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rewrite the misc to solve the display log

pull/63/head
WuHaobo 2020-04-22 12:36:56 +08:00
parent 65d3820660
commit fa8f585bee
3 changed files with 67 additions and 64 deletions

31
.pre-commit-config.yaml
View File

@ -9,21 +9,6 @@
hooks:
- id: autopep8
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.5.0
hooks:
- id: flake8
args: ['--ignore=E265']
- id: check-yaml
- id: check-merge-conflict
- id: detect-private-key
files: (?!.*paddle)^.*$
- id: end-of-file-fixer
files: \.(md|yml)$
- id: trailing-whitespace
files: \.(md|yml)$
- id: check-case-conflict
- repo: https://github.com/Lucas-C/pre-commit-hooks
sha: v1.0.1
hooks:
@ -35,3 +20,19 @@
files: \.(md|yml)$
- id: remove-tabs
files: \.(md|yml)$
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.5.0
hooks:
- id: check-yaml
- id: check-merge-conflict
- id: detect-private-key
files: (?!.*paddle)^.*$
- id: end-of-file-fixer
files: \.(md|yml)$
- id: trailing-whitespace
files: \.(md|yml)$
- id: check-case-conflict
- id: flake8
args: ['--ignore=E265']

39
ppcls/utils/misc.py
View File

@ -1,16 +1,16 @@
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# 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
# 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.
# 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.
__all__ = ['AverageMeter']
@ -20,10 +20,10 @@ class AverageMeter(object):
Computes and stores the average and current value
"""
def __init__(self, name='', fmt=':f', avg=False):
def __init__(self, name='', fmt='f', need_avg=False):
self.name = name
self.fmt = fmt
self.avg_flag = avg
self.need_avg = need_avg
self.reset()
def reset(self):
@ -40,8 +40,15 @@ class AverageMeter(object):
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '[{name}: {val' + self.fmt + '}]'
if self.avg_flag:
fmtstr += '[{name}(avg): {avg' + self.fmt + '}]'
return fmtstr.format(**self.__dict__)
@property
def total(self):
return '[{self.name}_sum: {self.sum:{self.fmt}}]'.format(self=self)
@property
def mean(self):
return '[{self.name}_avg: {self.avg:{self.fmt}}]'.format(
self=self) if self.need_avg else ''
@property
def value(self):
return '[{self.name}: {self.val:{self.fmt}}]'.format(self=self)

61
tools/program.py
View File

@ -1,33 +1,30 @@
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
# 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
# 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.
# 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 sys
import time
from collections import OrderedDict
import paddle
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
@ -94,7 +91,8 @@ def create_model(architecture, image, classes_num):
Create a model
Args:
architecture(dict): architecture information, name(such as ResNet50) is needed
architecture(dict): architecture information,
name(such as ResNet50) is needed
image(variable): model input variable
classes_num(int): num of classes
@ -126,7 +124,8 @@ def create_loss(out,
Args:
out(variable): model output variable
feeds(dict): dict of model input variables
architecture(dict): architecture information, name(such as ResNet50) is needed
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)
@ -141,9 +140,8 @@ def create_loss(out,
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))
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])
@ -180,11 +178,11 @@ def create_metric(out, feeds, topk=5, classes_num=1000,
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', ':2.4f', True))
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, ':2.4f', True))
fetchs[topk_name] = (topk, AverageMeter(topk_name, '.4f', need_avg=True))
return fetchs
@ -204,7 +202,8 @@ def create_fetchs(out,
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
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
@ -216,7 +215,7 @@ def create_fetchs(out,
fetchs = OrderedDict()
loss = create_loss(out, feeds, architecture, classes_num, epsilon, use_mix,
use_distillation)
fetchs['loss'] = (loss, AverageMeter('loss', ':2.4f', True))
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)
@ -325,7 +324,7 @@ def build(config, main_prog, startup_prog, is_train=True):
if is_train:
optimizer = create_optimizer(config)
lr = optimizer._global_learning_rate()
fetchs['lr'] = (lr, AverageMeter('lr', ':f', False))
fetchs['lr'] = (lr, AverageMeter('lr', 'f', need_avg=False))
optimizer = dist_optimizer(config, optimizer)
optimizer.minimize(fetchs['loss'][0])
@ -345,8 +344,6 @@ def compile(config, program, loss_name=None):
compiled_program(): a compiled program
"""
build_strategy = fluid.compiler.BuildStrategy()
#build_strategy.fuse_bn_act_ops = config.get("fuse_bn_act_ops")
#build_strategy.fuse_elewise_add_act_ops = config.get("fuse_elewise_add_act_ops")
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 1
@ -378,19 +375,17 @@ def run(dataloader, exe, program, fetchs, epoch=0, mode='train'):
metric_list = [f[1] for f in fetchs.values()]
for m in metric_list:
m.reset()
batch_time = AverageMeter('cost', ':6.3f')
batch_time = AverageMeter('cost', '.3f')
tic = time.time()
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0))
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) for m in metric_list] + [str(batch_time)])
if trainer_id == 0:
logger.info("[epoch:%3d][%s][step:%4d]%s" %
(epoch, mode, idx, fetchs_str))
if trainer_id == 0:
logger.info("END [epoch:%3d][%s]%s"%(epoch, mode, fetchs_str))
fetchs_str = ''.join([m.value
for m in metric_list] + [batch_time.value])
logger.info("[epoch:{:3d}][{:s}][step:{:4d}]{:s}".format(
epoch, mode, idx, fetchs_str))
end_str = ''.join([m.mean for m in metric_list] + [batch_time.total])
logger.info("END [epoch:{:3d}][{:s}]{:s}".format(epoch, mode, end_str))