Add mobile-side rcnn models (#488)

* add moile-side rcnn server model

configs/mobile_side/README.md

+# Practical Mobile-side detection method base on RCNN
+
+## Introduction
+
+* This is developed by PaddleDetection. Many useful tricks are utilized for the model training process. More details can be seen in the configuration file.
+* The inerence is tested on Qualcomm Snapdragon 845 Mobile Platform.
+
+
+## Model Zoo
+
+| Backbone                | Type     | Image/gpu | Lr schd | Inf time on SD845 (fps) | Box AP | Mask AP |                           Download                           |
+| :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: |
+| MobileNetV3-vd-FPN         | Cascade Faster     |     2     |   5.6x(CosineDecay)    |     8.13     |  25.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar) |
+| MobileNetV3-vd-FPN         | Cascade Faster     |     2     |   5.6x(CosineDecay)    |     2.66     |  30.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar) |
+
+**note**
+* `5.6x` means the model is trained with `50000` minibatches 8 GPU cards(batch size=2 for each card).

configs/mobile_side/cascade_rcnn_mobilenetv3_fpn_320.yml

+architecture: CascadeRCNN
+max_iters: 500000
+snapshot_iter: 50000
+use_gpu: true
+log_smooth_window: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
+weights: output/cascade_rcnn_mobilenetv3_fpn_320/model_final
+metric: COCO
+num_classes: 81
+
+CascadeRCNN:
+  backbone: MobileNetV3RCNN
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: CascadeBBoxHead
+  bbox_assigner: CascadeBBoxAssigner
+
+MobileNetV3RCNN:
+  norm_type: bn
+  freeze_norm: true
+  norm_decay: 0.0
+  feature_maps: [2, 3, 4]
+  conv_decay: 0.00001
+  lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
+  scale: 1.0
+  model_name: large
+
+FPN:
+  min_level: 2
+  max_level: 6
+  num_chan: 48
+  has_extra_convs: true
+  spatial_scale: [0.0625, 0.125, 0.25]
+
+FPNRPNHead:
+  anchor_generator:
+    anchor_sizes: [32, 64, 128, 256, 512]
+    aspect_ratios: [0.5, 1.0, 2.0]
+    stride: [16.0, 16.0]
+    variance: [1.0, 1.0, 1.0, 1.0]
+  anchor_start_size: 16
+  min_level: 2
+  max_level: 6
+  num_chan: 48
+  rpn_target_assign:
+    rpn_batch_size_per_im: 256
+    rpn_fg_fraction: 0.5
+    rpn_positive_overlap: 0.7
+    rpn_negative_overlap: 0.3
+    rpn_straddle_thresh: 0.0
+  train_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 2000
+    post_nms_top_n: 2000
+  test_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 300
+    post_nms_top_n: 100
+
+FPNRoIAlign:
+  canconical_level: 3
+  canonical_size: 112
+  min_level: 2
+  max_level: 4
+  box_resolution: 7
+  sampling_ratio: 2
+
+CascadeBBoxAssigner:
+  batch_size_per_im: 512
+  bbox_reg_weights: [10, 20, 30]
+  bg_thresh_lo: [0.0, 0.0, 0.0]
+  bg_thresh_hi: [0.5, 0.6, 0.7]
+  fg_thresh: [0.5, 0.6, 0.7]
+  fg_fraction: 0.25
+
+CascadeBBoxHead:
+  head: CascadeTwoFCHead
+  bbox_loss: BalancedL1Loss
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+
+BalancedL1Loss:
+  alpha: 0.5
+  gamma: 1.5
+  beta: 1.0
+  loss_weight: 1.0
+
+CascadeTwoFCHead:
+  mlp_dim: 128
+
+LearningRate:
+  base_lr: 0.02
+  schedulers:
+  - !CosineDecay
+    max_iters: 500000
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
+
+TrainReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
+  dataset:
+    !COCODataSet
+    image_dir: train2017
+    anno_path: annotations/instances_train2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+  - !RandomFlipImage
+    prob: 0.5
+  - !AutoAugmentImage
+    autoaug_type: v1
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    target_size: [224, 256, 288, 320, 352, 384]
+    max_size: 512
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: false
+  batch_size: 2
+  shuffle: true
+  worker_num: 2
+  use_process: false
+
+
+TestReader:
+  inputs_def:
+    # set image_shape if needed
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+  dataset:
+    !ImageFolder
+    anno_path: annotations/instances_val2017.json
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 320
+    target_size: 320
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+
+
+
+EvalReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    # for voc
+    #fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
+  dataset:
+    !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 320
+    target_size: 320
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+  drop_empty: false
+  worker_num: 2

