From config for single stage model YOLOv3/PPYOLO/SSD (#2112)

* fit for YOLOvd/PPYOLO/SSD

dygraph/configs/ppyolo/_base_/ppyolo_r50vd_dcn.yml

@@ -22,7 +22,6 @@ ResNet:
   norm_decay: 0.
 
 PPYOLOFPN:
-  feat_channels: [2048, 1280, 640]
   coord_conv: true
   drop_block: true
   block_size: 3

dygraph/configs/ssd/_base_/ssd_mobilenet_v1_300.yml

@@ -16,12 +16,9 @@ MobileNet:
   feature_maps: [11, 13, 14, 15, 16, 17]
 
 SSDHead:
-  in_channels: [512, 1024, 512, 256, 256, 128]
-  anchor_generator: AnchorGeneratorSSD
   kernel_size: 1
   padding: 0
-
-AnchorGeneratorSSD:
+  anchor_generator:
     steps: [0, 0, 0, 0, 0, 0]
     aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
     min_ratio: 20

dygraph/configs/ssd/_base_/ssd_vgg16_300.yml

 architecture: SSD
 pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/VGG16_caffe_pretrained.pdparams
-load_static_weights: True
 
 # Model Achitecture
 SSD:
@@ -15,10 +14,7 @@ VGG:
   normalizations: [20., -1, -1, -1, -1, -1]
 
 SSDHead:
-  in_channels: [512, 1024, 512, 256, 256, 256]
-  anchor_generator: AnchorGeneratorSSD
-
-AnchorGeneratorSSD:
+  anchor_generator:
     steps: [8, 16, 32, 64, 100, 300]
     aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]]
     min_ratio: 20

dygraph/configs/ssd/_base_/ssdlite_mobilenet_v1_300.yml

@@ -15,12 +15,9 @@ MobileNet:
   feature_maps: [11, 13, 14, 15, 16, 17]
 
 SSDHead:
-  in_channels: [512, 1024, 512, 256, 256, 128]
-  anchor_generator: AnchorGeneratorSSD
   use_sepconv: True
   conv_decay: 0.00004
-
-AnchorGeneratorSSD:
+  anchor_generator:
     steps: [16, 32, 64, 100, 150, 300]
     aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
     min_ratio: 20

dygraph/configs/ssd/_base_/ssdlite_mobilenet_v3_large_320.yml

@@ -18,12 +18,9 @@ MobileNetV3:
   multiplier: 0.5
 
 SSDHead:
-  in_channels: [672, 480, 512, 256, 256, 128]
-  anchor_generator: AnchorGeneratorSSD
   use_sepconv: True
   conv_decay: 0.00004
-
-AnchorGeneratorSSD:
+  anchor_generator:
     steps: [16, 32, 64, 107, 160, 320]
     aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
     min_ratio: 20

dygraph/configs/ssd/_base_/ssdlite_mobilenet_v3_small_320.yml

@@ -18,12 +18,9 @@ MobileNetV3:
   multiplier: 0.5
 
 SSDHead:
-  in_channels: [288, 288, 512, 256, 256, 128]
-  anchor_generator: AnchorGeneratorSSD
   use_sepconv: True
   conv_decay: 0.00004
-
-AnchorGeneratorSSD:
+  anchor_generator:
     steps: [16, 32, 64, 107, 160, 320]
     aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
     min_ratio: 20

dygraph/configs/yolov3/_base_/yolov3_darknet53.yml

@@ -14,8 +14,8 @@ DarkNet:
   depth: 53
   return_idx: [2, 3, 4]
 
-YOLOv3FPN:
-  feat_channels: [1024, 768, 384]
+# use default config
+# YOLOv3FPN:
 
 YOLOv3Head:
   anchors: [[10, 13], [16, 30], [33, 23],

dygraph/configs/yolov3/_base_/yolov3_mobilenet_v1.yml

@@ -15,8 +15,8 @@ MobileNet:
   with_extra_blocks: false
   extra_block_filters: []
 
-YOLOv3FPN:
-  feat_channels: [1024, 768, 384]
+# use default config
+# YOLOv3FPN:
 
 YOLOv3Head:
   anchors: [[10, 13], [16, 30], [33, 23],

dygraph/configs/yolov3/_base_/yolov3_mobilenet_v3_large.yml

@@ -16,8 +16,8 @@ MobileNetV3:
   extra_block_filters: []
   feature_maps: [7, 13, 16]
 
