yolo annotations (#2542)

ppdet/modeling/architectures/yolo.py

@@ -20,6 +20,16 @@ class YOLOv3(BaseArch):
                  yolo_head='YOLOv3Head',
                  post_process='BBoxPostProcess',
                  data_format='NCHW'):
+        """
+        YOLOv3 network, see https://arxiv.org/abs/1804.02767
+
+        Args:
+            backbone (nn.Layer): backbone instance
+            neck (nn.Layer): neck instance
+            yolo_head (nn.Layer): anchor_head instance
+            bbox_post_process (object): `BBoxPostProcess` instance
+            data_format (str): data format, NCHW or NHWC
+        """
         super(YOLOv3, self).__init__(data_format=data_format)
         self.backbone = backbone
         self.neck = neck

ppdet/modeling/backbones/darknet.py

@@ -37,6 +37,22 @@ class ConvBNLayer(nn.Layer):
                  act="leaky",
                  name=None,
                  data_format='NCHW'):
+        """
+        conv + bn + activation layer
+
+        Args:
+            ch_in (int): input channel
+            ch_out (int): output channel
+            filter_size (int): filter size, default 3
+            stride (int): stride, default 1
+            groups (int): number of groups of conv layer, default 1
+            padding (int): padding size, default 0
+            norm_type (str): batch norm type, default bn
+            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
+            act (str): activation function type, default 'leaky', which means leaky_relu
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(ConvBNLayer, self).__init__()
 
         self.conv = nn.Conv2D(
@@ -75,6 +91,20 @@ class DownSample(nn.Layer):
                  norm_decay=0.,
                  name=None,
                  data_format='NCHW'):
+        """
+        downsample layer
+
+        Args:
+            ch_in (int): input channel
+            ch_out (int): output channel
+            filter_size (int): filter size, default 3
+            stride (int): stride, default 2
+            padding (int): padding size, default 1
+            norm_type (str): batch norm type, default bn
+            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
 
         super(DownSample, self).__init__()
 
@@ -103,6 +133,18 @@ class BasicBlock(nn.Layer):
                  norm_decay=0.,
                  name=None,
                  data_format='NCHW'):
+        """
+        BasicBlock layer of DarkNet
+
+        Args:
+            ch_in (int): input channel
+            ch_out (int): output channel
+            norm_type (str): batch norm type, default bn
+            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
+
         super(BasicBlock, self).__init__()
 
         self.conv1 = ConvBNLayer(
@@ -142,6 +184,18 @@ class Blocks(nn.Layer):
                  norm_decay=0.,
                  name=None,
                  data_format='NCHW'):
+        """
+        Blocks layer, which consist of some BaickBlock layers
+
+        Args:
+            ch_in (int): input channel
+            ch_out (int): output channel
+            count (int): number of BasicBlock layer
+            norm_type (str): batch norm type, default bn
+            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(Blocks, self).__init__()
 
         self.basicblock0 = BasicBlock(
@@ -189,6 +243,18 @@ class DarkNet(nn.Layer):
                  norm_type='bn',
                  norm_decay=0.,
                  data_format='NCHW'):
+        """
+        Darknet, see https://pjreddie.com/darknet/yolo/
+
+        Args:
+            depth (int): depth of network
+            freeze_at (int): freeze the backbone at which stage
+            filter_size (int): filter size, default 3
+            return_idx (list): index of stages whose feature maps are returned
+            norm_type (str): batch norm type, default bn
+            norm_decay (str): decay for weight and bias of batch norm layer, default 0.
+            data_format (str): data format, NCHW or NHWC
+        """
         super(DarkNet, self).__init__()
         self.depth = depth
         self.freeze_at = freeze_at

ppdet/modeling/heads/yolo_head.py

@@ -28,6 +28,18 @@ class YOLOv3Head(nn.Layer):
                  iou_aware=False,
                  iou_aware_factor=0.4,
                  data_format='NCHW'):
+        """
+        Head for YOLOv3 network
+
+        Args:
+            num_classes (int): number of foreground classes
+            anchors (list): anchors
+            anchor_masks (list): anchor masks
+            loss (object): YOLOv3Loss instance
+            iou_aware (bool): whether to use iou_aware
+            iou_aware_factor (float): iou aware factor
+            data_format (str): data format, NCHW or NHWC
+        """
         super(YOLOv3Head, self).__init__()
         self.num_classes = num_classes
         self.loss = loss

