def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_epoch = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(5),
force_cpu=True)
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
def linear_warmup_and_cosine_decay(learning_rate, end_lr, warmup_steps,
max_training_steps):
"""Applies linear warmup and cosine decay to the learning rate."""
dtype = "float32"
with fluid.default_main_program()._lr_schedule_guard():
lr = layers.create_global_var(shape=[1],
value=0.0,
dtype=dtype,
persistable=True,
name="learning_rate")
global_step = _decay_step_counter(1)
with layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_steps):
warmup_lr = learning_rate * (global_step / warmup_steps)
layers.assign(warmup_lr, lr)
with switch.case(global_step < max_training_steps):
frac = 0.5 * (ops.cos((global_step - warmup_steps) * math.pi /
(max_training_steps - warmup_steps)) + 1)
decayed_lr = end_lr + (learning_rate - end_lr) * frac
layers.assign(decayed_lr, lr)
with switch.default():
learning_rate = layers.fill_constant(shape=[1],
dtype=dtype,
value=end_lr)
layers.assign(learning_rate, lr)
return lr
def cosine_decay_with_warmup(learning_rate,
max_iters=90000,
warmup_iters=1000,
warmup_factor=0.1):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_iters):
eta_min = learning_rate * warmup_factor
decayed_lr = eta_min + (learning_rate - eta_min) * (global_step / warmup_iters)
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_iters) * (math.pi / (max_iters - warmup_iters))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
def exponential_decay_with_warmup(learning_rate,
step_each_epoch,
decay_epochs,
decay_rate=0.97,
warm_up_epoch=5.0):
"""Applies exponential decay to the learning rate.
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_epoch = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(warm_up_epoch),
force_cpu=True)
with init_on_cpu():
epoch = ops.floor(global_step / step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < warmup_epoch):
decayed_lr = learning_rate * (global_step /
(step_each_epoch * warmup_epoch))
fluid.layers.assign(input=decayed_lr, output=lr)
with switch.default():
div_res = (global_step -
warmup_epoch * step_each_epoch) / decay_epochs
div_res = ops.floor(div_res)
decayed_lr = learning_rate * (decay_rate**div_res)
fluid.layers.assign(input=decayed_lr, output=lr)
return lr
def cosine_decay():
"""
Applies cosine decay to the learning rate.
"""
global_step = _decay_step_counter()
frac = (1 + ops.cos(global_step / max_step * math.pi)) / 2
return FLAGS.lr_min + (FLAGS.lr_max - FLAGS.lr_min) * frac
def linear_warmup_decay(init_lr, num_train_steps, num_warmup_steps,
main_program):
with main_program._lr_schedule_guard():
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(shape=[1],
value=init_lr,
dtype='float32',
persistable=True,
name="learning_rate")
with control_flow.Switch() as switch:
with switch.case(global_step < num_warmup_steps):
decayed_lr = init_lr * global_step * 1.0 / num_warmup_steps
fluid.layers.assign(decayed_lr, lr)
with switch.default():
decayed_lr = lr_scheduler.polynomial_decay(
learning_rate=init_lr,
decay_steps=num_train_steps,
end_learning_rate=0.0,
power=1.0,
cycle=False)
fluid.layers.assign(decayed_lr, lr)
return lr
def __call__(self):
global_step = _decay_step_counter()
learning_rate = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
epoch = ops.floor(global_step / self.step_each_epoch)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch < self.warmup_epoch):
decayed_lr = self.lr * \
(global_step / (self.step_each_epoch * self.warmup_epoch))
fluid.layers.tensor.assign(input=decayed_lr,
output=learning_rate)
with switch.default():
current_step = global_step - self.warmup_epoch * self.step_each_epoch
total_step = (self.epochs -
self.warmup_epoch) * self.step_each_epoch
decayed_lr = self.lr * \
(ops.cos(current_step * math.pi / total_step) + 1) / 2
fluid.layers.tensor.assign(input=decayed_lr,
output=learning_rate)
