diff --git a/dygraph/configs/gn/README.md b/dygraph/configs/gn/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5cf4cd4d1e790253d5a8782e50f33c07c0a6fcc0 --- /dev/null +++ b/dygraph/configs/gn/README.md @@ -0,0 +1,20 @@ +# 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} +} +``` diff --git a/dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml b/dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml new file mode 100644 index 0000000000000000000000000000000000000000..c38e19d7f452f0555f41fcdd6cb6bfe0c3abbc49 --- /dev/null +++ b/dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml @@ -0,0 +1,61 @@ +_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 diff --git a/dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x.yml b/dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x.yml new file mode 100644 index 0000000000000000000000000000000000000000..21abed6d58b983ef91abb1385304662a3f570218 --- /dev/null +++ b/dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x.yml @@ -0,0 +1,37 @@ +_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 diff --git a/dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml b/dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml new file mode 100644 index 0000000000000000000000000000000000000000..e8eb5679347900cbbedde7363cffb6d43ede13bd --- /dev/null +++ b/dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml @@ -0,0 +1,45 @@ +_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 diff --git a/dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml b/dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml new file mode 100644 index 0000000000000000000000000000000000000000..2104fa901e194fbbf3c66aef36ae1ffaf0f88272 --- /dev/null +++ b/dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml @@ -0,0 +1,61 @@ +_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 diff --git a/dygraph/ppdet/modeling/heads/bbox_head.py b/dygraph/ppdet/modeling/heads/bbox_head.py index bf4dac9a83176bd684007b6997b559b574f9b4c2..662b9f93fcb0f60590e0035290c103521c46eb36 100644 --- a/dygraph/ppdet/modeling/heads/bbox_head.py +++ b/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'] diff --git a/dygraph/ppdet/modeling/heads/cascade_head.py b/dygraph/ppdet/modeling/heads/cascade_head.py index f1a33e1173d5bf6c284fccbdc1cb3a57987190f7..4ce1b8dadb41490dfb38a070bb4e128787552976 100644 --- a/dygraph/ppdet/modeling/heads/cascade_head.py +++ b/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'] diff --git a/dygraph/ppdet/modeling/heads/mask_head.py b/dygraph/ppdet/modeling/heads/mask_head.py index 9a5243d958704b81f7c6542578d48f2f531d7873..dc624ff838e8b9dcb66e024fcbf83fcdbb08cf4a 100644 --- a/dygraph/ppdet/modeling/heads/mask_head.py +++ b/dygraph/ppdet/modeling/heads/mask_head.py @@ -20,33 +20,55 @@ 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() - for i in range(self.num_convs): - conv_name = 'mask_inter_feat_{}'.format(i + 1) - mask_conv.add_sublayer( - conv_name, - nn.Conv2D( - in_channels=in_channels if i == 0 else out_channels, - out_channels=out_channels, - kernel_size=3, - padding=1, - weight_attr=paddle.ParamAttr( - initializer=KaimingNormal(fan_in=fan_conv)))) - mask_conv.add_sublayer(conv_name + 'act', nn.ReLU()) + 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( + conv_name, + nn.Conv2D( + in_channels=in_channels if i == 0 else out_channels, + out_channels=out_channels, + kernel_size=3, + padding=1, + weight_attr=paddle.ParamAttr( + initializer=KaimingNormal(fan_in=fan_conv)))) + mask_conv.add_sublayer(conv_name + 'act', nn.ReLU()) mask_conv.add_sublayer( 'conv5_mask', nn.Conv2DTranspose( diff --git a/dygraph/ppdet/modeling/layers.py b/dygraph/ppdet/modeling/layers.py index 04dcbd3ff89d3586c19ab28c2962c4b27efeac4f..c50e1c706f5652e2796a410b53a68695dea183cd 100644 --- a/dygraph/ppdet/modeling/layers.py +++ b/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, diff --git a/dygraph/ppdet/modeling/necks/fpn.py b/dygraph/ppdet/modeling/necks/fpn.py index aa7198ea86dfc25ef336591b7d9832fd5d215aa6..85767bb105dd4d134b339e37b9f759016ce9f369 100644 --- a/dygraph/ppdet/modeling/necks/fpn.py +++ b/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 = [] @@ -62,26 +67,56 @@ class FPN(Layer): else: lateral_name = 'fpn_inner_res{}_sum_lateral'.format(i + 2) in_c = in_channels[i - st_stage] - lateral = self.add_sublayer( - lateral_name, - Conv2D( - in_channels=in_c, - out_channels=out_channel, - kernel_size=1, - weight_attr=ParamAttr( - initializer=XavierUniform(fan_out=in_c)))) + 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, + nn.Conv2D( + in_channels=in_c, + out_channels=out_channel, + kernel_size=1, + weight_attr=ParamAttr( + initializer=XavierUniform(fan_out=in_c)))) self.lateral_convs.append(lateral) fpn_name = 'fpn_res{}_sum'.format(i + 2) - fpn_conv = self.add_sublayer( - fpn_name, - Conv2D( - in_channels=out_channel, - out_channels=out_channel, - kernel_size=3, - padding=1, - weight_attr=ParamAttr( - initializer=XavierUniform(fan_out=fan)))) + if self.norm_type == 'gn': + fpn_conv = self.add_sublayer( + fpn_name, + 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, + padding=1, + weight_attr=ParamAttr( + initializer=XavierUniform(fan_out=fan)))) self.fpn_convs.append(fpn_conv) # add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5) @@ -93,16 +128,31 @@ class FPN(Layer): else: in_c = out_channel extra_fpn_name = 'fpn_{}'.format(lvl + 2) - extra_fpn_conv = self.add_sublayer( - extra_fpn_name, - Conv2D( - in_channels=in_c, - out_channels=out_channel, - kernel_size=3, - stride=2, - padding=1, - weight_attr=ParamAttr( - initializer=XavierUniform(fan_out=fan)))) + 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, + nn.Conv2D( + in_channels=in_c, + out_channels=out_channel, + kernel_size=3, + stride=2, + padding=1, + weight_attr=ParamAttr( + initializer=XavierUniform(fan_out=fan)))) self.fpn_convs.append(extra_fpn_conv) @classmethod