# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from collections import OrderedDict
import paddle.fluid as fluid
from ppcls.optimizer import LearningRateBuilder
from ppcls.optimizer import OptimizerBuilder
from ppcls.modeling import architectures
from ppcls.modeling.loss import CELoss
from ppcls.modeling.loss import MixCELoss
from ppcls.modeling.loss import JSDivLoss
from ppcls.modeling.loss import GoogLeNetLoss
from ppcls.utils.misc import AverageMeter
from ppcls.utils import logger
from paddle.fluid.incubate.fleet.collective import fleet
from paddle.fluid.incubate.fleet.collective import DistributedStrategy
def create_feeds(image_shape, use_mix=None):
"""
Create feeds as model input
Args:
image_shape(list[int]): model input shape, such as [3, 224, 224]
use_mix(bool): whether to use mix(include mixup, cutmix, fmix)
Returns:
feeds(dict): dict of model input variables
"""
feeds = OrderedDict()
feeds['image'] = fluid.data(
name="feed_image", shape=[None] + image_shape, dtype="float32")
if use_mix:
feeds['feed_y_a'] = fluid.data(
name="feed_y_a", shape=[None, 1], dtype="int64")
feeds['feed_y_b'] = fluid.data(
name="feed_y_b", shape=[None, 1], dtype="int64")
feeds['feed_lam'] = fluid.data(
name="feed_lam", shape=[None, 1], dtype="float32")
else:
feeds['label'] = fluid.data(
name="feed_label", shape=[None, 1], dtype="int64")
return feeds
def create_dataloader(feeds):
"""
Create a dataloader with model input variables
Args:
feeds(dict): dict of model input variables
Returns:
dataloader(fluid dataloader):
"""
trainer_num = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
capacity = 64 if trainer_num <= 1 else 8
dataloader = fluid.io.DataLoader.from_generator(
feed_list=feeds,
capacity=capacity,
use_double_buffer=True,
iterable=True)
return dataloader
def create_model(architecture, image, classes_num):
"""
Create a model
Args:
architecture(dict): architecture information,
name(such as ResNet50) is needed
image(variable): model input variable
classes_num(int): num of classes
Returns:
out(variable): model output variable
"""
name = architecture["name"]
params = architecture.get("params", {})
model = architectures.__dict__[name](**params)
out = model.net(input=image, class_dim=classes_num)
return out
def create_loss(out,
feeds,
architecture,
classes_num=1000,
epsilon=None,
use_mix=False,
use_distillation=False):
"""
Create a loss for optimization, such as:
1. CrossEnotry loss
2. CrossEnotry loss with label smoothing
3. CrossEnotry loss with mix(mixup, cutmix, fmix)
4. CrossEnotry loss with label smoothing and (mixup, cutmix, fmix)
5. GoogLeNet loss
Args:
out(variable): model output variable
feeds(dict): dict of model input variables
architecture(dict): architecture information,
name(such as ResNet50) is needed
classes_num(int): num of classes
epsilon(float): parameter for label smoothing, 0.0 <= epsilon <= 1.0
use_mix(bool): whether to use mix(include mixup, cutmix, fmix)
Returns:
loss(variable): loss variable
"""
if architecture["name"] == "GoogLeNet":
assert len(out) == 3, "GoogLeNet should have 3 outputs"
loss = GoogLeNetLoss(class_dim=classes_num, epsilon=epsilon)
target = feeds['label']
return loss(out[0], out[1], out[2], target)
if use_distillation:
assert len(out) == 2, ("distillation output length must be 2, "
"but got {}".format(len(out)))
loss = JSDivLoss(class_dim=classes_num, epsilon=epsilon)
return loss(out[1], out[0])
if use_mix:
loss = MixCELoss(class_dim=classes_num, epsilon=epsilon)
feed_y_a = feeds['feed_y_a']
feed_y_b = feeds['feed_y_b']
feed_lam = feeds['feed_lam']
return loss(out, feed_y_a, feed_y_b, feed_lam)
else:
loss = CELoss(class_dim=classes_num, epsilon=epsilon)
target = feeds['label']
return loss(out, target)
def create_metric(out, feeds, topk=5, classes_num=1000,
use_distillation=False):
"""
Create measures of model accuracy, such as top1 and top5
Args:
out(variable): model output variable
feeds(dict): dict of model input variables(included label)
topk(int): usually top5
classes_num(int): num of classes
Returns:
fetchs(dict): dict of measures
"""
# just need student label to get metrics
if use_distillation:
out = out[1]
fetchs = OrderedDict()
label = feeds['label']
softmax_out = fluid.layers.softmax(out, use_cudnn=False)
top1 = fluid.layers.accuracy(softmax_out, label=label, k=1)
fetchs['top1'] = (top1, AverageMeter('top1', '.4f', need_avg=True))
k = min(topk, classes_num)
topk = fluid.layers.accuracy(softmax_out, label=label, k=k)
topk_name = 'top{}'.format(k)
fetchs[topk_name] = (topk, AverageMeter(topk_name, '.4f', need_avg=True))
return fetchs
def create_fetchs(out,
feeds,
architecture,
topk=5,
classes_num=1000,
epsilon=None,
use_mix=False,
use_distillation=False):
"""
Create fetchs as model outputs(included loss and measures),
will call create_loss and create_metric(if use_mix).