return learning_rate
def cosine_with_warmup_decay(learning_rate, lr_min, steps_one_epoch,
warmup_epochs, total_epoch, num_gpu):
global_step = _decay_step_counter()
epoch_idx = fluid.layers.floor(global_step / steps_one_epoch)
lr = fluid.layers.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_epoch_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(warmup_epochs), force_cpu=True)
num_gpu_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(num_gpu), force_cpu=True)
batch_idx = global_step - steps_one_epoch * epoch_idx
with fluid.layers.control_flow.Switch() as switch:
with switch.case(epoch_idx < warmup_epoch_var):
epoch_ = (batch_idx + 1) / steps_one_epoch
factor = 1 / num_gpu_var * (
epoch_ * (num_gpu_var - 1) / warmup_epoch_var + 1)
decayed_lr = learning_rate * factor * num_gpu_var
fluid.layers.assign(decayed_lr, lr)
epoch_ = (batch_idx + 1) / steps_one_epoch
m = epoch_ / total_epoch
frac = (1 + ops.cos(math.pi * m)) / 2
cosine_lr = (lr_min + (learning_rate - lr_min) * frac) * num_gpu_var
with switch.default():
fluid.layers.assign(cosine_lr, lr)
return lr
def cosine_decay(lr, step_each_epoch, epochs):
global_step = _decay_step_counter()
with init_on_cpu():
epoch = fluid.layers.floor(global_step / step_each_epoch)
decayed_lr = lr * (fluid.layers.cos(epoch *
(math.pi / epochs)) + 1) / 2
return decayed_lr
def cosine_decay(learning_rate, step_each_epoch, epochs=120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
epoch = ops.floor(global_step / step_each_epoch)
decayed_lr = learning_rate * \
(ops.cos(epoch * (math.pi / epochs)) + 1)/2
return decayed_lr
def cosine_warmup_decay(learning_rate, betas, warmup_factor, decay_factor,
total_step, warmup_pct):
def annealing_cos(start, end, pct):
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = fluid.layers.cos(pct * np.pi) + 1.
return cos_out * (start - end) / 2. + end
warmup_start_lr = learning_rate * warmup_factor
decay_end_lr = learning_rate * decay_factor
warmup_step = total_step * warmup_pct
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(shape=[1],
value=float(learning_rate),
dtype='float32',
persistable=True,
name="learning_rate")
beta1 = fluid.layers.create_global_var(shape=[1],
value=float(betas[0]),
dtype='float32',
persistable=True,
name="beta1")
warmup_step_var = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(warmup_step),
force_cpu=True)
warmup_pred = global_step < warmup_step_var
decay_pred = global_step >= warmup_step_var
# learning rate warmup and decay
def warmup_lr():
return annealing_cos(warmup_start_lr, learning_rate,
global_step / warmup_step_var)
def decay_lr():
return annealing_cos(learning_rate, decay_end_lr,
(global_step - warmup_step_var) /
(total_step - warmup_step))
lr = fluid.layers.case(pred_fn_pairs=[(warmup_pred,
warmup_lr), (decay_pred, decay_lr)])
# Adam beta1 warmup and decay
def warmup_beta1():
return annealing_cos(betas[0], betas[1], global_step / warmup_step_var)
def decay_beta1():
return annealing_cos(betas[0], betas[1], global_step / warmup_step_var)
beta1 = fluid.layers.case(
pred_fn_pairs=[(warmup_pred, warmup_beta1), (decay_pred, decay_beta1)])
return lr, beta1
def cosine_decay(learning_rate, step_each_epoch, epochs = 120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
with init_on_cpu():
epoch = fluid.layers.floor(global_step / step_each_epoch)
lr = learning_rate / 2.
decayed_lr = lr * (fluid.layers.cos(epoch * (math.pi / epochs)) + 1)
return decayed_lr
def cosine_decay(learning_rate, num_epoch, steps_one_epoch):
"""Applies cosine decay to the learning rate.
lr = 0.5 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
decayed_lr = learning_rate * \
(ops.cos(fluid.layers.floor(global_step / steps_one_epoch) \
* math.pi / num_epoch) + 1)/2
return decayed_lr
def cosine_decay_v2(learning_rate, totalsteps):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(global_step * (math.pi / totalsteps)) + 1)
decrease lr for every mini-batch.
"""
global_step = _decay_step_counter()
with init_on_cpu():
decayed_lr = learning_rate * \
(ops.cos(global_step * (math.pi / float(totalsteps))) + 1)/2
return decayed_lr
def cosine_decay(learning_rate, num_epoch, steps_one_epoch):
"""Applies cosine decay to the learning rate.
lr = 0.5 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
with init_on_cpu():
decayed_lr = learning_rate * \
(ops.cos((global_step / steps_one_epoch) \
* math.pi / num_epoch) + 1)/2
return decayed_lr
def cos_anneal_with_warmup_decay(learning_rate, boundaries, values,
warmup_iter, warmup_factor):
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_iter_var = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(warmup_iter),
force_cpu=True)
cos_boundaries = [warmup_iter] + boundaries[:-1]
cos_boundaries = np.array(boundaries) - np.array(cos_boundaries)
cos_boundaries = cos_boundaries.tolist()
cos_step = [warmup_iter] + boundaries[:-1]
#pdb.set_trace()
with control_flow.Switch() as switch:
with switch.case(global_step < warmup_iter_var):
alpha = global_step / warmup_iter_var
factor = warmup_factor * (1 - alpha) + alpha
decayed_lr = learning_rate * factor
fluid.layers.assign(decayed_lr, lr)
for i in range(len(boundaries)):
boundary_val = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(
boundaries[i]),
force_cpu=True)
#value_var = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(values[i]))
with switch.case(global_step < boundary_val):
#pdb.set_trace()
cur_epoch_factor = (global_step -
cos_step[i]) / cos_boundaries[i]
cur_epoch_cos_factor = ops.cos(cur_epoch_factor * math.pi /
2.0)
cur_lr = cur_epoch_cos_factor * values[i]
fluid.layers.assign(cur_lr, lr)
last_value_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
fluid.layers.assign(last_value_var, lr)
return lr
def cosine_decay(learning_rate, step_each_epoch, epochs = 120):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
"""
global_step = _decay_step_counter()
with init_on_cpu():
# update
epoch = ops.floor(global_step / step_each_epoch)
decayed_lr = learning_rate * \
(ops.cos(epoch * (math.pi / epochs)) + 1)/2
#if global_step % step_each_epoch == 0:
# print("epoch={0}, global_step={1},decayed_lr={2} \
# (step_each_epoch={3})".format( \
# epoch,global_step,decayed_lr,step_each_epoch))
return decayed_lr
def cosine_warmup_decay(learning_rate, betas, warmup_factor, decay_factor,
total_step, warmup_pct):
def annealing_cos(start, end, pct):
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = fluid.layers.cos(pct * np.pi) + 1.
