Add https://www.reddit.com/r/Ultralytics/ badge (#13284)
* Refactor code for speed and clarity * Update README.zh-CN.md Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>pull/13285/head
Glenn Jocher
GitHub
parent
b40781b3e3
commit
c5ffbbf1c3
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@ -34,11 +34,11 @@ jobs:
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g = Github(os.getenv('GITHUB_TOKEN'))
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repo = g.get_repo(os.getenv('GITHUB_REPOSITORY'))
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# Fetch the default branch name
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default_branch_name = repo.default_branch
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default_branch = repo.get_branch(default_branch_name)
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for pr in repo.get_pulls(state='open', sort='created'):
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try:
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# Get full names for repositories and branches
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@ -10,7 +10,7 @@
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
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<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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<a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
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<a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
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<br>
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<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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@ -10,6 +10,7 @@
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
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<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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<a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a> <a href="https://community.ultralytics.com"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a> <a href="https://reddit.com/r/ultralytics"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
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<br>
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<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
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@ -91,6 +91,8 @@ MACOS = platform.system() == "Darwin" # macOS environment
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class iOSModel(torch.nn.Module):
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"""An iOS-compatible wrapper for YOLOv5 models that normalizes input images based on their dimensions."""
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def __init__(self, model, im):
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"""
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Initializes an iOS compatible model with normalization based on image dimensions.
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@ -71,7 +71,8 @@ def autopad(k, p=None, d=1):
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class Conv(nn.Module):
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# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
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"""Applies a convolution, batch normalization, and activation function to an input tensor in a neural network."""
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default_act = nn.SiLU() # default activation
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
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@ -91,7 +92,8 @@ class Conv(nn.Module):
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class DWConv(Conv):
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# Depth-wise convolution
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"""Implements a depth-wise convolution layer with optional activation for efficient spatial filtering."""
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def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
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"""Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
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channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
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@ -100,7 +102,8 @@ class DWConv(Conv):
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class DWConvTranspose2d(nn.ConvTranspose2d):
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# Depth-wise transpose convolution
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"""A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models."""
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def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
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"""Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
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(c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
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@ -109,7 +112,8 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
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class TransformerLayer(nn.Module):
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# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
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"""Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm."""
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def __init__(self, c, num_heads):
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"""
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Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers.
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@ -132,7 +136,8 @@ class TransformerLayer(nn.Module):
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class TransformerBlock(nn.Module):
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# Vision Transformer https://arxiv.org/abs/2010.11929
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"""A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers."""
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def __init__(self, c1, c2, num_heads, num_layers):
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"""Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified
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layers.
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@ -157,7 +162,8 @@ class TransformerBlock(nn.Module):
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class Bottleneck(nn.Module):
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# Standard bottleneck
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"""A bottleneck layer with optional shortcut and group convolution for efficient feature extraction."""
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
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"""Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
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expansion.
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@ -176,7 +182,8 @@ class Bottleneck(nn.Module):
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class BottleneckCSP(nn.Module):
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# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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"""CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
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groups, expansion.
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@ -201,7 +208,8 @@ class BottleneckCSP(nn.Module):
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class CrossConv(nn.Module):
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# Cross Convolution Downsample
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"""Implements a cross convolution layer with downsampling, expansion, and optional shortcut."""
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def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
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"""
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Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output
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@ -221,7 +229,8 @@ class CrossConv(nn.Module):
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class C3(nn.Module):
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# CSP Bottleneck with 3 convolutions
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"""Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
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convolutions, and expansion.
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@ -239,7 +248,8 @@ class C3(nn.Module):
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class C3x(C3):
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# C3 module with cross-convolutions
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"""Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups,
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and expansion.
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@ -250,7 +260,8 @@ class C3x(C3):
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class C3TR(C3):
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# C3 module with TransformerBlock()
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"""C3 module with TransformerBlock for enhanced feature extraction in object detection models."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut
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config, group, and expansion.
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@ -261,7 +272,8 @@ class C3TR(C3):
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class C3SPP(C3):
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# C3 module with SPP()
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"""Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels."""
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def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
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"""Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel
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sizes, shortcut, group, and expansion ratio.
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@ -272,7 +284,8 @@ class C3SPP(C3):
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class C3Ghost(C3):
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# C3 module with GhostBottleneck()
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"""Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5."""
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
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"""Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction."""
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super().__init__(c1, c2, n, shortcut, g, e)
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@ -281,7 +294,8 @@ class C3Ghost(C3):
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class SPP(nn.Module):
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# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
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"""Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729."""
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def __init__(self, c1, c2, k=(5, 9, 13)):
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"""Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes)."""
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super().__init__()
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@ -301,7 +315,8 @@ class SPP(nn.Module):
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class SPPF(nn.Module):
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# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
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"""Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models."""
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def __init__(self, c1, c2, k=5):
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"""
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Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
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@ -326,7 +341,8 @@ class SPPF(nn.Module):
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class Focus(nn.Module):
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# Focus wh information into c-space
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"""Focuses spatial information into channel space using slicing and convolution for efficient feature extraction."""
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
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"""Initializes Focus module to concentrate width-height info into channel space with configurable convolution
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parameters.