-YOLOv3FPN:
-  feat_channels: [160, 368, 168]
+# use default config
+# YOLOv3FPN:
 
 YOLOv3Head:
   anchors: [[10, 13], [16, 30], [33, 23],

dygraph/configs/yolov3/_base_/yolov3_mobilenet_v3_small.yml

@@ -16,8 +16,8 @@ MobileNetV3:
   extra_block_filters: []
   feature_maps: [4, 9, 12]
 
-YOLOv3FPN:
-  feat_channels: [96, 304, 152]
+# use default config
+# YOLOv3FPN:
 
 YOLOv3Head:
   anchors: [[10, 13], [16, 30], [33, 23],

dygraph/ppdet/modeling/architectures/ssd.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from ppdet.core.workspace import register
+from ppdet.core.workspace import register, create
 from .meta_arch import BaseArch
 
 __all__ = ['SSD']
@@ -11,38 +11,47 @@ __all__ = ['SSD']
 @register
 class SSD(BaseArch):
     __category__ = 'architecture'
-    __inject__ = ['backbone', 'neck', 'ssd_head', 'post_process']
+    __inject__ = ['post_process']
 
-    def __init__(self, backbone, ssd_head, post_process, neck=None):
+    def __init__(self, backbone, ssd_head, post_process):
         super(SSD, self).__init__()
         self.backbone = backbone
-        self.neck = neck
         self.ssd_head = ssd_head
         self.post_process = post_process
 
-    def model_arch(self):
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        # backbone
+        backbone = create(cfg['backbone'])
+
+        # head
+        kwargs = {'input_shape': backbone.out_shape}
+        ssd_head = create(cfg['ssd_head'], **kwargs)
+
+        return {
+            'backbone': backbone,
+            "ssd_head": ssd_head,
+        }
+
+    def _forward(self):
         # Backbone
         body_feats = self.backbone(self.inputs)
 
-        # Neck
-        if self.neck is not None:
-            body_feats, spatial_scale = self.neck(body_feats)
-
         # SSD Head
-        self.ssd_head_outs, self.anchors = self.ssd_head(body_feats,
+        if self.training:
+            return self.ssd_head(body_feats, self.inputs['image'],
+                                 self.inputs['gt_bbox'],
+                                 self.inputs['gt_class'])
+        else:
+            boxes, scores, anchors = self.ssd_head(body_feats,
                                                    self.inputs['image'])
+            bbox, bbox_num = self.post_process((boxes, scores), anchors,
+                                               self.inputs['im_shape'],
+                                               self.inputs['scale_factor'])
+            return bbox, bbox_num
 
     def get_loss(self, ):
-        loss = self.ssd_head.get_loss(self.ssd_head_outs, self.inputs,
-                                      self.anchors)
-        return {"loss": loss}
+        return {"loss": self._forward()}
 
     def get_pred(self):
-        bbox, bbox_num = self.post_process(self.ssd_head_outs, self.anchors,
-                                           self.inputs['im_shape'],
-                                           self.inputs['scale_factor'])
-        outs = {
-            "bbox": bbox,
-            "bbox_num": bbox_num,
-        }
-        return outs
+        return dict(zip(['bbox', 'bbox_num'], self._forward()))

dygraph/ppdet/modeling/architectures/yolo.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from ppdet.core.workspace import register
+from ppdet.core.workspace import register, create
 from .meta_arch import BaseArch
 
 __all__ = ['YOLOv3']
@@ -11,12 +11,7 @@ __all__ = ['YOLOv3']
 @register
 class YOLOv3(BaseArch):
     __category__ = 'architecture'
-    __inject__ = [
-        'backbone',
-        'neck',
-        'yolo_head',
-        'post_process',
-    ]
+    __inject__ = ['post_process']
 
     def __init__(self,
                  backbone='DarkNet',
@@ -29,27 +24,50 @@ class YOLOv3(BaseArch):
         self.yolo_head = yolo_head
         self.post_process = post_process
 
-    def model_arch(self, ):
-        # Backbone
-        body_feats = self.backbone(self.inputs)
+    @classmethod
+    def from_config(cls, cfg, *args, **kwargs):
+        # backbone
+        backbone = create(cfg['backbone'])
 