return cos_out * (start - end) / 2. + end
warmup_start_lr = learning_rate * warmup_factor
decay_end_lr = learning_rate * decay_factor
warmup_step = total_step * warmup_pct
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(
shape=[1],
value=float(learning_rate),
dtype='float32',
persistable=True,
name="learning_rate")
beta1 = fluid.layers.create_global_var(
shape=[1],
value=float(betas[0]),
dtype='float32',
persistable=True,
name="beta1")
warmup_step_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(warmup_step), force_cpu=True)
with control_flow.Switch() as switch:
with switch.case(global_step < warmup_step_var):
cur_lr = annealing_cos(warmup_start_lr, learning_rate,
global_step / warmup_step_var)
fluid.layers.assign(cur_lr, lr)
cur_beta1 = annealing_cos(betas[0], betas[1],
global_step / warmup_step_var)
fluid.layers.assign(cur_beta1, beta1)
with switch.case(global_step >= warmup_step_var):
cur_lr = annealing_cos(learning_rate, decay_end_lr,
(global_step - warmup_step_var) / (total_step - warmup_step))
fluid.layers.assign(cur_lr, lr)
cur_beta1 = annealing_cos(betas[1], betas[0],
(global_step - warmup_step_var) / (total_step - warmup_step))
fluid.layers.assign(cur_beta1, beta1)
return lr, beta1
def exponential_with_warmup_decay(learning_rate, boundaries, values,
warmup_iter, warmup_factor):
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_iter_var = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(warmup_iter),
force_cpu=True)
with control_flow.Switch() as switch:
with switch.case(global_step < warmup_iter_var):
alpha = global_step / warmup_iter_var
factor = warmup_factor * (1 - alpha) + alpha
decayed_lr = learning_rate * factor
fluid.layers.assign(decayed_lr, lr)
for i in range(len(boundaries)):
boundary_val = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(
boundaries[i]),
force_cpu=True)
value_var = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(values[i]))
with switch.case(global_step < boundary_val):
fluid.layers.assign(value_var, lr)
last_value_var = fluid.layers.fill_constant(
shape=[1], dtype='float32', value=float(values[len(values) - 1]))
with switch.default():
fluid.layers.assign(last_value_var, lr)
return lr
def cosine_decay_v2_with_warmup(learning_rate, warmupsteps, totalsteps):
"""Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
with init_on_cpu():
with control_flow.Switch() as switch:
with switch.case(global_step < warmupsteps):
decayed_lr = learning_rate * (global_step / float(warmupsteps))
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmupsteps) * (math.pi / (totalsteps))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
def __call__(self, base_lr=None, learning_rate=None):
steps = _decay_step_counter()
total = self.total_steps
if self.skip_steps is not None:
total -= self.skip_steps
lr = fluid.layers.tensor.create_global_var(shape=[1],
value=base_lr,
dtype='float32',
persistable=True,
name="learning_rate")
def decay():
cos_lr = base_lr * .5 * (cos(steps * (math.pi / total)) + 1)
fluid.layers.tensor.assign(input=cos_lr, output=lr)
if self.skip_steps is None:
decay()
else:
skipped = steps >= self.skip_steps
fluid.layers.cond(skipped, decay)
return lr
def linear_warmup_and_invsqrt_decay(learning_rate, warmup_steps, decay_steps):
"""Applies linear warmup and invsqrt decay to the learning rate."""
dtype = "float32"
with fluid.default_main_program()._lr_schedule_guard():
lr = layers.create_global_var(shape=[1],
value=0.0,
dtype=dtype,
persistable=True,
name="learning_rate")
global_step = _decay_step_counter(1)
with layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_steps):
warmup_lr = learning_rate * (global_step / warmup_steps)
layers.assign(warmup_lr, lr)
with switch.default():
decayed_lr = lr * ops.sqrt(
decay_steps / (global_step - warmup_steps + decay_steps))
layers.assign(decayed_lr, lr)
return lr
def cosine_decay_with_warmup(learning_rate,
step_each_epoch,
epochs=500,
warmup_minibatch=1000):
"""
Applies cosine decay to the learning rate.
lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
decrease lr for every mini-batch and start with warmup.
