refine code and docs

pull/1819/head
HydrogenSulfate 2022-05-05 22:14:07 +08:00
parent 1c31010b14
commit 790815f430
5 changed files with 11 additions and 29 deletions
ppcls

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@ -33,8 +33,8 @@ def get_architectures():
def get_blacklist_model_in_static_mode():
from ppcls.arch.backbone import (distilled_vision_transformer,
vision_transformer)
from ppcls.arch.backbone import distilled_vision_transformer
from ppcls.arch.backbone import vision_transformer
blacklist = distilled_vision_transformer.__all__ + vision_transformer.__all__
return blacklist
@ -60,10 +60,10 @@ def get_param_attr_dict(ParamAttr_config: Union[None, bool, Dict[str, Dict]]
"""parse ParamAttr from an dict
Args:
ParamAttr_config (Union[bool, Dict[str, Dict]]): ParamAttr_config
ParamAttr_config (Union[None, bool, Dict[str, Dict]]): ParamAttr configure
Returns:
Union[bool, paddle.ParamAttr]: Generated ParamAttr
Union[None, bool, paddle.ParamAttr]: Generated ParamAttr
"""
if ParamAttr_config is None:
return None

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@ -22,7 +22,6 @@ import six
import math
import random
import cv2
from typing import Sequence
import numpy as np
from PIL import Image, ImageOps, __version__ as PILLOW_VERSION
from paddle.vision.transforms import ColorJitter as RawColorJitter

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@ -23,8 +23,9 @@ import paddle.nn as nn
class CenterLoss(nn.Layer):
"""Center loss class
"""Center loss
paper : [A Discriminative Feature Learning Approach for Deep Face Recognition](https://link.springer.com/content/pdf/10.1007%2F978-3-319-46478-7_31.pdf)
code reference: https://github.com/michuanhaohao/reid-strong-baseline/blob/master/layers/center_loss.py#L7
Args:
num_classes (int): number of classes.
feat_dim (int): number of feature dimensions.

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@ -71,7 +71,7 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
optim_cfg = optim_item[optim_name] # get optim_cfg
lr = build_lr_scheduler(optim_cfg.pop('lr'), epochs, step_each_epoch)
logger.info("build lr ({}) for scope ({}) success..".format(
logger.debug("build lr ({}) for scope ({}) success..".format(
lr, optim_scope))
# step2 build regularization
if 'regularizer' in optim_cfg and optim_cfg['regularizer'] is not None:
@ -83,8 +83,8 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
reg_name = reg_config.pop('name') + 'Decay'
reg = getattr(paddle.regularizer, reg_name)(**reg_config)
optim_cfg["weight_decay"] = reg
logger.info("build regularizer ({}) for scope ({}) success..".
format(reg, optim_scope))
logger.debug("build regularizer ({}) for scope ({}) success..".
format(reg, optim_scope))
# step3 build optimizer
if 'clip_norm' in optim_cfg:
clip_norm = optim_cfg.pop('clip_norm')
@ -123,7 +123,7 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
optim = getattr(optimizer, optim_name)(
learning_rate=lr, grad_clip=grad_clip,
**optim_cfg)(model_list=optim_model)
logger.info("build optimizer ({}) for scope ({}) success..".format(
logger.debug("build optimizer ({}) for scope ({}) success..".format(
optim, optim_scope))
optim_list.append(optim)
lr_list.append(lr)

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@ -262,24 +262,6 @@ class Piecewise(object):
return learning_rate
class Constant(LRScheduler):
"""
Constant learning rate
Args:
lr (float): The initial learning rate. It is a python float number.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
"""
def __init__(self, learning_rate, last_epoch=-1, by_epoch=False, **kwargs):
self.learning_rate = learning_rate
self.last_epoch = last_epoch
self.by_epoch = by_epoch
super().__init__()
def get_lr(self):
return self.learning_rate
class MultiStepDecay(LRScheduler):
"""
Update the learning rate by ``gamma`` once ``epoch`` reaches one of the milestones.