-        # neck
-        body_feats = self.neck(body_feats)
+        # fpn
+        kwargs = {'input_shape': backbone.out_shape}
+        neck = create(cfg['neck'], **kwargs)
 
-        # YOLO Head
-        self.yolo_head_outs = self.yolo_head(body_feats)
+        # head
+        kwargs = {'input_shape': neck.out_shape}
+        yolo_head = create(cfg['yolo_head'], **kwargs)
 
-    def get_loss(self, ):
-        loss = self.yolo_head.get_loss(self.yolo_head_outs, self.inputs)
-        return loss
+        return {
+            'backbone': backbone,
+            'neck': neck,
+            "yolo_head": yolo_head,
+        }
 
-    def get_pred(self):
-        yolo_head_outs = self.yolo_head.get_outputs(self.yolo_head_outs)
+    def _forward(self):
+        body_feats = self.backbone(self.inputs)
+        body_feats = self.neck(body_feats)
+
+        if self.training:
+            return self.yolo_head(body_feats, self.inputs)
+        else:
+            yolo_head_outs = self.yolo_head(body_feats)
             bbox, bbox_num = self.post_process(
                 yolo_head_outs, self.yolo_head.mask_anchors,
                 self.inputs['im_shape'], self.inputs['scale_factor'])
-        outs = {
-            "bbox": bbox,
-            "bbox_num": bbox_num,
+            return bbox, bbox_num
+
+    def get_loss(self):
+        return self._forward()
+
+    def get_pred(self):
+        bbox_pred, bbox_num = self._forward()
+        label = bbox_pred[:, 0]
+        score = bbox_pred[:, 1]
+        bbox = bbox_pred[:, 2:]
+        output = {
+            'bbox': bbox,
+            'score': score,
+            'label': label,
+            'bbox_num': bbox_num
         }
-        return outs
+        return output

dygraph/ppdet/modeling/backbones/darknet.py

@@ -19,6 +19,7 @@ from paddle import ParamAttr
 from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, serializable
 from ppdet.modeling.ops import batch_norm
+from ..shape_spec import ShapeSpec
 
 __all__ = ['DarkNet', 'ConvBNLayer']
 
@@ -193,6 +194,7 @@ class DarkNet(nn.Layer):
             norm_decay=norm_decay,
             name='yolo_input.downsample')
 
+        self._out_channels = []
         self.darknet_conv_block_list = []
         self.downsample_list = []
         ch_in = [64, 128, 256, 512, 1024]
@@ -208,6 +210,8 @@ class DarkNet(nn.Layer):
                     norm_decay=norm_decay,
                     name=name))
             self.darknet_conv_block_list.append(conv_block)
+            if i in return_idx:
+                self._out_channels.append(64 * (2**i))
         for i in range(num_stages - 1):
             down_name = 'stage.{}.downsample'.format(i)
             downsample = self.add_sublayer(
@@ -235,3 +239,7 @@ class DarkNet(nn.Layer):
             if i < self.num_stages - 1:
                 out = self.downsample_list[i](out)
         return blocks
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]

dygraph/ppdet/modeling/backbones/mobilenet_v1.py

@@ -24,6 +24,7 @@ from paddle.regularizer import L2Decay
 from paddle.nn.initializer import KaimingNormal
 from ppdet.core.workspace import register, serializable
 from numbers import Integral
+from ..shape_spec import ShapeSpec
 
 __all__ = ['MobileNet']
 
@@ -201,6 +202,8 @@ class MobileNet(nn.Layer):
         self.with_extra_blocks = with_extra_blocks
         self.extra_block_filters = extra_block_filters
 
+        self._out_channels = []
+
         self.conv1 = ConvBNLayer(
             in_channels=3,
             out_channels=int(32 * scale),
@@ -229,6 +232,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv2_1"))
         self.dwsl.append(dws21)
+        self._update_out_channels(64, len(self.dwsl), feature_maps)
         dws22 = self.add_sublayer(
             "conv2_2",
             sublayer=DepthwiseSeparable(
@@ -244,6 +248,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv2_2"))
         self.dwsl.append(dws22)
+        self._update_out_channels(128, len(self.dwsl), feature_maps)
         # 1/4
         dws31 = self.add_sublayer(
             "conv3_1",
@@ -260,6 +265,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv3_1"))
         self.dwsl.append(dws31)
+        self._update_out_channels(128, len(self.dwsl), feature_maps)
         dws32 = self.add_sublayer(
             "conv3_2",
             sublayer=DepthwiseSeparable(
@@ -275,6 +281,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv3_2"))
         self.dwsl.append(dws32)
+        self._update_out_channels(256, len(self.dwsl), feature_maps)
         # 1/8
         dws41 = self.add_sublayer(
             "conv4_1",
@@ -291,6 +298,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv4_1"))
         self.dwsl.append(dws41)
+        self._update_out_channels(256, len(self.dwsl), feature_maps)
         dws42 = self.add_sublayer(
             "conv4_2",
             sublayer=DepthwiseSeparable(
@@ -306,6 +314,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv4_2"))
         self.dwsl.append(dws42)
+        self._update_out_channels(512, len(self.dwsl), feature_maps)
         # 1/16
         for i in range(5):
             tmp = self.add_sublayer(
@@ -323,6 +332,7 @@ class MobileNet(nn.Layer):
                     norm_type=norm_type,
                     name="conv5_" + str(i + 1)))
             self.dwsl.append(tmp)
+            self._update_out_channels(512, len(self.dwsl), feature_maps)
         dws56 = self.add_sublayer(
             "conv5_6",
             sublayer=DepthwiseSeparable(
@@ -338,6 +348,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv5_6"))
         self.dwsl.append(dws56)
+        self._update_out_channels(1024, len(self.dwsl), feature_maps)
         # 1/32
         dws6 = self.add_sublayer(
             "conv6",
@@ -354,6 +365,7 @@ class MobileNet(nn.Layer):
                 norm_type=norm_type,
                 name="conv6"))
         self.dwsl.append(dws6)
+        self._update_out_channels(1024, len(self.dwsl), feature_maps)
 
         if self.with_extra_blocks:
             self.extra_blocks = []
@@ -371,6 +383,13 @@ class MobileNet(nn.Layer):
                         norm_type=norm_type,
                         name="conv7_" + str(i + 1)))
                 self.extra_blocks.append(conv_extra)
+                self._update_out_channels(
+                    block_filter[1],
+                    len(self.dwsl) + len(self.extra_blocks), feature_maps)
+
+    def _update_out_channels(self, channel, feature_idx, feature_maps):
+        if feature_idx in feature_maps:
+            self._out_channels.append(channel)
 
     def forward(self, inputs):
         outs = []
@@ -390,3 +409,7 @@ class MobileNet(nn.Layer):
             if idx + 1 in self.feature_maps:
                 outs.append(y)
         return outs
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]

dygraph/ppdet/modeling/backbones/mobilenet_v3.py

@@ -23,6 +23,7 @@ from paddle import ParamAttr
 from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, serializable
 from numbers import Integral
+from ..shape_spec import ShapeSpec
 
 __all__ = ['MobileNetV3']
 
@@ -383,6 +384,7 @@ class MobileNetV3(nn.Layer):
             freeze_norm=freeze_norm,
             name="conv1")
 
+        self._out_channels = []
         self.block_list = []
         i = 0
         inplanes = make_divisible(inplanes * scale)
@@ -413,6 +415,9 @@ class MobileNetV3(nn.Layer):
             self.block_list.append(block)
             inplanes = make_divisible(scale * c)
             i += 1
+            self._update_out_channels(
+                make_divisible(scale * exp)
+                if return_list else inplanes, i + 1, feature_maps)
 
         if self.with_extra_blocks:
             self.extra_block_list = []
@@ -438,6 +443,7 @@ class MobileNetV3(nn.Layer):
                     name="conv" + str(i + 2)))
             self.extra_block_list.append(conv_extra)
             i += 1
+            self._update_out_channels(extra_out_c, i + 1, feature_maps)
 
             for j, block_filter in enumerate(self.extra_block_filters):
                 in_c = extra_out_c if j == 0 else self.extra_block_filters[j -
@@ -457,6 +463,11 @@ class MobileNetV3(nn.Layer):
                         name='conv' + str(i + 2)))
                 self.extra_block_list.append(conv_extra)
                 i += 1
+                self._update_out_channels(block_filter[1], i + 1, feature_maps)
+
+    def _update_out_channels(self, channel, feature_idx, feature_maps):
+        if feature_idx in feature_maps:
+            self._out_channels.append(channel)
 
