Merge pull request #1285 from will-jl944/cls_dev

add pplcnet doc

docs/apis/models/classification.md

@@ -25,7 +25,7 @@ paddlex.cls.ResNet50(num_classes=1000)
 ### <h3 id="11">train</h3>
 
 ```python
-train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, optimizer=None, save_interval_epochs=1, log_interval_steps=10, save_dir='output', pretrain_weights='IMAGENET', learning_rate=.025, warmup_steps=0, warmup_start_lr=0.0, lr_decay_epochs=(30, 60, 90), lr_decay_gamma=0.1, early_stop=False, early_stop_patience=5, use_vdl=True)
+train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, optimizer=None, save_interval_epochs=1, log_interval_steps=10, save_dir='output', pretrain_weights='IMAGENET', learning_rate=.025, warmup_steps=0, warmup_start_lr=0.0, lr_decay_epochs=(30, 60, 90), lr_decay_gamma=0.1, label_smoothing=None, early_stop=False, early_stop_patience=5, use_vdl=True)
 ```
 >
 > **参数**
@@ -44,6 +44,7 @@ train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, o
 - **warmup_start_lr**(float): 默认优化器的warmup起始学习率，默认为0.0。
 - **lr_decay_epochs** (list): 默认优化器的学习率衰减轮数。默认为[30, 60, 90]。
 - **lr_decay_gamma** (float): 默认优化器的学习率衰减率。默认为0.1。
+- **label_smoothing** (float, bool or None): 是否使用标签平滑。若为float，表示标签平滑系数。若为True，使用系数为0.1的标签平滑。若为None或False，则不采用标签平滑。默认为None。
 - **early_stop** (bool): 是否使用提前终止训练策略。默认为False。
 - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认为5。
 - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认为True。
@@ -171,43 +172,45 @@ quant_aware_train(self, num_epochs, train_dataset, train_batch_size=64, eval_dat
 
 PaddleX提供了共计38种分类模型，所有分类模型均提供同`ResNet50`相同的训练`train`，评估`evaluate`，预测`predict`，敏感度分析`analyze_sensitivity`，剪裁`prune`和在线量化`quant_aware_train`接口，各模型效果可参考[模型库](../../appendix/model_zoo.md)。
 
