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Merge pull request #185 from littletomatodonkey/dyg_ls 

Add label smooth support for dygraph
pull/218/head
littletomatodonkey 2020-06-29 15:58:01 +08:00 committed by GitHub
parent fe302aec12 ac321194a5
commit 5d3fe63f6f
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 171 additions and 122 deletions
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3
ppcls/modeling/architectures/__init__.py
View File

@ -13,3 +13,6 @@
# limitations under the License.
from .resnet_name import *
from .dpn import DPN68
from .densenet import DenseNet121
from .hrnet import HRNet_W18_C

38
ppcls/modeling/architectures/densenet.py
View File

@ -1,3 +1,21 @@
# 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 numpy as np
import paddle
import paddle.fluid as fluid
@ -268,26 +286,26 @@ class DenseNet(fluid.dygraph.Layer):
return y
def DenseNet121():
model = DenseNet(layers=121)
def DenseNet121(**args):
model = DenseNet(layers=121, **args)
return model
def DenseNet161():
model = DenseNet(layers=161)
def DenseNet161(**args):
model = DenseNet(layers=161, **args)
return model
def DenseNet169():
model = DenseNet(layers=169)
def DenseNet169(**args):
model = DenseNet(layers=169, **args)
return model
def DenseNet201():
model = DenseNet(layers=201)
def DenseNet201(**args):
model = DenseNet(layers=201, **args)
return model
def DenseNet264():
model = DenseNet(layers=264)
def DenseNet264(**args):
model = DenseNet(layers=264, **args)
return model

38
ppcls/modeling/architectures/dpn.py
View File

@ -1,3 +1,21 @@
# 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 numpy as np
import sys
import paddle
@ -386,26 +404,26 @@ class DPN(fluid.dygraph.Layer):
return net_arg
def DPN68():
model = DPN(layers=68)
def DPN68(**args):
model = DPN(layers=68, **args)
return model
def DPN92():
model = DPN(layers=92)
def DPN92(**args):
model = DPN(layers=92, **args)
return model
def DPN98():
model = DPN(layers=98)
def DPN98(**args):
model = DPN(layers=98, **args)
return model
def DPN107():
model = DPN(layers=107)
def DPN107(**args):
model = DPN(layers=107, **args)
return model
def DPN131():
model = DPN(layers=131)
def DPN131(**args):
model = DPN(layers=131, **args)
return model

82
ppcls/modeling/architectures/hrnet.py
View File

@ -1,3 +1,21 @@
# 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 numpy as np
import paddle
import paddle.fluid as fluid
@ -647,81 +665,81 @@ class HRNet(fluid.dygraph.Layer):
return y
def HRNet_W18_C():
model = HRNet(width=18)
def HRNet_W18_C(**args):
model = HRNet(width=18, **args)
return model
def HRNet_W30_C():
model = HRNet(width=30)
def HRNet_W30_C(**args):
model = HRNet(width=30, **args)
return model
def HRNet_W32_C():
model = HRNet(width=32)
def HRNet_W32_C(**args):
model = HRNet(width=32, **args)
return model
def HRNet_W40_C():
model = HRNet(width=40)
def HRNet_W40_C(**args):
model = HRNet(width=40, **args)
return model
def HRNet_W44_C():
model = HRNet(width=44)
def HRNet_W44_C(**args):
model = HRNet(width=44, **args)
return model
def HRNet_W48_C():
model = HRNet(width=48)
def HRNet_W48_C(**args):
model = HRNet(width=48, **args)
return model
def HRNet_W60_C():
model = HRNet(width=60)
def HRNet_W60_C(**args):
model = HRNet(width=60, **args)
return model
def HRNet_W64_C():
model = HRNet(width=64)
def HRNet_W64_C(**args):
model = HRNet(width=64, **args)
return model
def SE_HRNet_W18_C():
model = HRNet(width=18, has_se=True)
def SE_HRNet_W18_C(**args):
model = HRNet(width=18, has_se=True, **args)
return model
def SE_HRNet_W30_C():
model = HRNet(width=30, has_se=True)
def SE_HRNet_W30_C(**args):
model = HRNet(width=30, has_se=True, **args)
return model
def SE_HRNet_W32_C():
model = HRNet(width=32, has_se=True)
def SE_HRNet_W32_C(**args):
model = HRNet(width=32, has_se=True, **args)
return model
def SE_HRNet_W40_C():
model = HRNet(width=40, has_se=True)
def SE_HRNet_W40_C(**args):
model = HRNet(width=40, has_se=True, **args)
return model
def SE_HRNet_W44_C():
model = HRNet(width=44, has_se=True)
def SE_HRNet_W44_C(**args):
model = HRNet(width=44, has_se=True, **args)
return model
def SE_HRNet_W48_C():
model = HRNet(width=48, has_se=True)
def SE_HRNet_W48_C(**args):
model = HRNet(width=48, has_se=True, **args)
return model
def SE_HRNet_W60_C():
model = HRNet(width=60, has_se=True)
def SE_HRNet_W60_C(**args):
model = HRNet(width=60, has_se=True, **args)
return model
def SE_HRNet_W64_C():
model = HRNet(width=64, has_se=True)
def SE_HRNet_W64_C(**args):
model = HRNet(width=64, has_se=True, **args)
return model