configs/mobile_side/cascade_rcnn_mobilenetv3_fpn_640.yml

+architecture: CascadeRCNN
+max_iters: 500000
+snapshot_iter: 50000
+use_gpu: true
+log_smooth_window: 20
+save_dir: output
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
+weights: output/cascade_rcnn_mobilenetv3_fpn_640/model_final
+metric: COCO
+num_classes: 81
+
+CascadeRCNN:
+  backbone: MobileNetV3RCNN
+  fpn: FPN
+  rpn_head: FPNRPNHead
+  roi_extractor: FPNRoIAlign
+  bbox_head: CascadeBBoxHead
+  bbox_assigner: CascadeBBoxAssigner
+
+MobileNetV3RCNN:
+  norm_type: bn
+  freeze_norm: true
+  norm_decay: 0.0
+  feature_maps: [2, 3, 4]
+  conv_decay: 0.00001
+  lr_mult_list: [1.0, 1.0, 1.0, 1.0, 1.0]
+  scale: 1.0
+  model_name: large
+
+FPN:
+  min_level: 2
+  max_level: 6
+  num_chan: 48
+  has_extra_convs: true
+  spatial_scale: [0.0625, 0.125, 0.25]
+
+FPNRPNHead:
+  anchor_generator:
+    anchor_sizes: [32, 64, 128, 256, 512]
+    aspect_ratios: [0.5, 1.0, 2.0]
+    stride: [16.0, 16.0]
+    variance: [1.0, 1.0, 1.0, 1.0]
+  anchor_start_size: 24
+  min_level: 2
+  max_level: 6
+  num_chan: 48
+  rpn_target_assign:
+    rpn_batch_size_per_im: 256
+    rpn_fg_fraction: 0.5
+    rpn_positive_overlap: 0.7
+    rpn_negative_overlap: 0.3
+    rpn_straddle_thresh: 0.0
+  train_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 2000
+    post_nms_top_n: 2000
+  test_proposal:
+    min_size: 0.0
+    nms_thresh: 0.7
+    pre_nms_top_n: 300
+    post_nms_top_n: 100
+
+FPNRoIAlign:
+  canconical_level: 4
+  canonical_size: 224
+  min_level: 2
+  max_level: 5
+  box_resolution: 7
+  sampling_ratio: 2
+
+CascadeBBoxAssigner:
+  batch_size_per_im: 512
+  bbox_reg_weights: [10, 20, 30]
+  bg_thresh_lo: [0.0, 0.0, 0.0]
+  bg_thresh_hi: [0.5, 0.6, 0.7]
+  fg_thresh: [0.5, 0.6, 0.7]
+  fg_fraction: 0.25
+
+CascadeBBoxHead:
+  head: CascadeTwoFCHead
+  bbox_loss: BalancedL1Loss
+  nms:
+    keep_top_k: 100
+    nms_threshold: 0.5
+    score_threshold: 0.05
+
+BalancedL1Loss:
+  alpha: 0.5
+  gamma: 1.5
+  beta: 1.0
+  loss_weight: 1.0
+
+CascadeTwoFCHead:
+  mlp_dim: 128
+
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !CosineDecay
+    max_iters: 500000
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 500
+
+OptimizerBuilder:
+  optimizer:
+    momentum: 0.9
+    type: Momentum
+  regularizer:
+    factor: 0.00004
+    type: L2
+
+TrainReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
+  dataset:
+    !COCODataSet
+    image_dir: train2017
+    anno_path: annotations/instances_train2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+  - !RandomFlipImage
+    prob: 0.5
+  - !AutoAugmentImage
+    autoaug_type: v1
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    target_size: [416, 448, 480, 512, 544, 576, 608, 640, 672]
+    max_size: 1000
+    interp: 1
+    use_cv2: true
+  - !Permute
+    to_bgr: false
+    channel_first: true
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: false
+  batch_size: 2
+  shuffle: true
+  worker_num: 2
+  use_process: false
+
+
+TestReader:
+  inputs_def:
+    # set image_shape if needed
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+  dataset:
+    !ImageFolder
+    anno_path: annotations/instances_val2017.json
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 640
+    target_size: 640
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+
+
+
+EvalReader:
+  inputs_def:
+    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    # for voc
+    #fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
+  dataset:
+    !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco
+  sample_transforms:
+  - !DecodeImage
+    to_rgb: true
+    with_mixup: false
+  - !NormalizeImage
+    is_channel_first: false
+    is_scale: true
+    mean: [0.485,0.456,0.406]
+    std: [0.229, 0.224,0.225]
+  - !ResizeImage
+    interp: 1
+    max_size: 640
+    target_size: 640
+    use_cv2: true
+  - !Permute
+    channel_first: true
+    to_bgr: false
+  batch_transforms:
+  - !PadBatch
+    pad_to_stride: 32
+    use_padded_im_info: true
+  batch_size: 1
+  shuffle: false
+  drop_empty: false
+  worker_num: 2

ppdet/modeling/backbones/mobilenet_v3.py

@@ -22,14 +22,18 @@ import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
+import math
+import numpy as np
+from collections import OrderedDict
+
 from ppdet.core.workspace import register
 from numbers import Integral
 
-__all__ = ['MobileNetV3']
+__all__ = ['MobileNetV3', 'MobileNetV3RCNN']
 
 
 @register
-class MobileNetV3():
+class MobileNetV3(object):
     """
     MobileNet v3, see https://arxiv.org/abs/1905.02244
     Args:
@@ -40,18 +44,25 @@ class MobileNetV3():
         conv_decay (float): weight decay for convolution layer weights.
         feature_maps (list): index of stages whose feature maps are returned.
         extra_block_filters (list): number of filter for each extra block.
+        lr_mult_list (list): learning rate ratio of different blocks, lower learning rate ratio
+                             is need for pretrained model got using distillation(default as 
+                             [1.0, 1.0, 1.0, 1.0, 1.0]).
+        freeze_norm (bool): freeze normalization layers
+        feature_maps (list): feature maps used in two-stage rcnn models(default as None).
     """
     __shared__ = ['norm_type']
 
-    def __init__(self,
-                 scale=1.0,
-                 model_name='small',
-                 feature_maps=[5, 6, 7, 8, 9, 10],
-                 conv_decay=0.0,
-                 norm_type='bn',
-                 norm_decay=0.0,
-                 extra_block_filters=[[256, 512], [128, 256], [128, 256],
-                                      [64, 128]]):
+    def __init__(
+            self,
+            scale=1.0,
+            model_name='small',
+            feature_maps=[5, 6, 7, 8, 9, 10],
+            conv_decay=0.0,
+            norm_type='bn',
+            norm_decay=0.0,
+            extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]],
+            lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
+            freeze_norm=False, ):
         if isinstance(feature_maps, Integral):
             feature_maps = [feature_maps]
 
@@ -64,6 +75,12 @@ class MobileNetV3():
         self.inplanes = 16
         self.end_points = []
         self.block_stride = 0
+
+        self.lr_mult_list = lr_mult_list
+        self.freeze_norm = freeze_norm
+        self.norm_type = norm_type
+        self.curr_stage = 0
+
         if model_name == "large":
             self.cfg = [
                 # kernel_size, expand, channel, se_block, act_mode, stride
@@ -83,6 +100,8 @@ class MobileNetV3():
                 [5, 960, 160, True, 'hard_swish', 1],
                 [5, 960, 160, True, 'hard_swish', 1],
             ]
+            self.cls_ch_squeeze = 960
+            self.cls_ch_expand = 1280
         elif model_name == "small":
             self.cfg = [
                 # kernel_size, expand, channel, se_block, act_mode, stride
@@ -98,6 +117,8 @@ class MobileNetV3():
                 [5, 576, 96, True, 'hard_swish', 1],
                 [5, 576, 96, True, 'hard_swish', 1],
             ]
+            self.cls_ch_squeeze = 576
+            self.cls_ch_expand = 1280
         else:
             raise NotImplementedError
 
@@ -112,8 +133,9 @@ class MobileNetV3():
                        act=None,
                        name=None,
                        use_cudnn=True):
-        conv_param_attr = ParamAttr(
-            name=name + '_weights', regularizer=L2Decay(self.conv_decay))
+        lr_idx = self.curr_stage // 3
+        lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
+        lr_mult = self.lr_mult_list[lr_idx]
         conv = fluid.layers.conv2d(
             input=input,
             num_filters=num_filters,
@@ -123,19 +145,14 @@ class MobileNetV3():
             groups=num_groups,
             act=None,
             use_cudnn=use_cudnn,
-            param_attr=conv_param_attr,
+            param_attr=ParamAttr(
+                name=name + '_weights',
+                learning_rate=lr_mult,
+                regularizer=L2Decay(self.conv_decay)),
             bias_attr=False)
         bn_name = name + '_bn'
-        bn_param_attr = ParamAttr(
-            name=bn_name + "_scale", regularizer=L2Decay(self.norm_decay))
-        bn_bias_attr = ParamAttr(
-            name=bn_name + "_offset", regularizer=L2Decay(self.norm_decay))
-        bn = fluid.layers.batch_norm(
-            input=conv,
-            param_attr=bn_param_attr,
-            bias_attr=bn_bias_attr,
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+        bn = self._bn(conv, bn_name=bn_name)
+
         if if_act:
             if act == 'relu':
                 bn = fluid.layers.relu(bn)
@@ -145,10 +162,64 @@ class MobileNetV3():
                 bn = fluid.layers.relu6(bn)
         return bn
 
+    def _bn(self, input, act=None, bn_name=None):
+        lr_idx = self.curr_stage // 3
+        lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
+        lr_mult = self.lr_mult_list[lr_idx]
+        norm_lr = 0. if self.freeze_norm else lr_mult
+        norm_decay = self.norm_decay
+        pattr = ParamAttr(
+            name=bn_name + '_scale',
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay))
+        battr = ParamAttr(
+            name=bn_name + '_offset',
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay))
+
+        conv = input
+
+        if self.norm_type in ['bn', 'sync_bn']:
+            global_stats = True if self.freeze_norm else False
+            out = fluid.layers.batch_norm(
+                input=conv,
+                act=act,
+                name=bn_name + '.output.1',
+                param_attr=pattr,
+                bias_attr=battr,
+                moving_mean_name=bn_name + '_mean',
+                moving_variance_name=bn_name + '_variance',
+                use_global_stats=global_stats)
+            scale = fluid.framework._get_var(pattr.name)
+            bias = fluid.framework._get_var(battr.name)
+        elif self.norm_type == 'affine_channel':
+            scale = fluid.layers.create_parameter(
+                shape=[conv.shape[1]],
+                dtype=conv.dtype,
+                attr=pattr,
+                default_initializer=fluid.initializer.Constant(1.))
+            bias = fluid.layers.create_parameter(
+                shape=[conv.shape[1]],
+                dtype=conv.dtype,
+                attr=battr,
+                default_initializer=fluid.initializer.Constant(0.))
+            out = fluid.layers.affine_channel(
+                x=conv, scale=scale, bias=bias, act=act)
+
+        if self.freeze_norm:
+            scale.stop_gradient = True
+            bias.stop_gradient = True
+
+        return out
+
     def _hard_swish(self, x):
         return x * fluid.layers.relu6(x + 3) / 6.
 