     def forward(self, inputs):
         x = self.conv1(inputs['image'])
@@ -479,3 +490,7 @@ class MobileNetV3(nn.Layer):
             if idx + 2 in self.feature_maps:
                 outs.append(x)
         return outs
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]

dygraph/ppdet/modeling/backbones/vgg.py

@@ -7,6 +7,7 @@ from paddle import ParamAttr
 from paddle.regularizer import L2Decay
 from paddle.nn import Conv2D, MaxPool2D
 from ppdet.core.workspace import register, serializable
+from ..shape_spec import ShapeSpec
 
 __all__ = ['VGG']
 
@@ -129,6 +130,8 @@ class VGG(nn.Layer):
         self.normalizations = normalizations
         self.extra_block_filters = extra_block_filters
 
+        self._out_channels = []
+
         self.conv_block_0 = ConvBlock(
             3, 64, self.groups[0], 2, 2, 0, name="conv1_")
         self.conv_block_1 = ConvBlock(
@@ -139,6 +142,7 @@ class VGG(nn.Layer):
             256, 512, self.groups[3], 2, 2, 0, name="conv4_")
         self.conv_block_4 = ConvBlock(
             512, 512, self.groups[4], 3, 1, 1, name="conv5_")
+        self._out_channels.append(512)
 
         self.fc6 = Conv2D(
             in_channels=512,
@@ -153,6 +157,7 @@ class VGG(nn.Layer):
             kernel_size=1,
             stride=1,
             padding=0)
+        self._out_channels.append(1024)
 
         # extra block
         self.extra_convs = []
@@ -164,6 +169,7 @@ class VGG(nn.Layer):
                                                       v[2], v[3], v[4]))
             last_channels = v[1]
             self.extra_convs.append(extra_conv)
+            self._out_channels.append(last_channels)
 
         self.norms = []
         for i, n in enumerate(self.normalizations):
@@ -192,7 +198,7 @@ class VGG(nn.Layer):
         outputs.append(out)
 
         if not self.extra_block_filters:
-            return out
+            return outputs
 
         # extra block
         for extra_conv in self.extra_convs:
@@ -204,3 +210,7 @@ class VGG(nn.Layer):
                 outputs[i] = self.norms[i](outputs[i])
 
         return outputs
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]

dygraph/ppdet/modeling/heads/ssd_head.py

@@ -5,6 +5,8 @@ from ppdet.core.workspace import register
 from paddle.regularizer import L2Decay
 from paddle import ParamAttr
 
+from ..layers import AnchorGeneratorSSD
+
 
 class SepConvLayer(nn.Layer):
     def __init__(self,
@@ -58,7 +60,7 @@ class SSDHead(nn.Layer):
     def __init__(self,
                  num_classes=81,
                  in_channels=(512, 1024, 512, 256, 256, 256),
-                 anchor_generator='AnchorGeneratorSSD',
+                 anchor_generator=AnchorGeneratorSSD().__dict__,
                  kernel_size=3,
                  padding=1,
                  use_sepconv=False,
@@ -69,8 +71,11 @@ class SSDHead(nn.Layer):
         self.in_channels = in_channels
         self.anchor_generator = anchor_generator
         self.loss = loss
-        self.num_priors = self.anchor_generator.num_priors
 
+        if isinstance(anchor_generator, dict):
+            self.anchor_generator = AnchorGeneratorSSD(**anchor_generator)
+
+        self.num_priors = self.anchor_generator.num_priors
         self.box_convs = []
         self.score_convs = []
         for i, num_prior in enumerate(self.num_priors):
@@ -116,7 +121,11 @@ class SSDHead(nn.Layer):
                         name=score_conv_name))
             self.score_convs.append(score_conv)
 
-    def forward(self, feats, image):
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {'in_channels': [i.channels for i in input_shape], }
+
+    def forward(self, feats, image, gt_bbox=None, gt_class=None):
         box_preds = []
         cls_scores = []
         prior_boxes = []
@@ -134,10 +143,11 @@ class SSDHead(nn.Layer):
 
         prior_boxes = self.anchor_generator(feats, image)
 
