[dev] add bifpn in necks (#4148)

ppdet/modeling/necks/__init__.py

@@ -18,6 +18,7 @@ from . import hrfpn
 from . import ttf_fpn
 from . import centernet_fpn
 from . import pan
+from . import bifpn
 
 from .fpn import *
 from .yolo_fpn import *
@@ -26,3 +27,4 @@ from .ttf_fpn import *
 from .centernet_fpn import *
 from .blazeface_fpn import *
 from .pan import *
+from .bifpn import *

ppdet/modeling/necks/bifpn.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+from paddle.nn.initializer import Constant
+
+from ppdet.core.workspace import register, serializable
+from ppdet.modeling.layers import ConvNormLayer
+from ..shape_spec import ShapeSpec
+
+__all__ = ['BiFPN']
+
+
+class SeparableConvLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels=None,
+                 kernel_size=3,
+                 norm_type='bn',
+                 norm_groups=32,
+                 act='swish'):
+        super(SeparableConvLayer, self).__init__()
+        assert norm_type in ['bn', 'sync_bn', 'gn', None]
+        assert act in ['swish', 'relu', None]
+
+        self.in_channels = in_channels
+        if out_channels is None:
+            self.out_channels = self.in_channels
+        self.norm_type = norm_type
+        self.norm_groups = norm_groups
+        self.depthwise_conv = nn.Conv2D(
+            in_channels,
+            in_channels,
+            kernel_size,
+            padding=kernel_size // 2,
+            groups=in_channels,
+            bias_attr=False)
+        self.pointwise_conv = nn.Conv2D(in_channels, self.out_channels, 1)
+
+        # norm type
+        if self.norm_type == 'bn':
+            self.norm = nn.BatchNorm2D(self.out_channels)
+        elif self.norm_type == 'sync_bn':
+            self.norm = nn.SyncBatchNorm(self.out_channels)
+        elif self.norm_type == 'gn':
+            self.norm = nn.GroupNorm(
+                num_groups=self.norm_groups, num_channels=self.out_channels)
+
+        # activation
+        if act == 'swish':
+            self.act = nn.Swish()
+        elif act == 'relu':
+            self.act = nn.ReLU()
+
+    def forward(self, x):
+        if self.act is not None:
+            x = self.act(x)
+        out = self.depthwise_conv(x)
+        out = self.pointwise_conv(out)
+        if self.norm_type is not None:
+            out = self.norm(out)
+        return out
+
+
+class BiFPNCell(nn.Layer):
+    def __init__(self,
+                 channels=256,
+                 num_levels=5,
+                 eps=1e-5,
+                 use_weighted_fusion=True,
+                 kernel_size=3,
+                 norm_type='bn',
+                 norm_groups=32,
+                 act='swish'):
+        super(BiFPNCell, self).__init__()
+        self.channels = channels
+        self.num_levels = num_levels
+        self.eps = eps
+        self.use_weighted_fusion = use_weighted_fusion
+
+        # up
+        self.conv_up = nn.LayerList([
+            SeparableConvLayer(
+                self.channels,
+                kernel_size=kernel_size,
+                norm_type=norm_type,
+                norm_groups=norm_groups,
+                act=act) for _ in range(self.num_levels - 1)
+        ])
+        # down
+        self.conv_down = nn.LayerList([
+            SeparableConvLayer(
+                self.channels,
+                kernel_size=kernel_size,
+                norm_type=norm_type,
+                norm_groups=norm_groups,
+                act=act) for _ in range(self.num_levels - 1)
+        ])
+
+        if self.use_weighted_fusion:
+            self.up_weights = self.create_parameter(
+                shape=[self.num_levels - 1, 2],
+                attr=ParamAttr(initializer=Constant(1.)))
+            self.down_weights = self.create_parameter(
+                shape=[self.num_levels - 1, 3],
+                attr=ParamAttr(initializer=Constant(1.)))
+
+    def _feature_fusion_cell(self,
+                             conv_layer,
+                             lateral_feat,
+                             sampling_feat,
+                             route_feat=None,
+                             weights=None):
+        if self.use_weighted_fusion:
+            weights = F.relu(weights)
+            weights = weights / (weights.sum() + self.eps)
+            if route_feat is not None:
+                out_feat = weights[0] * lateral_feat + \
+                           weights[1] * sampling_feat + \
+                           weights[2] * route_feat
+            else:
+                out_feat = weights[0] * lateral_feat + \
+                           weights[1] * sampling_feat
+        else:
+            if route_feat is not None:
+                out_feat = lateral_feat + sampling_feat + route_feat
+            else:
+                out_feat = lateral_feat + sampling_feat
+
+        out_feat = conv_layer(out_feat)
+        return out_feat
+
+    def forward(self, feats):
+        # feats: [P3 - P7]
+        lateral_feats = []
+
+        # up
+        up_feature = feats[-1]
+        for i, feature in enumerate(feats[::-1]):
+            if i == 0:
+                lateral_feats.append(feature)
+            else:
+                shape = paddle.shape(feature)
+                up_feature = F.interpolate(
+                    up_feature, size=[shape[2], shape[3]])
+                lateral_feature = self._feature_fusion_cell(
+                    self.conv_up[i - 1],
+                    feature,
+                    up_feature,
+                    weights=self.up_weights[i - 1]