     def _se_block(self, input, num_out_filter, ratio=4, name=None):
+        lr_idx = self.curr_stage // 3
+        lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
+        lr_mult = self.lr_mult_list[lr_idx]
+
         num_mid_filter = int(num_out_filter // ratio)
         pool = fluid.layers.pool2d(
             input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
@@ -157,15 +228,27 @@ class MobileNetV3():
             filter_size=1,
             num_filters=num_mid_filter,
             act='relu',
-            param_attr=ParamAttr(name=name + '_1_weights'),
-            bias_attr=ParamAttr(name=name + '_1_offset'))
+            param_attr=ParamAttr(
+                name=name + '_1_weights',
+                learning_rate=lr_mult,
+                regularizer=L2Decay(self.conv_decay)),
+            bias_attr=ParamAttr(
+                name=name + '_1_offset',
+                learning_rate=lr_mult,
+                regularizer=L2Decay(self.conv_decay)))
         conv2 = fluid.layers.conv2d(
             input=conv1,
             filter_size=1,
             num_filters=num_out_filter,
             act='hard_sigmoid',
-            param_attr=ParamAttr(name=name + '_2_weights'),
-            bias_attr=ParamAttr(name=name + '_2_offset'))
+            param_attr=ParamAttr(
+                name=name + '_2_weights',
+                learning_rate=lr_mult,
+                regularizer=L2Decay(self.conv_decay)),
+            bias_attr=ParamAttr(
+                name=name + '_2_offset',
+                learning_rate=lr_mult,
+                regularizer=L2Decay(self.conv_decay)))
 
         scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
         return scale
@@ -190,6 +273,7 @@ class MobileNetV3():
             if_act=True,
             act=act,
             name=name + '_expand')
+
         if self.block_stride == 4 and stride == 2:
             self.block_stride += 1
             if self.block_stride in self.feature_maps:
@@ -258,6 +342,14 @@ class MobileNetV3():
             name=name + "_extra2_sep")
         return normal_conv
 
+    def _make_divisible(self, v, divisor=8, min_value=None):
+        if min_value is None:
+            min_value = divisor
+        new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+        if new_v < 0.9 * v:
+            new_v += divisor
+        return new_v
+
     def __call__(self, input):
         scale = self.scale
         inplanes = self.inplanes
@@ -268,7 +360,7 @@ class MobileNetV3():
         conv = self._conv_bn_layer(
             input,
             filter_size=3,
-            num_filters=inplanes if scale <= 1.0 else int(inplanes * scale),
+            num_filters=self._make_divisible(inplanes * scale),
             stride=2,
             padding=1,
             num_groups=1,
@@ -276,6 +368,7 @@ class MobileNetV3():
             act='hard_swish',
             name='conv1')
         i = 0
+        inplanes = self._make_divisible(inplanes * scale)
         for layer_cfg in cfg:
             if layer_cfg[5] == 2:
                 self.block_stride += 1
@@ -285,15 +378,16 @@ class MobileNetV3():
             conv = self._residual_unit(
                 input=conv,
                 num_in_filter=inplanes,
-                num_mid_filter=int(scale * layer_cfg[1]),
-                num_out_filter=int(scale * layer_cfg[2]),
+                num_mid_filter=self._make_divisible(scale * layer_cfg[1]),
+                num_out_filter=self._make_divisible(scale * layer_cfg[2]),
                 act=layer_cfg[4],
                 stride=layer_cfg[5],
                 filter_size=layer_cfg[0],
                 use_se=layer_cfg[3],
                 name='conv' + str(i + 2))
-            inplanes = int(scale * layer_cfg[2])
+            inplanes = self._make_divisible(scale * layer_cfg[2])
             i += 1
+            self.curr_stage += 1
         self.block_stride += 1
         if self.block_stride in self.feature_maps:
             self.end_points.append(conv)
@@ -302,7 +396,7 @@ class MobileNetV3():
         conv_extra = self._conv_bn_layer(
             conv,
             filter_size=1,
-            num_filters=int(scale * cfg[-1][1]),
+            num_filters=self._make_divisible(scale * cfg[-1][1]),
             stride=1,
             padding="SAME",
             num_groups=1,
@@ -324,3 +418,132 @@ class MobileNetV3():
 
         return OrderedDict([('mbv3_{}'.format(idx), feat)
                             for idx, feat in enumerate(self.end_points)])
+
+
+@register
+class MobileNetV3RCNN(MobileNetV3):
+    def __init__(
+            self,
+            scale=1.0,
+            model_name='large',
+            conv_decay=0.0,
+            norm_type='bn',
+            norm_decay=0.0,
+            freeze_norm=True,
+            feature_maps=[2, 3, 4, 5],
+            lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0], ):
+        super(MobileNetV3RCNN, self).__init__(
+            scale=scale,
+            model_name=model_name,
+            conv_decay=conv_decay,
+            norm_type=norm_type,
+            norm_decay=norm_decay,
+            lr_mult_list=lr_mult_list,
+            feature_maps=feature_maps)
+        self.curr_stage = 0
+        self.block_stride = 1
+
+    def _residual_unit(self,
+                       input,
+                       num_in_filter,
+                       num_mid_filter,
+                       num_out_filter,
+                       stride,
+                       filter_size,
+                       act=None,
+                       use_se=False,
+                       name=None):
+        input_data = input
+        conv0 = self._conv_bn_layer(
+            input=input,
+            filter_size=1,
+            num_filters=num_mid_filter,
+            stride=1,
+            padding=0,
+            if_act=True,
+            act=act,
+            name=name + '_expand')
+
+        feature_level = int(np.log2(self.block_stride))
+        if feature_level in self.feature_maps and stride == 2:
+            self.end_points.append(conv0)
+
+        conv1 = self._conv_bn_layer(
+            input=conv0,
+            filter_size=filter_size,
+            num_filters=num_mid_filter,
+            stride=stride,
+            padding=int((filter_size - 1) // 2),
+            if_act=True,
+            act=act,
+            num_groups=num_mid_filter,
+            use_cudnn=False,
+            name=name + '_depthwise')
+
+        if use_se:
+            conv1 = self._se_block(
+                input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
+
+        conv2 = self._conv_bn_layer(
+            input=conv1,
+            filter_size=1,
+            num_filters=num_out_filter,
+            stride=1,
+            padding=0,
+            if_act=False,
+            name=name + '_linear')
+        if num_in_filter != num_out_filter or stride != 1:
+            return conv2
+        else:
+            return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
+
+    def __call__(self, input):
+        scale = self.scale
+        inplanes = self.inplanes
+        cfg = self.cfg
+        #conv1
+        conv = self._conv_bn_layer(
+            input,
+            filter_size=3,
+            num_filters=self._make_divisible(inplanes * scale),
+            stride=2,
+            padding=1,
+            num_groups=1,
+            if_act=True,
+            act='hard_swish',
+            name='conv1')
+        i = 0
+        inplanes = self._make_divisible(inplanes * scale)
+        for layer_cfg in cfg:
+            self.block_stride *= layer_cfg[5]
+            conv = self._residual_unit(
+                input=conv,
+                num_in_filter=inplanes,
+                num_mid_filter=self._make_divisible(scale * layer_cfg[1]),
+                num_out_filter=self._make_divisible(scale * layer_cfg[2]),
+                act=layer_cfg[4],
+                stride=layer_cfg[5],
+                filter_size=layer_cfg[0],
+                use_se=layer_cfg[3],
+                name='conv' + str(i + 2))
+            inplanes = self._make_divisible(scale * layer_cfg[2])
+            i += 1
+            self.curr_stage += 1
+
+        if np.max(self.feature_maps) >= 5:
+            conv = self._conv_bn_layer(
+                input=conv,
+                filter_size=1,
+                num_filters=self._make_divisible(scale * cfg[-1][1]),
+                stride=1,
+                padding=0,
+                num_groups=1,
+                if_act=True,
+                act='hard_swish',
+                name='conv_last')
+            self.end_points.append(conv)
+            i += 1
+
+        res = OrderedDict([('mv3_{}'.format(idx), self.end_points[idx])
+                           for idx, feat_idx in enumerate(self.feature_maps)])
+        return res