refine code and docs

ppcls/arch/utils.py

@@ -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

ppcls/data/preprocess/ops/operators.py

@@ -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

ppcls/loss/centerloss.py

@@ -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.

ppcls/optimizer/__init__.py

@@ -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)

ppcls/optimizer/learning_rate.py

@@ -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.