-| 模型              | 接口                    |
-| :---------------- | :---------------------- |
-| ResNet18          | paddlex.cls.ResNet18(num_classes=1000) |
-| ResNet18_vd       | paddlex.cls.ResNet18_vd(num_classes=1000) |
-| ResNet34          | paddlex.cls.ResNet34(num_classes=1000) |
-| ResNet34_vd          | paddlex.cls.ResNet34_vd(num_classes=1000) |
-| ResNet50          | paddlex.cls.ResNet50(num_classes=1000) |
-| ResNet50_vd       | paddlex.cls.ResNet50_vd(num_classes=1000) |
-| ResNet50_vd_ssld    | paddlex.cls.ResNet50_vd_ssld(num_classes=1000) |
-| ResNet101          | paddlex.cls.ResNet101(num_classes=1000) |
-| ResNet101_vd        | paddlex.cls.ResNet101_vd(num_classes=1000) |
-| ResNet101_vd_ssld   | paddlex.cls.ResNet101_vd_ssld(num_classes=1000) |
-| ResNet152 | paddlex.cls.ResNet152(num_classes=1000) |
-| ResNet152_vd | paddlex.cls.ResNet152_vd(num_classes=1000) |
-| ResNet200_vd | paddlex.cls.ResNet200_vd(num_classes=1000) |
-| DarkNet53      | paddlex.cls.DarkNet53(num_classes=1000) |
-| MobileNetV1         | paddlex.cls.MobileNetV1(num_classes=1000, scale=1.0) |
-| MobileNetV2       | paddlex.cls.MobileNetV2(num_classes=1000, scale=1.0) |
-| MobileNetV3_small       | paddlex.cls.MobileNetV3_small(num_classes=1000, scale=1.0) |
-| MobileNetV3_small_ssld  | paddlex.cls.MobileNetV3_small_ssld(num_classes=1000, scale=1.0) |
-| MobileNetV3_large   | paddlex.cls.MobileNetV3_large(num_classes=1000, scale=1.0) |
-| MobileNetV3_large_ssld | paddlex.cls.MobileNetV3_large_ssld(num_classes=1000) |
-| Xception41     | paddlex.cls.Xception41(num_classes=1000) |
-| Xception65     | paddlex.cls.Xception65(num_classes=1000) |
-| Xception71     | paddlex.cls.Xception71(num_classes=1000) |
-| ShuffleNetV2     | paddlex.cls.ShuffleNetV2(num_classes=1000, scale=1.0) |
-| ShuffleNetV2_swish     | paddlex.cls.ShuffleNetV2_swish(num_classes=1000) |
-| DenseNet121      | paddlex.cls.DenseNet121(num_classes=1000) |
-| DenseNet161       | paddlex.cls.DenseNet161(num_classes=1000) |
-| DenseNet169       | paddlex.cls.DenseNet169(num_classes=1000) |
-| DenseNet201       | paddlex.cls.DenseNet201(num_classes=1000) |
-| DenseNet264       | paddlex.cls.DenseNet264(num_classes=1000) |
-| HRNet_W18_C       | paddlex.cls.HRNet_W18_C(num_classes=1000) |
-| HRNet_W30_C       | paddlex.cls.HRNet_W30_C(num_classes=1000) |
-| HRNet_W32_C       | paddlex.cls.HRNet_W32_C(num_classes=1000) |
-| HRNet_W40_C       | paddlex.cls.HRNet_W40_C(num_classes=1000) |
-| HRNet_W44_C       | paddlex.cls.HRNet_W44_C(num_classes=1000) |
-| HRNet_W48_C       | paddlex.cls.HRNet_W48_C(num_classes=1000) |
-| HRNet_W64_C       | paddlex.cls.HRNet_W64_C(num_classes=1000) |
-| AlexNet         | paddlex.cls.AlexNet(num_classes=1000) |
+| 模型                     | 接口                                                              |
+|:-----------------------|:----------------------------------------------------------------|
+| PPLCNet                | paddlex.cls.PPLCNet(num_classes=1000)                           |
+| PPLCNet_ssld                | paddlex.cls.PPLCNet_ssld(num_classes=1000)                      |
+| ResNet18               | paddlex.cls.ResNet18(num_classes=1000)                          |
+| ResNet18_vd            | paddlex.cls.ResNet18_vd(num_classes=1000)                       |
+| ResNet34               | paddlex.cls.ResNet34(num_classes=1000)                          |
+| ResNet34_vd            | paddlex.cls.ResNet34_vd(num_classes=1000)                       |
+| ResNet50               | paddlex.cls.ResNet50(num_classes=1000)                          |
+| ResNet50_vd            | paddlex.cls.ResNet50_vd(num_classes=1000)                       |
+| ResNet50_vd_ssld       | paddlex.cls.ResNet50_vd_ssld(num_classes=1000)                  |
+| ResNet101              | paddlex.cls.ResNet101(num_classes=1000)                         |
+| ResNet101_vd           | paddlex.cls.ResNet101_vd(num_classes=1000)                      |
+| ResNet101_vd_ssld      | paddlex.cls.ResNet101_vd_ssld(num_classes=1000)                 |
+| ResNet152              | paddlex.cls.ResNet152(num_classes=1000)                         |
+| ResNet152_vd           | paddlex.cls.ResNet152_vd(num_classes=1000)                      |
+| ResNet200_vd           | paddlex.cls.ResNet200_vd(num_classes=1000)                      |
+| DarkNet53              | paddlex.cls.DarkNet53(num_classes=1000)                         |
+| MobileNetV1            | paddlex.cls.MobileNetV1(num_classes=1000, scale=1.0)            |
+| MobileNetV2            | paddlex.cls.MobileNetV2(num_classes=1000, scale=1.0)            |
+| MobileNetV3_small      | paddlex.cls.MobileNetV3_small(num_classes=1000, scale=1.0)      |
+| MobileNetV3_small_ssld | paddlex.cls.MobileNetV3_small_ssld(num_classes=1000, scale=1.0) |
+| MobileNetV3_large      | paddlex.cls.MobileNetV3_large(num_classes=1000, scale=1.0)      |
+| MobileNetV3_large_ssld | paddlex.cls.MobileNetV3_large_ssld(num_classes=1000)            |
+| Xception41             | paddlex.cls.Xception41(num_classes=1000)                        |
+| Xception65             | paddlex.cls.Xception65(num_classes=1000)                        |
+| Xception71             | paddlex.cls.Xception71(num_classes=1000)                        |
+| ShuffleNetV2           | paddlex.cls.ShuffleNetV2(num_classes=1000, scale=1.0)           |
+| ShuffleNetV2_swish     | paddlex.cls.ShuffleNetV2_swish(num_classes=1000)                |
+| DenseNet121            | paddlex.cls.DenseNet121(num_classes=1000)                       |
+| DenseNet161            | paddlex.cls.DenseNet161(num_classes=1000)                       |
+| DenseNet169            | paddlex.cls.DenseNet169(num_classes=1000)                       |
+| DenseNet201            | paddlex.cls.DenseNet201(num_classes=1000)                       |
+| DenseNet264            | paddlex.cls.DenseNet264(num_classes=1000)                       |
+| HRNet_W18_C            | paddlex.cls.HRNet_W18_C(num_classes=1000)                       |
+| HRNet_W30_C            | paddlex.cls.HRNet_W30_C(num_classes=1000)                       |
+| HRNet_W32_C            | paddlex.cls.HRNet_W32_C(num_classes=1000)                       |
+| HRNet_W40_C            | paddlex.cls.HRNet_W40_C(num_classes=1000)                       |
+| HRNet_W44_C            | paddlex.cls.HRNet_W44_C(num_classes=1000)                       |
+| HRNet_W48_C            | paddlex.cls.HRNet_W48_C(num_classes=1000)                       |
+| HRNet_W64_C            | paddlex.cls.HRNet_W64_C(num_classes=1000)                       |
+| AlexNet                | paddlex.cls.AlexNet(num_classes=1000)                           |