-        outputs = {}
-        outputs['boxes'] = box_preds
-        outputs['scores'] = cls_scores
-        return outputs, prior_boxes
+        if self.training:
+            return self.get_loss(box_preds, cls_scores, gt_bbox, gt_class,
+                                 prior_boxes)
+        else:
+            return box_preds, cls_scores, prior_boxes
 
-    def get_loss(self, inputs, targets, prior_boxes):
-        return self.loss(inputs, targets, prior_boxes)
+    def get_loss(self, boxes, scores, gt_bbox, gt_class, prior_boxes):
+        return self.loss(boxes, scores, gt_bbox, gt_class, prior_boxes)

dygraph/ppdet/modeling/heads/yolo_head.py

@@ -67,21 +67,19 @@ class YOLOv3Head(nn.Layer):
                 assert mask < anchor_num, "anchor mask index overflow"
                 self.mask_anchors[-1].extend(anchors[mask])
 
-    def forward(self, feats):
+    def forward(self, feats, targets=None):
         assert len(feats) == len(self.anchors)
         yolo_outputs = []
         for i, feat in enumerate(feats):
             yolo_output = self.yolo_outputs[i](feat)
             yolo_outputs.append(yolo_output)
-        return yolo_outputs
-
-    def get_loss(self, inputs, targets):
-        return self.loss(inputs, targets, self.anchors)
 
-    def get_outputs(self, outputs):
+        if self.training:
+            return self.loss(yolo_outputs, targets, self.anchors)
+        else:
             if self.iou_aware:
                 y = []
-            for i, out in enumerate(outputs):
+                for i, out in enumerate(yolo_outputs):
                     na = len(self.anchors[i])
                     ioup, x = out[:, 0:na, :, :], out[:, na:, :, :]
                     b, c, h, w = x.shape
@@ -101,4 +99,4 @@ class YOLOv3Head(nn.Layer):
                     y.append(y_t)
                 return y
             else:
-            return outputs
+                return yolo_outputs

dygraph/ppdet/modeling/layers.py

@@ -403,7 +403,7 @@ class MatrixNMS(object):
         self.gaussian_sigma = gaussian_sigma
         self.background_label = background_label
 
-    def __call__(self, bbox, score):
+    def __call__(self, bbox, score, *args):
         return ops.matrix_nms(
             bboxes=bbox,
             scores=score,
@@ -469,7 +469,7 @@ class SSDBox(object):
                  im_shape,
                  scale_factor,
                  var_weight=None):
-        boxes, scores = preds['boxes'], preds['scores']
+        boxes, scores = preds
         outputs = []
         for box, score, prior_box in zip(boxes, scores, prior_boxes):
             pb_w = prior_box[:, 2] - prior_box[:, 0] + self.norm_delta

dygraph/ppdet/modeling/losses/ssd_loss.py

@@ -109,12 +109,11 @@ class SSDLoss(nn.Layer):
         neg_mask = (idx_rank < num_neg).astype(conf_loss.dtype)
         return neg_mask
 
-    def forward(self, inputs, targets, anchors):
-        boxes = paddle.concat(inputs['boxes'], axis=1)
-        scores = paddle.concat(inputs['scores'], axis=1)
+    def forward(self, boxes, scores, gt_box, gt_class, anchors):
+        boxes = paddle.concat(boxes, axis=1)
+        scores = paddle.concat(scores, axis=1)
         prior_boxes = paddle.concat(anchors, axis=0)
-        gt_box = targets['gt_bbox']
-        gt_label = targets['gt_class'].unsqueeze(-1)
+        gt_label = gt_class.unsqueeze(-1)
         batch_size, num_priors, num_classes = scores.shape
 
         def _reshape_to_2d(x):

dygraph/ppdet/modeling/necks/fpn.py

@@ -23,6 +23,8 @@ from paddle.regularizer import L2Decay
 from ppdet.core.workspace import register, serializable
 from ..shape_spec import ShapeSpec
 
+__all__ = ['FPN']
+
 
 @register
 @serializable

dygraph/ppdet/modeling/necks/ttf_fpn.py

@@ -25,6 +25,8 @@ from ppdet.modeling.layers import DeformableConvV2
 import math
 from ppdet.modeling.ops import batch_norm
 