args:
learning_rate(float): initial learning rate
step_each_epoch (int): number of step for each epoch in training process
epochs(int): number of training epochs
warmup_minibatch(int): number of minibatch for warmup
return:
lr(tensor): learning rate tensor
"""
global_step = _decay_step_counter()
lr = fluid.layers.tensor.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_minibatch = fluid.layers.fill_constant(
shape=[1],
dtype='float32',
value=float(warmup_minibatch),
force_cpu=True)
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_minibatch):
decayed_lr = learning_rate * (1.0 * global_step / warmup_minibatch)
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
with switch.default():
decayed_lr = learning_rate * \
(ops.cos((global_step - warmup_minibatch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
fluid.layers.tensor.assign(input=decayed_lr, output=lr)
return lr
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 exponential_with_warmup_decay(learning_rate, boundaries, values,
warmup_iter, warmup_factor):
global_step = lr_scheduler._decay_step_counter()
lr = fluid.layers.create_global_var(shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="learning_rate")
warmup_iter_var = fluid.layers.fill_constant(shape=[1],
dtype='float32',
value=float(warmup_iter),
force_cpu=True)
with control_flow.Switch() as switch:
with switch.case(global_step < warmup_iter_var):
alpha = global_step / warmup_iter_var
factor = warmup_factor * (1 - alpha) + alpha
decayed_lr = learning_rate * factor
fluid.layers.assign(decayed_lr, lr)
#for i in range(len(boundaries)):
# boundary_val = fluid.layers.fill_constant(
# shape=[1],
# dtype='float32',
# value=float(1000000000),
# force_cpu=True)
# #value_var = fluid.layers.fill_constant(
# # shape=[1], dtype='float32', value=float(values[i]))
# lr_decay_rate = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(0.94))
# lr_decay_steps = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(1250.0))
# tmp1 = fluid.layers.cast(global_step / lr_decay_steps, dtype='int32')
# tmp2 = fluid.layers.cast(tmp1, dtype='float32')
# value_var = learning_rate * fluid.layers.pow(lr_decay_rate, global_step / lr_decay_steps)
# with switch.case(global_step < boundary_val):
# fluid.layers.assign(value_var, lr)
#last_value_var = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(values[len(values) - 1]))
ex_decay_lr = fluid.layers.exponential_decay(
learning_rate=learning_rate,
decay_steps=10000,
decay_rate=0.94,
staircase=True)
with switch.default():
fluid.layers.assign(ex_decay_lr, lr)
# with control_flow.Switch() as switch:
# with switch.case(global_step < warmup_iter_var):
# alpha = global_step / warmup_iter_var
# factor = warmup_factor * (1 - alpha) + alpha
# decayed_lr = learning_rate * factor
# fluid.layers.assign(decayed_lr, lr)
#
# for i in range(len(boundaries)):
# boundary_val = fluid.layers.fill_constant(
# shape=[1],
# dtype='float32',
# value=float(boundaries[i]),
# force_cpu=True)
# value_var = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(values[i]))
# with switch.case(global_step < boundary_val):
# fluid.layers.assign(value_var, lr)
#
# last_value_var = fluid.layers.fill_constant(
# shape=[1], dtype='float32', value=float(values[len(values) - 1]))
# with switch.default():
# fluid.layers.assign(last_value_var, lr)
return lr
def poly_decay():
global_step = _decay_step_counter()
with init_on_cpu():
decayed_lr = LEARNING_RATE * (fluid.layers.pow(
(1 - global_step / TOTAL_STEP), POWER))
return decayed_lr
def cosine_annealing(self):
step = _decay_step_counter()
lr = FLAGS.lr_min + (FLAGS.lr_max - FLAGS.lr_min) / 2 \
* (1.0 + fluid.layers.ops.cos(step / self.max_step * math.pi))
return lr
def scheduler_handler(self, max_train_steps):
scheduled_lr = fluid.layers.create_global_var(shape=[1],
value=self.learning_rate,
dtype='float32',
persistable=True,
name="learning_rate")
if not self.scheduler["slanted_triangle"]["cut_fraction"]:
warmup_steps = int(max_train_steps * self.scheduler["warmup"])
linear_decay_start = int(
max_train_steps *
self.scheduler["linear_decay"]["start_point"])
if linear_decay_start < warmup_steps:
logger.warning(
"linear decay can not start during warmup process,"
"it will start after warmup ends!")