132
tools/program.py
View File

@ -49,11 +49,9 @@ def create_dataloader():
dataloader(fluid dataloader):
"""
trainer_num = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
capacity = 64 if trainer_num <= 1 else 8
capacity = 64 if trainer_num == 1 else 8
dataloader = fluid.io.DataLoader.from_generator(
capacity=capacity,
use_double_buffer=True,
iterable=True)
capacity=capacity, use_double_buffer=True, iterable=True)
return dataloader
@ -76,8 +74,8 @@ def create_model(architecture, classes_num):
return architectures.__dict__[name](class_dim=classes_num, **params)
def create_loss(out,
label,
def create_loss(feeds,
out,
architecture,
classes_num=1000,
epsilon=None,
@ -106,7 +104,7 @@ def create_loss(out,
if architecture["name"] == "GoogLeNet":
assert len(out) == 3, "GoogLeNet should have 3 outputs"
loss = GoogLeNetLoss(class_dim=classes_num, epsilon=epsilon)
return loss(out[0], out[1], out[2], label)
return loss(out[0], out[1], out[2], feeds["label"])
if use_distillation:
assert len(out) == 2, ("distillation output length must be 2, "
@ -116,14 +114,13 @@ def create_loss(out,
if use_mix:
loss = MixCELoss(class_dim=classes_num, epsilon=epsilon)
raise NotImplementedError
#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)
feed_y_a = feeds['y_a']
feed_y_b = feeds['y_b']
feed_lam = feeds['lam']
return loss(out, feed_y_a, feed_y_b, feed_lam)
else:
loss = CELoss(class_dim=classes_num, epsilon=epsilon)
return loss(out, label)
return loss(out, feeds["label"])
def create_metric(out,
@ -166,14 +163,7 @@ def create_metric(out,
return fetchs
def create_fetchs(out,
label,
architecture,
topk=5,
classes_num=1000,
epsilon=None,
use_mix=False,
use_distillation=False):
def create_fetchs(feeds, net, config, mode="train"):
"""
Create fetchs as model outputs(included loss and measures),
will call create_loss and create_metric(if use_mix).
@ -192,12 +182,21 @@ def create_fetchs(out,
Returns:
fetchs(dict): dict of model outputs(included loss and measures)
"""
architecture = config.ARCHITECTURE
topk = config.topk
classes_num = config.classes_num
epsilon = config.get('ls_epsilon')
use_mix = config.get('use_mix') and mode == 'train'
use_distillation = config.get('use_distillation')
out = net(feeds["image"])
fetchs = OrderedDict()
fetchs['loss'] = create_loss(out, label, architecture, classes_num, epsilon, use_mix,
use_distillation)
fetchs['loss'] = create_loss(feeds, out, architecture, classes_num,
epsilon, use_mix, use_distillation)
if not use_mix:
metric = create_metric(out, label, architecture, topk, classes_num,
use_distillation)
metric = create_metric(out, feeds["label"], architecture, topk,
classes_num, use_distillation)
fetchs.update(metric)
return fetchs
@ -278,36 +277,17 @@ def mixed_precision_optimizer(config, optimizer):
return optimizer
def compute(config, out, label, mode='train'):
"""
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)
"""
fetchs = create_fetchs(
out,
label,
config.ARCHITECTURE,
config.topk,
config.classes_num,
epsilon=config.get('ls_epsilon'),
use_mix=config.get('use_mix') and mode == 'train',
