[cherry-pick] Add gnfpn and gnhead (#2237)

* add gn head fpn, test=dygraph

* add gn for cascade

* update gn readme, test=dygraph

dygraph/configs/gn/README.md

+# Group Normalization
+
+## Model Zoo
+
+| 骨架网络         | 网络类型        | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | Mask AP |  下载  | 配置文件 |
+| :------------- | :------------- | :-----------: | :------: | :--------: |:-----: | :-----: | :----: | :----: |
+| ResNet50-FPN   | Faster         |    1          |   2x     |    -       |  41.9  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/faster_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml) |
+| ResNet50-FPN   | Mask           |    1          |   2x     |    -       |    -   |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/dygraph/mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml) |
+
+**注意：** Faster R-CNN baseline仅使用 `2fc` head，而此处使用[`4conv1fc` head](https://arxiv.org/abs/1803.08494)（4层conv之间使用GN），并且FPN也使用GN，而对于Mask R-CNN是在mask head的4层conv之间也使用GN。
+
+## Citations
+```
+@inproceedings{wu2018group,
+  title={Group Normalization},
+  author={Wu, Yuxin and He, Kaiming},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  year={2018}
+}
+```

dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '../cascade_rcnn/_base_/optimizer_1x.yml',
+  '../cascade_rcnn/_base_/cascade_mask_rcnn_r50_fpn.yml',
+  '../cascade_rcnn/_base_/cascade_mask_fpn_reader.yml',
+]
+weights: output/cascade_mask_rcnn_r50_fpn_gn_2x/model_final
+
+CascadeRCNN:
+  backbone: ResNet
+  neck: FPN
+  rpn_head: RPNHead
+  bbox_head: CascadeHead
+  mask_head: MaskHead
+  # post process
+  bbox_post_process: BBoxPostProcess
+  mask_post_process: MaskPostProcess
+
+FPN:
+  out_channel: 256
+  norm_type: gn
+
+CascadeHead:
+  head: CascadeXConvNormHead
+  roi_extractor:
+    resolution: 7
+    sampling_ratio: 0
+    aligned: True
+  bbox_assigner: BBoxAssigner
+
+CascadeXConvNormHead:
+  num_convs: 4
+  mlp_dim: 1024
+  norm_type: gn
+
+MaskHead:
+  head: MaskFeat
+  roi_extractor:
+    resolution: 14
+    sampling_ratio: 0
+    aligned: True
+  mask_assigner: MaskAssigner
+  share_bbox_feat: False
+
+MaskFeat:
+  num_convs: 4
+  out_channels: 256
+  norm_type: gn
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000

dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../cascade_rcnn/_base_/optimizer_1x.yml',
+  '../cascade_rcnn/_base_/cascade_rcnn_r50_fpn.yml',
+  '../cascade_rcnn/_base_/cascade_fpn_reader.yml',
+]
+weights: output/cascade_rcnn_r50_fpn_gn_2x/model_final
+
+FPN:
+  out_channel: 256
+  norm_type: gn
+
+CascadeHead:
+  head: CascadeXConvNormHead
+  roi_extractor:
+    resolution: 7
+    sampling_ratio: 0
+    aligned: True
+  bbox_assigner: BBoxAssigner
+
+CascadeXConvNormHead:
+  num_convs: 4
+  mlp_dim: 1024
+  norm_type: gn
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000

dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml

+_BASE_: [
+  '../datasets/coco_detection.yml',
+  '../runtime.yml',
+  '../faster_rcnn/_base_/optimizer_1x.yml',
+  '../faster_rcnn/_base_/faster_rcnn_r50_fpn.yml',
+  '../faster_rcnn/_base_/faster_fpn_reader.yml',
+]
+weights: output/faster_rcnn_r50_fpn_gn_2x_coco/model_final
+
+FasterRCNN:
+  backbone: ResNet
+  neck: FPN
+  rpn_head: RPNHead
+  bbox_head: BBoxHead
+  # post process
+  bbox_post_process: BBoxPostProcess
+
+FPN:
+  out_channel: 256
+  norm_type: gn
+
+BBoxHead:
+  head: XConvNormHead
+  roi_extractor:
+    resolution: 7
+    sampling_ratio: 0
+    aligned: True
+  bbox_assigner: BBoxAssigner
+
+XConvNormHead:
+  num_convs: 4
+  mlp_dim: 1024
+  norm_type: gn
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000

dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml

+_BASE_: [
+  '../datasets/coco_instance.yml',
+  '../runtime.yml',
+  '../mask_rcnn/_base_/optimizer_1x.yml',
+  '../mask_rcnn/_base_/mask_rcnn_r50_fpn.yml',
+  '../mask_rcnn/_base_/mask_fpn_reader.yml',
+]
+weights: output/mask_rcnn_r50_fpn_gn_2x_coco/model_final
+
+MaskRCNN:
+  backbone: ResNet
+  neck: FPN
+  rpn_head: RPNHead
+  bbox_head: BBoxHead
+  mask_head: MaskHead
+  # post process
+  bbox_post_process: BBoxPostProcess
+  mask_post_process: MaskPostProcess
+
+FPN:
+  out_channel: 256
+  norm_type: gn
+
+BBoxHead:
+  head: XConvNormHead
+  roi_extractor:
+    resolution: 7
+    sampling_ratio: 0
+    aligned: True
+  bbox_assigner: BBoxAssigner
+
+XConvNormHead:
+  num_convs: 4
+  mlp_dim: 1024
+  norm_type: gn
+
+MaskHead:
+  head: MaskFeat
+  roi_extractor:
+    resolution: 14
+    sampling_ratio: 0
+    aligned: True
+  mask_assigner: MaskAssigner
+  share_bbox_feat: False
+
+MaskFeat:
+  num_convs: 4
+  out_channels: 256
+  norm_type: gn
+
+
+epoch: 24
+LearningRate:
+  base_lr: 0.01
+  schedulers:
+  - !PiecewiseDecay
+    gamma: 0.1
+    milestones: [16, 22]
+  - !LinearWarmup
+    start_factor: 0.1
+    steps: 1000

dygraph/ppdet/modeling/heads/bbox_head.py

@@ -15,7 +15,7 @@
 import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
-from paddle.nn.initializer import Normal, XavierUniform
+from paddle.nn.initializer import Normal, XavierUniform, KaimingNormal
 from paddle.regularizer import L2Decay
 
 from ppdet.core.workspace import register, create
@@ -24,6 +24,9 @@ from ppdet.modeling import ops
 from .roi_extractor import RoIAlign
 from ..shape_spec import ShapeSpec
 from ..bbox_utils import bbox2delta
+from ppdet.modeling.layers import ConvNormLayer
+
+__all__ = ['TwoFCHead', 'XConvNormHead', 'BBoxHead']
 
 
 @register
@@ -63,6 +66,86 @@ class TwoFCHead(nn.Layer):
         return fc7
 
 
+@register
+class XConvNormHead(nn.Layer):
+    """
+    RCNN bbox head with serveral convolution layers
+    Args:
+        in_dim(int): num of channels for the input rois_feat
+        num_convs(int): num of convolution layers for the rcnn bbox head
+        conv_dim(int): num of channels for the conv layers
+        mlp_dim(int): num of channels for the fc layers
+        resolution(int): resolution of the rois_feat
+        norm_type(str): norm type, 'gn' by defalut
+        freeze_norm(bool): whether to freeze the norm
+        stage_name(str): used in CascadeXConvNormHead, '' by default
+    """
+    __shared__ = ['norm_type', 'freeze_norm']
+
+    def __init__(self,
+                 in_dim=256,
+                 num_convs=4,
+                 conv_dim=256,
+                 mlp_dim=1024,
+                 resolution=7,
+                 norm_type='gn',
+                 freeze_norm=False,
+                 stage_name=''):
+        super(XConvNormHead, self).__init__()
+        self.in_dim = in_dim
+        self.num_convs = num_convs
+        self.conv_dim = conv_dim
+        self.mlp_dim = mlp_dim
+        self.norm_type = norm_type
+        self.freeze_norm = freeze_norm
+
+        self.bbox_head_convs = []
+        fan = conv_dim * 3 * 3
+        initializer = KaimingNormal(fan_in=fan)
+        for i in range(self.num_convs):
+            in_c = in_dim if i == 0 else conv_dim
+            head_conv_name = stage_name + 'bbox_head_conv{}'.format(i)
+            head_conv = self.add_sublayer(
+                head_conv_name,
+                ConvNormLayer(
+                    ch_in=in_c,
+                    ch_out=conv_dim,
+                    filter_size=3,
+                    stride=1,
+                    norm_type=self.norm_type,
+                    norm_name=head_conv_name + '_norm',
+                    freeze_norm=self.freeze_norm,
+                    initializer=initializer,
+                    name=head_conv_name))
+            self.bbox_head_convs.append(head_conv)
+
+        fan = conv_dim * resolution * resolution
+        self.fc6 = nn.Linear(
+            conv_dim * resolution * resolution,
+            mlp_dim,
+            weight_attr=paddle.ParamAttr(
+                initializer=XavierUniform(fan_out=fan)),
+            bias_attr=paddle.ParamAttr(
+                learning_rate=2., regularizer=L2Decay(0.)))
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        s = input_shape
+        s = s[0] if isinstance(s, (list, tuple)) else s
+        return {'in_dim': s.channels}
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=self.mlp_dim, )]
+
+    def forward(self, rois_feat):
+        for i in range(self.num_convs):
+            rois_feat = F.relu(self.bbox_head_convs[i](rois_feat))
+        rois_feat = paddle.flatten(rois_feat, start_axis=1, stop_axis=-1)
+        fc6 = F.relu(self.fc6(rois_feat))
+        return fc6
+
+
 @register
 class BBoxHead(nn.Layer):
     __shared__ = ['num_classes']

dygraph/ppdet/modeling/heads/cascade_head.py

@@ -21,11 +21,13 @@ from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, create
 from ppdet.modeling import ops
 
-from .bbox_head import BBoxHead, TwoFCHead
+from .bbox_head import BBoxHead, TwoFCHead, XConvNormHead
 from .roi_extractor import RoIAlign
 from ..shape_spec import ShapeSpec
 from ..bbox_utils import bbox2delta, delta2bbox, clip_bbox, nonempty_bbox
 
+__all__ = ['CascadeTwoFCHead', 'CascadeXConvNormHead', 'CascadeHead']
+
 
 @register
 class CascadeTwoFCHead(nn.Layer):
@@ -62,6 +64,53 @@ class CascadeTwoFCHead(nn.Layer):
         return out
 
 
+@register
+class CascadeXConvNormHead(nn.Layer):
+    __shared__ = ['norm_type', 'freeze_norm', 'num_cascade_stage']
+
+    def __init__(self,
+                 in_dim=256,
+                 num_convs=4,
+                 conv_dim=256,
+                 mlp_dim=1024,
+                 resolution=7,
+                 norm_type='gn',
+                 freeze_norm=False,
+                 num_cascade_stage=3):
+        super(CascadeXConvNormHead, self).__init__()
+        self.in_dim = in_dim
+        self.mlp_dim = mlp_dim
+
+        self.head_list = []
+        for stage in range(num_cascade_stage):
+            head_per_stage = self.add_sublayer(
+                str(stage),
+                XConvNormHead(
+                    in_dim,
+                    num_convs,
+                    conv_dim,
+                    mlp_dim,
+                    resolution,
+                    norm_type,
+                    freeze_norm,
+                    stage_name='stage{}_'.format(stage)))
+            self.head_list.append(head_per_stage)
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        s = input_shape
+        s = s[0] if isinstance(s, (list, tuple)) else s
+        return {'in_dim': s.channels}
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=self.mlp_dim, )]
+
+    def forward(self, rois_feat, stage=0):
+        out = self.head_list[stage](rois_feat)
+        return out
+
+
 @register
 class CascadeHead(BBoxHead):
     __shared__ = ['num_classes', 'num_cascade_stages']

dygraph/ppdet/modeling/heads/mask_head.py

@@ -20,21 +20,43 @@ from paddle.regularizer import L2Decay
 
 from ppdet.core.workspace import register, create
 from ppdet.modeling import ops
+from ppdet.modeling.layers import ConvNormLayer
 
 from .roi_extractor import RoIAlign
 
 
 @register
 class MaskFeat(nn.Layer):
-    def __init__(self, num_convs=0, in_channels=2048, out_channels=256):
+    def __init__(self,
+                 num_convs=4,
+                 in_channels=256,
+                 out_channels=256,
+                 norm_type=None):
         super(MaskFeat, self).__init__()
         self.num_convs = num_convs
         self.in_channels = in_channels
         self.out_channels = out_channels
+        self.norm_type = norm_type
         fan_conv = out_channels * 3 * 3
         fan_deconv = out_channels * 2 * 2
 
         mask_conv = nn.Sequential()
+        if norm_type == 'gn':
+            for i in range(self.num_convs):
+                conv_name = 'mask_inter_feat_{}'.format(i + 1)
+                mask_conv.add_sublayer(
+                    conv_name,
+                    ConvNormLayer(
+                        ch_in=in_channels if i == 0 else out_channels,
+                        ch_out=out_channels,
+                        filter_size=3,
+                        stride=1,
+                        norm_type=self.norm_type,
+                        norm_name=conv_name + '_norm',
+                        initializer=KaimingNormal(fan_in=fan_conv),
+                        name=conv_name))
+                mask_conv.add_sublayer(conv_name + 'act', nn.ReLU())
+        else:
             for i in range(self.num_convs):
                 conv_name = 'mask_inter_feat_{}'.format(i + 1)
                 mask_conv.add_sublayer(

dygraph/ppdet/modeling/layers.py

@@ -117,11 +117,15 @@ class ConvNormLayer(nn.Layer):
                  filter_size,
                  stride,
                  norm_type='bn',
+                 norm_decay=0.,
                  norm_groups=32,
                  use_dcn=False,
                  norm_name=None,
                  bias_on=False,
                  lr_scale=1.,
+                 freeze_norm=False,
+                 initializer=Normal(
+                     mean=0., std=0.01),
                  name=None):
         super(ConvNormLayer, self).__init__()
         assert norm_type in ['bn', 'sync_bn', 'gn']
@@ -144,8 +148,7 @@ class ConvNormLayer(nn.Layer):
                 groups=1,
                 weight_attr=ParamAttr(
                     name=name + "_weight",
-                    initializer=Normal(
-                        mean=0., std=0.01),
+                    initializer=initializer,
                     learning_rate=1.),
                 bias_attr=bias_attr)
         else:
@@ -159,25 +162,28 @@ class ConvNormLayer(nn.Layer):
                 groups=1,
                 weight_attr=ParamAttr(
                     name=name + "_weight",
-                    initializer=Normal(
-                        mean=0., std=0.01),
+                    initializer=initializer,
                     learning_rate=1.),
                 bias_attr=True,
                 lr_scale=2.,
-                regularizer=L2Decay(0.),
+                regularizer=L2Decay(norm_decay),
                 name=name)
 
+        norm_lr = 0. if freeze_norm else 1.
         param_attr = ParamAttr(
             name=norm_name + "_scale",
-            learning_rate=1.,
-            regularizer=L2Decay(0.))
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay))
         bias_attr = ParamAttr(
             name=norm_name + "_offset",
-            learning_rate=1.,
-            regularizer=L2Decay(0.))
-        if norm_type in ['bn', 'sync_bn']:
+            learning_rate=norm_lr,
+            regularizer=L2Decay(norm_decay))
+        if norm_type == 'bn':
             self.norm = nn.BatchNorm2D(
                 ch_out, weight_attr=param_attr, bias_attr=bias_attr)
+        elif norm_type == 'sync_bn':
+            self.norm = nn.SyncBatchNorm(
+                ch_out, weight_attr=param_attr, bias_attr=bias_attr)
         elif norm_type == 'gn':
             self.norm = nn.GroupNorm(
                 num_groups=norm_groups,

dygraph/ppdet/modeling/necks/fpn.py

@@ -14,13 +14,13 @@
 
 import numpy as np
 import paddle
+import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
-from paddle.nn import Layer
-from paddle.nn import Conv2D
 from paddle.nn.initializer import XavierUniform
 from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, serializable
+from ppdet.modeling.layers import ConvNormLayer
 from ..shape_spec import ShapeSpec
 
 __all__ = ['FPN']
@@ -28,7 +28,7 @@ __all__ = ['FPN']
 
 @register
 @serializable
-class FPN(Layer):
+class FPN(nn.Layer):
     def __init__(self,
                  in_channels,
                  out_channel,
@@ -36,8 +36,10 @@ class FPN(Layer):
                  has_extra_convs=False,
                  extra_stage=1,
                  use_c5=True,
+                 norm_type=None,
+                 norm_decay=0.,
+                 freeze_norm=False,
                  relu_before_extra_convs=True):
-
         super(FPN, self).__init__()
         self.out_channel = out_channel
         for s in range(extra_stage):
@@ -47,6 +49,9 @@ class FPN(Layer):
         self.extra_stage = extra_stage
         self.use_c5 = use_c5
         self.relu_before_extra_convs = relu_before_extra_convs
+        self.norm_type = norm_type
+        self.norm_decay = norm_decay
+        self.freeze_norm = freeze_norm
 
         self.lateral_convs = []
         self.fpn_convs = []
@@ -62,9 +67,24 @@ class FPN(Layer):
             else:
                 lateral_name = 'fpn_inner_res{}_sum_lateral'.format(i + 2)
             in_c = in_channels[i - st_stage]
+            if self.norm_type == 'gn':
+                lateral = self.add_sublayer(
+                    lateral_name,
+                    ConvNormLayer(
+                        ch_in=in_c,
+                        ch_out=out_channel,
+                        filter_size=1,
+                        stride=1,
+                        norm_type=self.norm_type,
+                        norm_decay=self.norm_decay,
+                        norm_name=lateral_name + '_norm',
+                        freeze_norm=self.freeze_norm,
+                        initializer=XavierUniform(fan_out=in_c),
+                        name=lateral_name))
+            else:
                 lateral = self.add_sublayer(
                     lateral_name,
-                Conv2D(
+                    nn.Conv2D(
                         in_channels=in_c,
                         out_channels=out_channel,
                         kernel_size=1,
@@ -73,9 +93,24 @@ class FPN(Layer):
             self.lateral_convs.append(lateral)
 
             fpn_name = 'fpn_res{}_sum'.format(i + 2)
+            if self.norm_type == 'gn':
                 fpn_conv = self.add_sublayer(
                     fpn_name,
-                Conv2D(
+                    ConvNormLayer(
+                        ch_in=out_channel,
+                        ch_out=out_channel,
+                        filter_size=3,
+                        stride=1,
+                        norm_type=self.norm_type,
+                        norm_decay=self.norm_decay,
+                        norm_name=fpn_name + '_norm',
+                        freeze_norm=self.freeze_norm,
+                        initializer=XavierUniform(fan_out=fan),
+                        name=fpn_name))
+            else:
+                fpn_conv = self.add_sublayer(
+                    fpn_name,
+                    nn.Conv2D(
                         in_channels=out_channel,
                         out_channels=out_channel,
                         kernel_size=3,
@@ -93,9 +128,24 @@ class FPN(Layer):
                 else:
                     in_c = out_channel
                 extra_fpn_name = 'fpn_{}'.format(lvl + 2)
+                if self.norm_type == 'gn':
+                    extra_fpn_conv = self.add_sublayer(
+                        extra_fpn_name,
+                        ConvNormLayer(
+                            ch_in=in_c,
+                            ch_out=out_channel,
+                            filter_size=3,
+                            stride=2,
+                            norm_type=self.norm_type,
+                            norm_decay=self.norm_decay,
+                            norm_name=extra_fpn_name + '_norm',
+                            freeze_norm=self.freeze_norm,
+                            initializer=XavierUniform(fan_out=fan),
+                            name=extra_fpn_name))
+                else:
                     extra_fpn_conv = self.add_sublayer(
                         extra_fpn_name,
-                    Conv2D(
+                        nn.Conv2D(
                             in_channels=in_c,
                             out_channels=out_channel,
                             kernel_size=3,