linear_decay_start = warmup_steps
if self.scheduler["noam_decay"]:
if warmup_steps > 0:
scheduled_lr = fluid.layers.learning_rate_scheduler \
.noam_decay(1 / (warmup_steps * (self.learning_rate ** 2)),
warmup_steps)
else:
logger.warning(
"Noam decay learning rate scheduler should have positive \
warmup steps, using constant learning rate instead!")
if not self.scheduler["noam_decay"] and \
(warmup_steps > 0 or self.scheduler["linear_decay"]["start_point"]<1):
with self.main_program._lr_schedule_guard():
global_step = lr_scheduler._decay_step_counter()
with control_flow.Switch() as switch:
if warmup_steps > 0:
with switch.case(global_step < warmup_steps):
decayed_lr = self.learning_rate * global_step * 1.0 / warmup_steps
fluid.layers.assign(decayed_lr, scheduled_lr)
if self.scheduler["linear_decay"]["start_point"] < 1:
with switch.case(
global_step >= linear_decay_start):
decayed_lr = lr_scheduler.polynomial_decay(
learning_rate=self.learning_rate,
decay_steps=max_train_steps,
end_learning_rate=self.scheduler[
"linear_decay"]["end_learning_rate"],
power=1.0,
cycle=False)
fluid.layers.assign(decayed_lr, scheduled_lr)
else:
if self.scheduler["warmup"] or self.scheduler[
"noam_decay"] or self.scheduler["linear_decay"][
"start_point"] < 1:
logger.warning(
"You are using slanted_triangle learning rate "
"which will make warmup, noam_decay and linear_decay unable"
)
cut_step = int(max_train_steps *
self.scheduler["slanted_triangle"]["cut_fraction"])
ratio = self.scheduler["slanted_triangle"]["ratio"]
global_step = lr_scheduler._decay_step_counter()
with control_flow.Switch() as switch:
with switch.case(global_step <= cut_step):
pct = global_step / cut_step
decayed_lr = self.learning_rate * (1 + pct *
(ratio - 1)) / ratio
fluid.layers.assign(decayed_lr, scheduled_lr)
with switch.default():
pct = 1 - (global_step - cut_step) / (max_train_steps -
cut_step)
decayed_lr = self.learning_rate * (1 + pct *
(ratio - 1)) / ratio
fluid.layers.assign(decayed_lr, scheduled_lr)
super(CombinedStrategy,
self).__init__(optimizer_name=self._optimizer_name,
learning_rate=scheduled_lr)
if self.scheduler["discriminative"]["blocks"]:
_block_layers = math.ceil(
len(self.sorted_depth) /
self.scheduler["discriminative"]["blocks"])
power = 0
for cnt, depth in enumerate(self.sorted_depth):
for index, param in enumerate(self.depth_params_dict[depth]):
param.optimize_attr["learning_rate"] *= \
pow(1.0 / self.scheduler["discriminative"]["factor"], power)
if cnt and cnt % _block_layers == 0:
power += 1
return scheduled_lr
def poly_decay():
global_step = _decay_step_counter()
# 建立损失函数
y_true = P.data(name='y_true',
shape=[-1, 8, 28, 28],
append_batch_size=False,
dtype='float32')
# 先把差值逐项平方，可以用P.pow()这个op，也可以用python里的运算符**。
mseloss = P.pow(y_true - act02_out_tensor, 2)
mseloss = P.reduce_mean(mseloss) # 再求平均，即mse损失函数
# 优化器
optimizer = fluid.optimizer.SGD(learning_rate=lr)
optimizer.minimize(mseloss)
# ema
global_steps = _decay_step_counter()
ema = ExponentialMovingAverage(ema_decay, thres_steps=global_steps)
ema.update()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
# 重新建立一次网络，用相同的张量名，不用写损失层
inputs = P.data(name='input_1',
shape=[-1, 3, 28, 28],
append_batch_size=False,
dtype='float32')
conv01_out_tensor = fluid.layers.conv2d(
input=inputs,
num_filters=8,
filter_size=1,
