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Implement all missing docstrings (#5298)

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Glenn Jocher 2023-10-10 20:07:13 +02:00 • committed by GitHub
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26 changed files with 649 additions and 79 deletions
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35
ultralytics/nn/modules/head.py
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@ -25,7 +25,8 @@ class Detect(nn.Module):
anchors = torch.empty(0) # init
strides = torch.empty(0) # init
def __init__(self, nc=80, ch=()): # detection layer
def __init__(self, nc=80, ch=()):
"""Initializes the YOLOv8 detection layer with specified number of classes and channels."""
super().__init__()
self.nc = nc # number of classes
self.nl = len(ch) # number of detection layers
@ -149,7 +150,10 @@ class Pose(Detect):
class Classify(nn.Module):
"""YOLOv8 classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
"""Initializes YOLOv8 classification head with specified input and output channels, kernel size, stride,
padding, and groups.
"""
super().__init__()
c_ = 1280 # efficientnet_b0 size
self.conv = Conv(c1, c_, k, s, p, g)
@ -166,6 +170,13 @@ class Classify(nn.Module):
class RTDETRDecoder(nn.Module):
"""
Real-Time Deformable Transformer Decoder (RTDETRDecoder) module for object detection.
This decoder module utilizes Transformer architecture along with deformable convolutions to predict bounding boxes
and class labels for objects in an image. It integrates features from multiple layers and runs through a series of
Transformer decoder layers to output the final predictions.
"""
export = False # export mode
def __init__(
@ -186,6 +197,26 @@ class RTDETRDecoder(nn.Module):
label_noise_ratio=0.5,
box_noise_scale=1.0,
learnt_init_query=False):
"""
Initializes the RTDETRDecoder module with the given parameters.
Args:
nc (int): Number of classes. Default is 80.
ch (tuple): Channels in the backbone feature maps. Default is (512, 1024, 2048).
hd (int): Dimension of hidden layers. Default is 256.
nq (int): Number of query points. Default is 300.
ndp (int): Number of decoder points. Default is 4.
nh (int): Number of heads in multi-head attention. Default is 8.
ndl (int): Number of decoder layers. Default is 6.
d_ffn (int): Dimension of the feed-forward networks. Default is 1024.
dropout (float): Dropout rate. Default is 0.
act (nn.Module): Activation function. Default is nn.ReLU.
eval_idx (int): Evaluation index. Default is -1.
nd (int): Number of denoising. Default is 100.
label_noise_ratio (float): Label noise ratio. Default is 0.5.
box_noise_scale (float): Box noise scale. Default is 1.0.
learnt_init_query (bool): Whether to learn initial query embeddings. Default is False.
"""
super().__init__()
self.hidden_dim = hd
self.nhead = nh