+                    if self.use_weighted_fusion else None)
+                lateral_feats.append(lateral_feature)
+                up_feature = lateral_feature
+
+        out_feats = []
+        # down
+        down_feature = lateral_feats[-1]
+        for i, (lateral_feature,
+                route_feature) in enumerate(zip(lateral_feats[::-1], feats)):
+            if i == 0:
+                out_feats.append(lateral_feature)
+            else:
+                down_feature = F.max_pool2d(down_feature, 3, 2, 1)
+                if i == len(feats) - 1:
+                    route_feature = None
+                    weights = self.down_weights[
+                        i - 1][:2] if self.use_weighted_fusion else None
+                else:
+                    weights = self.down_weights[
+                        i - 1] if self.use_weighted_fusion else None
+                out_feature = self._feature_fusion_cell(
+                    self.conv_down[i - 1],
+                    lateral_feature,
+                    down_feature,
+                    route_feature,
+                    weights=weights)
+                out_feats.append(out_feature)
+                down_feature = out_feature
+
+        return out_feats
+
+
+@register
+@serializable
+class BiFPN(nn.Layer):
+    """
+    Bidirectional Feature Pyramid Network, see https://arxiv.org/abs/1911.09070
+
+    Args:
+        in_channels (list[int]): input channels of each level which can be
+            derived from the output shape of backbone by from_config.
+        out_channel (int): output channel of each level.
+        num_extra_levels (int): the number of extra stages added to the last level.
+            default: 2
+        fpn_strides (List): The stride of each level.
+        num_stacks (int): the number of stacks for BiFPN, default: 1.
+        use_weighted_fusion (bool): use weighted feature fusion in BiFPN, default: True.
+        norm_type (string|None): the normalization type in BiFPN module. If
+            norm_type is None, norm will not be used after conv and if
+            norm_type is string, bn, gn, sync_bn are available. default: bn.
+        norm_groups (int): if you use gn, set this param.
+        act (string|None): the activation function of BiFPN.
+    """
+
+    def __init__(self,
+                 in_channels=(512, 1024, 2048),
+                 out_channel=256,
+                 num_extra_levels=2,
+                 fpn_strides=[8, 16, 32, 64, 128],
+                 num_stacks=1,
+                 use_weighted_fusion=True,
+                 norm_type='bn',
+                 norm_groups=32,
+                 act='swish'):
+        super(BiFPN, self).__init__()
+        assert num_stacks > 0, "The number of stacks of BiFPN is at least 1."
+        assert norm_type in ['bn', 'sync_bn', 'gn', None]
+        assert act in ['swish', 'relu', None]
+        assert num_extra_levels >= 0, \
+            "The `num_extra_levels` must be non negative(>=0)."
+
+        self.in_channels = in_channels
+        self.out_channel = out_channel
+        self.num_extra_levels = num_extra_levels
+        self.num_stacks = num_stacks
+        self.use_weighted_fusion = use_weighted_fusion
+        self.norm_type = norm_type
+        self.norm_groups = norm_groups
+        self.act = act
+        self.num_levels = len(self.in_channels) + self.num_extra_levels
+        if len(fpn_strides) != self.num_levels:
+            for i in range(self.num_extra_levels):
+                fpn_strides += [fpn_strides[-1] * 2]
+        self.fpn_strides = fpn_strides
+
+        self.lateral_convs = nn.LayerList()
+        for in_c in in_channels:
+            self.lateral_convs.append(
+                ConvNormLayer(in_c, self.out_channel, 1, 1))
+        if self.num_extra_levels > 0:
+            self.extra_convs = nn.LayerList()
+            for i in range(self.num_extra_levels):
+                if i == 0:
+                    self.extra_convs.append(
+                        ConvNormLayer(self.in_channels[-1], self.out_channel, 3,
+                                      2))
+                else:
+                    self.extra_convs.append(nn.MaxPool2D(3, 2, 1))
+
+        self.bifpn_cells = nn.LayerList()
+        for i in range(self.num_stacks):
+            self.bifpn_cells.append(
+                BiFPNCell(
+                    self.out_channel,
+                    self.num_levels,
+                    use_weighted_fusion=self.use_weighted_fusion,
+                    norm_type=self.norm_type,
+                    norm_groups=self.norm_groups,
+                    act=self.act))
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {
+            'in_channels': [i.channels for i in input_shape],
+            'fpn_strides': [i.stride for i in input_shape]
+        }
+
+    @property
+    def out_shape(self):
+        return [
+            ShapeSpec(
+                channels=self.out_channel, stride=s) for s in self.fpn_strides
+        ]
+
+    def forward(self, feats):
+        assert len(feats) == len(self.in_channels)
+        fpn_feats = []
+        for conv_layer, feature in zip(self.lateral_convs, feats):
+            fpn_feats.append(conv_layer(feature))
+        if self.num_extra_levels > 0:
+            feat = feats[-1]
+            for conv_layer in self.extra_convs:
+                feat = conv_layer(feat)
+                fpn_feats.append(feat)
+
+        for bifpn_cell in self.bifpn_cells:
+            fpn_feats = bifpn_cell(fpn_feats)
+        return fpn_feats