add efficientnet-lite model

configs/EfficientNet/EfficientLite0.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: "EfficientNetLite0"
+    params:
+        is_test: False
+        padding_type : "SAME"
+        override_params:
+            drop_connect_rate: 0.1
+            fix_head_stem: True
+            relu_fn: True
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 360
+topk: 5
+image_shape: [3, 224, 224]
+use_ema: True
+ema_decay: 0.9999
+use_aa: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+    function: 'ExponentialWarmup'
+    params:
+        lr: 0.032
+
+OPTIMIZER:
+    function: 'RMSProp'
+    params:
+        momentum: 0.9
+        rho: 0.9
+        epsilon: 0.001
+    regularizer:
+        function: 'L2'
+        factor: 0.00001
+
+TRAIN:
+    batch_size: 512
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/train_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+            interpolation: 2
+        - RandFlipImage:
+            flip_code: 1
+        - AutoAugment:
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+
+
+VALID:
+    batch_size: 128
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - ResizeImage:
+            interpolation: 2
+            resize_short: 412
+        - CropImage:
+            size: 256
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+
+

configs/EfficientNet/EfficientNetB0.yaml

@@ -46,7 +46,7 @@ TRAIN:
     transforms:
         - DecodeImage:
             to_rgb: True
-            to_np: Fals
+            to_np: False
             channel_first: False
         - RandCropImage:
             size: 224

ppcls/modeling/architectures/__init__.py

@@ -37,6 +37,9 @@ from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .darknet import DarkNet53
 from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
 from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB0_small, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
+
+from .efficientnetlite import EfficientNetLite, EfficientNetLite0, EfficientNetLite1, EfficientNetLite2, EfficientNetLite4
+
 from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s
 from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
 from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C

ppcls/modeling/architectures/layers.py

-#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
@@ -242,6 +242,8 @@ def conv2d(input,
         conv = fluid.layers.sigmoid(conv, name=name + '_sigmoid')
     elif act == 'swish':
         conv = fluid.layers.swish(conv, name=name + '_swish')
+    elif act == 'relu6':
+        conv = fluid.layers.relu6(conv, name=name + '_relu6')
     elif act == None:
         conv = conv
     else: