Unify model name (#3316)

PaddleCV/image_classification/README.md

@@ -334,7 +334,7 @@ PaddlePaddle/Models ImageClassification 支持自定义数据
 |[SE_ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) | 79.52% | 94.75% | 10.345 | 7.662 |
 |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44% | 93.96% | 14.916 | 12.126 |
 |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12% | 94.20% | 30.085 | 24.110 |
-|[SENet_154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SENet_154_vd_pretrained.tar) | 81.40% | 95.48% | 71.892 | 64.855 |
+|[SENet154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) | 81.40% | 95.48% | 71.892 | 64.855 |
 
 ### Inception Series
 | Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |

PaddleCV/image_classification/README_en.md

@@ -316,7 +316,7 @@ Pretrained models can be downloaded by clicking related model names.
 |[SE_ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNet50_vd_pretrained.tar) | 79.52% | 94.75% | 10.345 | 7.662 |
 |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44% | 93.96% | 14.916 | 12.126 |
 |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12% | 94.20% | 30.085 | 24.110 |
-|[SENet_154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SENet_154_vd_pretrained.tar) | 81.40% | 95.48% | 71.892 | 64.855 |
+|[SENet154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SENet154_vd_pretrained.tar) | 81.40% | 95.48% | 71.892 | 64.855 |
 
 ### Inception Series
 | Model | Top-1 | Top-5 | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |

PaddleCV/image_classification/models/__init__.py

@@ -13,8 +13,8 @@
 #limitations under the License.
 
 from .alexnet import AlexNet
-from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
-from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
+from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x1_0, MobileNetV1_x0_75, MobileNetV1
+from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2_x1_0, MobileNetV2_x1_5, MobileNetV2_x2_0, MobileNetV2
 from .mobilenet_v3 import MobileNetV3_small_x0_25, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_25, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
 from .googlenet import GoogLeNet
 from .vgg import VGG11, VGG13, VGG16, VGG19
@@ -32,7 +32,7 @@ from .shufflenet_v2_swish import ShuffleNetV2_swish, ShuffleNetV2_x0_5_swish, Sh
 from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2
 from .fast_imagenet import FastImageNet
 from .xception import Xception41, Xception65, Xception71
-from .xception_deeplab import Xception41_deeplab , Xception65_deeplab , Xception71_deeplab 
+from .xception_deeplab import Xception41_deeplab, Xception65_deeplab, Xception71_deeplab
 from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
 from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .darknet import DarkNet53

PaddleCV/image_classification/models/mobilenet_v1.py

@@ -20,7 +20,10 @@ import paddle.fluid as fluid
 from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ['MobileNetV1', 'MobileNetV1_x0_25', 'MobileNetV1_x0_5', 'MobileNetV1_x0_75']
+__all__ = [
+    'MobileNetV1', 'MobileNetV1_x0_25', 'MobileNetV1_x0_5', 'MobileNetV1_x1_0',
+    'MobileNetV1_x0_75'
+]
 
 
 class MobileNetV1():
@@ -205,6 +208,11 @@ def MobileNetV1_x0_5():
     return model
 
 
+def MobileNetV1_x1_0():
+    model = MobileNetV1(scale=1.0)
+    return model
+
+
 def MobileNetV1_x0_75():
     model = MobileNetV1(scale=0.75)
     return model

PaddleCV/image_classification/models/mobilenet_v2.py

@@ -21,8 +21,8 @@ from paddle.fluid.param_attr import ParamAttr
 
 __all__ = [
     'MobileNetV2_x0_25', 'MobileNetV2_x0_5'
-    'MobileNetV2_x0_75', 'MobileNetV2', 'MobileNetV2_x1_5',
-    'MobileNetV2_x2_0', 
+    'MobileNetV2_x0_75', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5',
+    'MobileNetV2_x2_0', 'MobileNetV2'
 ]
 
 
@@ -215,12 +215,11 @@ def MobileNetV2_x0_75():
     return model
 
 
-def MobileNetV2():
+def MobileNetV2_x1_0():
     model = MobileNetV2(scale=1.0)
     return model
 
 
-
 def MobileNetV2_x1_5():
     model = MobileNetV2(scale=1.5)
     return model
@@ -229,3 +228,8 @@ def MobileNetV2_x1_5():
 def MobileNetV2_x2_0():
     model = MobileNetV2(scale=2.0)
     return model
+
+
+def MobileNetV2():
+    model = MobileNetV2(scale=1.0)
+    return model

PaddleCV/image_classification/scripts/train/MobileNetV1.sh

@@ -7,7 +7,7 @@ export FLAGS_fraction_of_gpu_memory_to_use=0.98
 
 
 python train.py \
-       --model=MobileNetV1_x1_0 \
+       --model=MobileNetV1 \
        --batch_size=256 \
        --total_images=1281167 \
        --class_dim=1000 \

PaddleCV/image_classification/scripts/train/MobileNetV2.sh

@@ -7,7 +7,7 @@ export FLAGS_fraction_of_gpu_memory_to_use=0.98
 
 
 python train.py \
-	--model=MobileNetV2_x1_0 \
+	--model=MobileNetV2 \
 	--batch_size=500 \
 	--total_images=1281167 \
 	--class_dim=1000 \

README.md

@@ -40,7 +40,7 @@ PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化
 | [Inceptionv4](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | 将Inception模块与Residual Connection进行结合，通过ResNet的结构极大地加速训练并获得性能的提升 | ImageNet-2012验证集 | 80.77%/95.26% |
 | [MobileNetV1](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | 将传统的卷积结构改造成两层卷积结构的网络，在基本不影响准确率的前提下大大减少计算时间，更适合移动端和嵌入式视觉应用 | ImageNet-2012验证集 | 70.99%/89.68% |
 | [MobileNetV2](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | MobileNet结构的微调，直接在thinner的bottleneck层上进行skip learning连接以及对bottleneck layer不进行ReLu非线性处理可取得更好的结果 | ImageNet-2012验证集 | 72.15%/90.65% |
-| [SENet_154_vd](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | 在ResNeXt 基础、上加入了SE（Sequeeze-and-Excitation）模块，提高了识别准确率，在ILSVRC 2017 的分类项目中取得了第一名 | ImageNet-2012验证集 | 81.40%/95.48% |
+| [SENet154_vd](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | 在ResNeXt 基础、上加入了SE（Sequeeze-and-Excitation）模块，提高了识别准确率，在ILSVRC 2017 的分类项目中取得了第一名 | ImageNet-2012验证集 | 81.40%/95.48% |
 | [ShuffleNetV2](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification) | ECCV2018，轻量级CNN网络，在速度和准确度之间做了很好地平衡。在同等复杂度下，比ShuffleNet和MobileNetv2更准确，更适合移动端以及无人车领域 | ImageNet-2012验证集 | 70.03%/89.17% |
 
 更多图像分类模型请参考[Image Classification](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification)