = Generator(z_dim)
gen.to_gpu(device=device)
if gen_npz is not None:
chainer.serializers.load_npz(out_dir + '/' + gen_npz, gen)
gen_opt = optimizers.Adam(alpha=adam_alpha,
beta1=adam_beta1,
beta2=adam_beta2)
gen_opt.setup(gen)
gen_opt.add_hook(optimizer_hooks.GradientClipping(grad_clip))
dis = Discriminator()
dis.to_gpu(device=device)
if dis_npz is not None:
chainer.serializers.load_npz(out_dir + '/' + dis_npz, dis)
dis_opt = optimizers.Adam(alpha=adam_alpha,
beta1=adam_beta1,
beta2=adam_beta2)
dis_opt.setup(dis)
dis_opt.add_hook(optimizer_hooks.GradientClipping(grad_clip))
updater = GANUpdater(dis_ln=dis_ln,
is_random=is_random,
iterator=train_iter,
optimizer={
'gen': gen_opt,
'dis': dis_opt
},
device=device)
trainer = training.Trainer(updater, (max_epoch, 'epoch'), out=out_dir)
trainer.extend(ex.LogReport(log_name=None, trigger=(1, 'iteration')))
trainer.extend(
ex.PrintReport([
'epoch', 'iteration', 'gen/loss', 'dis/loss', 'dis/fake_prob',
'elapsed_time'
]))
trainer.extend(
ex.PlotReport(
['gen/loss', 'dis/loss'],
x_key='epoch',
file_name='loss.png',
postprocess=lambda *args: gen.save_img(out_dir=out_dir + '/img')))
trainer.extend(
ex.PlotReport(['dis/fake_prob'],
x_key='epoch',
file_name='probability.png'))
trainer.extend(ex.snapshot_object(
gen, 'gen_snapshot_epoch-{.updater.epoch}.npz'),
trigger=(npz_interval, 'epoch'))
trainer.extend(ex.snapshot_object(
dis, 'dis_snapshot_epoch-{.updater.epoch}.npz'),
trigger=(npz_interval, 'epoch'))
trainer.run()
def get_trainer_and_reporter(
model:CbLossClassifier,
df_test:pd.DataFrame,
df_train:pd.DataFrame,
batch_size,
batch_converter,
args,
device,
plot_list,
print_list=[],
learning_rate=1e-5,
grad_clipping=0.5):
iter_train = get_iter(df_train,batch_size,shuffle=True ,repeat=True)
iter_test = get_iter(df_test ,batch_size,shuffle=False,repeat=False)
optimizer = optimizers.SGD(lr=learning_rate)
optimizer.setup(model)
if grad_clipping != None: # grad_clipping ありの場合
optimizer.add_hook(optimizer_hooks.GradientClipping(threshold=grad_clipping))
updater = training.StandardUpdater(
iter_train,
optimizer,
device=device,
converter=batch_converter
)
trainer = training.Trainer(updater,(args.epoch,'epoch'),out='result')
ext_evaluator = training.extensions.Evaluator(iter_test, model,device=device,converter=batch_converter)
ext_snapshot_object = training.extensions.snapshot_object(
target = model,
filename = 'model_{}.npz'.format(args.desc),
writer = training.extensions.snapshot_writers.ThreadQueueWriter())
ext_reporter = training.extensions.LogReport()
ext_plotreport_list = []
for tag,plot in plot_list:
ext_plotreport_list.append(
training.extensions.PlotReport(
plot,
x_key = 'epoch',
file_name = '{}/{}_{}.png'.format(args.result_directory,tag,args.desc))
)
ext_dump_graph = training.extensions.dump_graph('main/loss')
trainer.extend(ext_evaluator)
trainer.extend(ext_snapshot_object,trigger=(10,'epoch'))
trainer.extend(ext_reporter)
for pl in ext_plotreport_list:
trainer.extend(pl)
trainer.extend(ext_dump_graph)
if print_list != []:
ext_printreport = training.extensions.PrintReport(print_list)
trainer.extend(ext_printreport)
iter_test.reset()
iter_train.reset()
return trainer,ext_reporter