ppdet/modeling/losses/yolo_loss.py

@@ -46,6 +46,18 @@ class YOLOv3Loss(nn.Layer):
                  scale_x_y=1.,
                  iou_loss=None,
                  iou_aware_loss=None):
+        """
+        YOLOv3Loss layer
+
+        Args:
+            num_calsses (int): number of foreground classes
+            ignore_thresh (float): threshold to ignore confidence loss
+            label_smooth (bool): whether to use label smoothing
+            downsample (list): downsample ratio for each detection block
+            scale_x_y (float): scale_x_y factor
+            iou_loss (object): IoULoss instance
+            iou_aware_loss (object): IouAwareLoss instance  
+        """
         super(YOLOv3Loss, self).__init__()
         self.num_classes = num_classes
         self.ignore_thresh = ignore_thresh

ppdet/modeling/necks/yolo_fpn.py

@@ -27,6 +27,16 @@ __all__ = ['YOLOv3FPN', 'PPYOLOFPN']
 
 class YoloDetBlock(nn.Layer):
     def __init__(self, ch_in, channel, norm_type, name, data_format='NCHW'):
+        """
+        YOLODetBlock layer for yolov3, see https://arxiv.org/abs/1804.02767
+
+        Args:
+            ch_in (int): input channel
+            channel (int): base channel
+            norm_type (str): batch norm type
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(YoloDetBlock, self).__init__()
         self.ch_in = ch_in
         self.channel = channel
@@ -78,6 +88,17 @@ class SPP(nn.Layer):
                  norm_type,
                  name,
                  data_format='NCHW'):
+        """
+        SPP layer, which consist of four pooling layer follwed by conv layer
+
+        Args:
+            ch_in (int): input channel of conv layer
+            ch_out (int): output channel of conv layer
+            k (int): kernel size of conv layer
+            norm_type (str): batch norm type
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(SPP, self).__init__()
         self.pool = []
         for size in pool_size:
@@ -110,6 +131,15 @@ class SPP(nn.Layer):
 
 class DropBlock(nn.Layer):
     def __init__(self, block_size, keep_prob, name, data_format='NCHW'):
+        """
+        DropBlock layer, see https://arxiv.org/abs/1810.12890
+
+        Args:
+            block_size (int): block size
+            keep_prob (int): keep probability
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(DropBlock, self).__init__()
         self.block_size = block_size
         self.keep_prob = keep_prob
@@ -149,6 +179,19 @@ class CoordConv(nn.Layer):
                  norm_type,
                  name,
                  data_format='NCHW'):
+        """
+        CoordConv layer
+
+        Args:
+            ch_in (int): input channel
+            ch_out (int): output channel
+            filter_size (int): filter size, default 3
+            padding (int): padding size, default 0
+            norm_type (str): batch norm type, default bn
+            name (str): layer name
+            data_format (str): data format, NCHW or NHWC
+
+        """
         super(CoordConv, self).__init__()
         self.conv = ConvBNLayer(
             ch_in + 2,
@@ -193,6 +236,14 @@ class CoordConv(nn.Layer):
 
 class PPYOLODetBlock(nn.Layer):
     def __init__(self, cfg, name, data_format='NCHW'):
+        """
+        PPYOLODetBlock layer
+
+        Args:
+            cfg (list): layer configs for this block
+            name (str): block name
+            data_format (str): data format, NCHW or NHWC
+        """
         super(PPYOLODetBlock, self).__init__()
         self.conv_module = nn.Sequential()
         for idx, (conv_name, layer, args, kwargs) in enumerate(cfg[:-1]):
@@ -220,6 +271,15 @@ class YOLOv3FPN(nn.Layer):
                  in_channels=[256, 512, 1024],
                  norm_type='bn',
                  data_format='NCHW'):
+        """
+        YOLOv3FPN layer
+
+        Args:
+            in_channels (list): input channels for fpn
+            norm_type (str): batch norm type, default bn
+            data_format (str): data format, NCHW or NHWC
+
+        """
         super(YOLOv3FPN, self).__init__()
         assert len(in_channels) > 0, "in_channels length should > 0"
         self.in_channels = in_channels
@@ -300,6 +360,16 @@ class PPYOLOFPN(nn.Layer):
                  norm_type='bn',
                  data_format='NCHW',
                  **kwargs):
+        """
+        PPYOLOFPN layer
+
+        Args:
+            in_channels (list): input channels for fpn
+            norm_type (str): batch norm type, default bn
+            data_format (str): data format, NCHW or NHWC
+            kwargs: extra key-value pairs, such as parameter of DropBlock and spp 
+
+        """
         super(PPYOLOFPN, self).__init__()
         assert len(in_channels) > 0, "in_channels length should > 0"
         self.in_channels = in_channels