Args:
out(variable): model output variable
feeds(dict): dict of model input variables(included label)
architecture(dict): architecture information,
name(such as ResNet50) is needed
topk(int): usually top5
classes_num(int): num of classes
epsilon(float): parameter for label smoothing, 0.0 <= epsilon <= 1.0
use_mix(bool): whether to use mix(include mixup, cutmix, fmix)
Returns:
fetchs(dict): dict of model outputs(included loss and measures)
"""
fetchs = OrderedDict()
loss = create_loss(out, feeds, architecture, classes_num, epsilon, use_mix,
use_distillation)
fetchs['loss'] = (loss, AverageMeter('loss', '7.4f', need_avg=True))
if not use_mix:
metric = create_metric(out, feeds, topk, classes_num, use_distillation)
fetchs.update(metric)
return fetchs
def create_optimizer(config):
"""
Create an optimizer using config, usually including
learning rate and regularization.
Args:
config(dict): such as
{
'LEARNING_RATE':
{'function': 'Cosine',
'params': {'lr': 0.1}
},
'OPTIMIZER':
{'function': 'Momentum',
'params':{'momentum': 0.9},
'regularizer':
{'function': 'L2', 'factor': 0.0001}
}
}
Returns:
an optimizer instance
"""
# create learning_rate instance
lr_config = config['LEARNING_RATE']
lr_config['params'].update({
'epochs': config['epochs'],
'step_each_epoch':
config['total_images'] // config['TRAIN']['batch_size'],
})
lr = LearningRateBuilder(**lr_config)()
# create optimizer instance
opt_config = config['OPTIMIZER']
opt = OptimizerBuilder(**opt_config)
return opt(lr)
def dist_optimizer(config, optimizer):
"""
Create a distributed optimizer based on a normal optimizer
Args:
config(dict):
optimizer(): a normal optimizer
Returns:
optimizer: a distributed optimizer
"""
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 3
exec_strategy.num_iteration_per_drop_scope = 10
dist_strategy = DistributedStrategy()
dist_strategy.nccl_comm_num = 1
dist_strategy.fuse_all_reduce_ops = True
dist_strategy.exec_strategy = exec_strategy
optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
return optimizer
def build(config, main_prog, startup_prog, is_train=True):
"""
Build a program using a model and an optimizer
1. create feeds
2. create a dataloader
3. create a model
4. create fetchs
5. create an optimizer
Args:
config(dict): config
main_prog(): main program
startup_prog(): startup program
is_train(bool): train or valid
Returns:
dataloader(): a bridge between the model and the data
fetchs(dict): dict of model outputs(included loss and measures)
"""
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
use_mix = config.get('use_mix') and is_train
use_distillation = config.get('use_distillation')
feeds = create_feeds(config.image_shape, use_mix=use_mix)
dataloader = create_dataloader(feeds.values())
out = create_model(config.ARCHITECTURE, feeds['image'],
config.classes_num)
fetchs = create_fetchs(
out,
feeds,
config.ARCHITECTURE,
config.topk,
config.classes_num,
epsilon=config.get('ls_epsilon'),
use_mix=use_mix,
use_distillation=use_distillation)
if is_train:
optimizer = create_optimizer(config)
lr = optimizer._global_learning_rate()
fetchs['lr'] = (lr, AverageMeter('lr', 'f', need_avg=False))
optimizer = dist_optimizer(config, optimizer)
optimizer.minimize(fetchs['loss'][0])
return dataloader, fetchs
def compile(config, program, loss_name=None):
"""
Compile the program
Args:
config(dict): config
program(): the program which is wrapped by
loss_name(str): loss name
Returns:
compiled_program(): a compiled program
"""
build_strategy = fluid.compiler.BuildStrategy()
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 1
exec_strategy.num_iteration_per_drop_scope = 10
compiled_program = fluid.CompiledProgram(program).with_data_parallel(
loss_name=loss_name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
return compiled_program
def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
"""
Feed data to the model and fetch the measures and loss
Args:
dataloader(fluid dataloader):
exe():
program():
fetchs(dict): dict of measures and the loss
epoch(int): epoch of training or validation
model(str): log only
Returns:
"""
fetch_list = [f[0] for f in fetchs.values()]
metric_list = [f[1] for f in fetchs.values()]
for m in metric_list:
m.reset()
batch_time = AverageMeter('elapse', '.3f')
tic = time.time()
for idx, batch in enumerate(dataloader()):
metrics = exe.run(program=program, feed=batch, fetch_list=fetch_list)
batch_time.update(time.time() - tic)
tic = time.time()
for i, m in enumerate(metrics):
metric_list[i].update(m[0], len(batch[0]))
fetchs_str = ''.join([str(m.value) + ' '
for m in metric_list] + [batch_time.value])
if mode == 'valid':
logger.info("{:s} step:{:<4d} {:s}s".format(mode, idx, fetchs_str))
else:
logger.info("epoch:{:<3d} {:s} step:{:<4d} {:s}s".format(
epoch, mode, idx, fetchs_str))
end_str = ''.join([str(m.mean) + ' '
for m in metric_list] + [batch_time.total])
if mode == 'valid':
logger.info("END {:s} {:s}s".format(mode, end_str))
else:
logger.info("END epoch:{:<3d} {:s} {:s}s".format(epoch, mode, end_str))
# save the best model
if mode == 'valid':
top1_acc = fetchs["top1"][1].avg
return top1_acc