use_distillation=config.get('use_distillation'))
return fetchs
def create_feeds(batch, use_mix):
image = to_variable(batch[0].numpy().astype("float32"))
if use_mix:
y_a = to_variable(batch[1].numpy().astype("int64").reshape(-1, 1))
y_b = to_variable(batch[2].numpy().astype("int64").reshape(-1, 1))
lam = to_variable(batch[3].numpy().astype("float32").reshape(-1, 1))
feeds = {"image": image, "y_a": y_a, "y_b": y_b, "lam": lam}
else:
label = to_variable(batch[1].numpy().astype('int64').reshape(-1, 1))
feeds = {"image": image, "label": label}
return feeds
def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
@ -324,19 +304,30 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
Returns:
"""
topk_name = 'top{}'.format(config.topk)
metric_list = OrderedDict([
("loss", AverageMeter('loss', '7.4f')),
("top1", AverageMeter('top1', '.4f')),
(topk_name, AverageMeter(topk_name, '.4f')),
("lr", AverageMeter('lr', 'f', need_avg=False)),
("batch_time", AverageMeter('elapse', '.3f')),
])
use_mix = config.get("use_mix", False) and mode == "train"
if use_mix:
metric_list = OrderedDict([
("loss", AverageMeter('loss', '7.4f')),
("lr", AverageMeter(
'lr', 'f', need_avg=False)),
("batch_time", AverageMeter('elapse', '.3f')),
])
else:
topk_name = 'top{}'.format(config.topk)
metric_list = OrderedDict([
("loss", AverageMeter('loss', '7.4f')),
("top1", AverageMeter('top1', '.4f')),
(topk_name, AverageMeter(topk_name, '.4f')),
("lr", AverageMeter(
'lr', 'f', need_avg=False)),
("batch_time", AverageMeter('elapse', '.3f')),
])
tic = time.time()
for idx, (img, label) in enumerate(dataloader()):
label = to_variable(label.numpy().astype('int64').reshape(-1, 1))
fetchs = compute(config, net(img), label, mode)
for idx, batch in enumerate(dataloader()):
batch_size = len(batch[0])
feeds = create_feeds(batch, use_mix)
fetchs = create_fetchs(feeds, net, config, mode)
if mode == 'train':
avg_loss = net.scale_loss(fetchs['loss'])
avg_loss.backward()
@ -345,10 +336,10 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
optimizer.minimize(avg_loss)
net.clear_gradients()
metric_list['lr'].update(
optimizer._global_learning_rate().numpy()[0], len(img))
optimizer._global_learning_rate().numpy()[0], batch_size)
for name, fetch in fetchs.items():
metric_list[name].update(fetch.numpy()[0], len(img))
metric_list[name].update(fetch.numpy()[0], batch_size)
metric_list['batch_time'].update(time.time() - tic)
tic = time.time()
@ -365,7 +356,8 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
logger.coloring(step_str, "PURPLE"),
logger.coloring(fetchs_str, 'OKGREEN')))
end_str = ' '.join([str(m.mean) for m in metric_list.values()] + [metric_list['batch_time'].total])
end_str = ' '.join([str(m.mean) for m in metric_list.values()] +
[metric_list['batch_time'].total])
if mode == 'eval':
logger.info("END {:s} {:s}s".format(mode, end_str))
else:
@ -378,4 +370,4 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
# return top1_acc in order to save the best model
if mode == 'valid':
return metric_list['top1'].avg
return metric_list['top1'].avg