+__all__ = ['TTFFPN']
+
 
 class Upsample(nn.Layer):
     def __init__(self, ch_in, ch_out, name=None):

dygraph/ppdet/modeling/necks/yolo_fpn.py

@@ -20,6 +20,10 @@ from ppdet.core.workspace import register, serializable
 from ..backbones.darknet import ConvBNLayer
 import numpy as np
 
+from ..shape_spec import ShapeSpec
+
+__all__ = ['YOLOv3FPN', 'PPYOLOFPN']
+
 
 class YoloDetBlock(nn.Layer):
     def __init__(self, ch_in, channel, norm_type, name):
@@ -163,23 +167,30 @@ class PPYOLODetBlock(nn.Layer):
 class YOLOv3FPN(nn.Layer):
     __shared__ = ['norm_type']
 
-    def __init__(self, feat_channels=[1024, 768, 384], norm_type='bn'):
+    def __init__(self, in_channels=[256, 512, 1024], norm_type='bn'):
         super(YOLOv3FPN, self).__init__()
-        assert len(feat_channels) > 0, "feat_channels length should > 0"
-        self.feat_channels = feat_channels
-        self.num_blocks = len(feat_channels)
+        assert len(in_channels) > 0, "in_channels length should > 0"
+        self.in_channels = in_channels
+        self.num_blocks = len(in_channels)
+
+        self._out_channels = []
         self.yolo_blocks = []
         self.routes = []
         for i in range(self.num_blocks):
             name = 'yolo_block.{}'.format(i)
+            in_channel = in_channels[-i - 1]
+            if i > 0:
+                in_channel += 512 // (2**i)
             yolo_block = self.add_sublayer(
                 name,
                 YoloDetBlock(
-                    feat_channels[i],
+                    in_channel,
                     channel=512 // (2**i),
                     norm_type=norm_type,
                     name=name))
             self.yolo_blocks.append(yolo_block)
+            # tip layer output channel doubled
+            self._out_channels.append(1024 // (2**i))
 
             if i < self.num_blocks - 1:
                 name = 'yolo_transition.{}'.format(i)
@@ -211,20 +222,25 @@ class YOLOv3FPN(nn.Layer):
 
         return yolo_feats
 
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {'in_channels': [i.channels for i in input_shape], }
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]
+
 
 @register
 @serializable
 class PPYOLOFPN(nn.Layer):
     __shared__ = ['norm_type']
 
-    def __init__(self,
-                 feat_channels=[2048, 1280, 640],
-                 norm_type='bn',
-                 **kwargs):
+    def __init__(self, in_channels=[512, 1024, 2048], norm_type='bn', **kwargs):
         super(PPYOLOFPN, self).__init__()
-        assert len(feat_channels) > 0, "feat_channels length should > 0"
-        self.feat_channels = feat_channels
-        self.num_blocks = len(feat_channels)
+        assert len(in_channels) > 0, "in_channels length should > 0"
+        self.in_channels = in_channels
+        self.num_blocks = len(in_channels)
         # parse kwargs
         self.coord_conv = kwargs.get('coord_conv', False)
         self.drop_block = kwargs.get('drop_block', False)
@@ -246,9 +262,12 @@ class PPYOLOFPN(nn.Layer):
         else:
             dropblock_cfg = []
 
+        self._out_channels = []
         self.yolo_blocks = []
         self.routes = []
-        for i, ch_in in enumerate(self.feat_channels):
+        for i, ch_in in enumerate(self.in_channels[::-1]):
+            if i > 0:
+                ch_in += 512 // (2**i)
             channel = 64 * (2**self.num_blocks) // (2**i)
             base_cfg = [
                 # name of layer, Layer, args
@@ -279,6 +298,7 @@ class PPYOLOFPN(nn.Layer):
             name = 'yolo_block.{}'.format(i)
             yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name))
             self.yolo_blocks.append(yolo_block)
+            self._out_channels.append(channel * 2)
             if i < self.num_blocks - 1:
                 name = 'yolo_transition.{}'.format(i)
                 route = self.add_sublayer(
@@ -308,3 +328,11 @@ class PPYOLOFPN(nn.Layer):
                 route = F.interpolate(route, scale_factor=2.)
 
         return yolo_feats
+
+    @classmethod
+    def from_config(cls, cfg, input_shape):
+        return {'in_channels': [i.channels for i in input_shape], }
+
+    @property
+    def out_shape(self):
+        return [ShapeSpec(channels=c) for c in self._out_channels]