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@ -342,7 +358,8 @@ class Focus(nn.Module):
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class GhostConv(nn.Module):
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# Ghost Convolution https://github.com/huawei-noah/ghostnet
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"""Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet."""
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
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"""Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
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for efficiency.
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@ -359,7 +376,8 @@ class GhostConv(nn.Module):
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class GhostBottleneck(nn.Module):
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# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
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"""Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet."""
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def __init__(self, c1, c2, k=3, s=1):
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"""Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
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super().__init__()
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@ -379,7 +397,8 @@ class GhostBottleneck(nn.Module):
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class Contract(nn.Module):
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# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
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"""Contracts spatial dimensions into channel dimensions for efficient processing in neural networks."""
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def __init__(self, gain=2):
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"""Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape
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(1,64,80,80) to (1,256,40,40).
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@ -399,7 +418,8 @@ class Contract(nn.Module):
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class Expand(nn.Module):
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# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
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"""Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160)."""
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def __init__(self, gain=2):
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"""
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Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain
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@ -422,7 +442,8 @@ class Expand(nn.Module):
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class Concat(nn.Module):
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# Concatenate a list of tensors along dimension
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"""Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks."""
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def __init__(self, dimension=1):
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"""Initializes a Concat module to concatenate tensors along a specified dimension."""
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super().__init__()
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@ -436,7 +457,8 @@ class Concat(nn.Module):
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class DetectMultiBackend(nn.Module):
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# YOLOv5 MultiBackend class for python inference on various backends
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"""YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more."""
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def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True):
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"""Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX."""
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# PyTorch: weights = *.pt
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@ -778,7 +800,8 @@ class DetectMultiBackend(nn.Module):
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class AutoShape(nn.Module):
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# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
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"""AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats."""
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conf = 0.25 # NMS confidence threshold
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iou = 0.45 # NMS IoU threshold
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agnostic = False # NMS class-agnostic
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@ -889,7 +912,8 @@ class AutoShape(nn.Module):
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class Detections:
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# YOLOv5 detections class for inference results
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"""Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections."""
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def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
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"""Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization."""
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super().__init__()
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@ -1047,7 +1071,8 @@ class Detections:
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class Proto(nn.Module):
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# YOLOv5 mask Proto module for segmentation models
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"""YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors."""
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def __init__(self, c1, c_=256, c2=32):
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"""Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
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super().__init__()
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@ -1062,7 +1087,8 @@ class Proto(nn.Module):
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class Classify(nn.Module):
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# YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
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"""YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation."""
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def __init__(
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self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0
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): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
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62
models/tf.py
62
models/tf.py
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@ -49,7 +49,8 @@ from utils.general import LOGGER, make_divisible, print_args
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class TFBN(keras.layers.Layer):
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# TensorFlow BatchNormalization wrapper
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"""TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights."""
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def __init__(self, w=None):
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"""Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
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super().__init__()
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@ -67,7 +68,8 @@ class TFBN(keras.layers.Layer):
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class TFPad(keras.layers.Layer):
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# Pad inputs in spatial dimensions 1 and 2
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"""Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values."""
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def __init__(self, pad):
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"""
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Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
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@ -87,7 +89,8 @@ class TFPad(keras.layers.Layer):
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class TFConv(keras.layers.Layer):
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# Standard convolution
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"""Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow."""
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
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"""
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Initializes a standard convolution layer with optional batch normalization and activation; supports only
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@ -118,7 +121,8 @@ class TFConv(keras.layers.Layer):
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|
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class TFDWConv(keras.layers.Layer):
|
||||
# Depthwise convolution
|
||||
"""Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
|
||||
"""
|
||||
Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
|
||||
|
|
@ -147,7 +151,8 @@ class TFDWConv(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFDWConvTranspose2d(keras.layers.Layer):
|
||||
# Depthwise ConvTranspose2d
|
||||
"""Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
|
||||
"""
|
||||
Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
|
||||
|
|
@ -179,7 +184,8 @@ class TFDWConvTranspose2d(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFFocus(keras.layers.Layer):
|
||||
# Focus wh information into c-space
|
||||
"""Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models."""
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
"""
|
||||
Initializes TFFocus layer to focus width and height information into channel space with custom convolution
|
||||
|
|
@ -201,7 +207,8 @@ class TFFocus(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFBottleneck(keras.layers.Layer):
|
||||
# Standard bottleneck
|
||||
"""Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction."""
|
||||
|
||||
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""
|
||||
Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional
|
||||
|
|
@ -223,7 +230,8 @@ class TFBottleneck(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFCrossConv(keras.layers.Layer):
|
||||
# Cross Convolution
|
||||
"""Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow."""
|
||||
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
|
||||
"""Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
|
||||
super().__init__()
|
||||
|
|
@ -238,7 +246,8 @@ class TFCrossConv(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFConv2d(keras.layers.Layer):
|
||||
# Substitution for PyTorch nn.Conv2D
|
||||
"""Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride."""
|
||||
|
||||
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
|
||||
"""Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
|
||||
sizes and stride.
|
||||
|
|
@ -261,7 +270,8 @@ class TFConv2d(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFBottleneckCSP(keras.layers.Layer):
|
||||
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||||
"""Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""
|
||||
Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
|
||||
|
|
@ -289,7 +299,8 @@ class TFBottleneckCSP(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFC3(keras.layers.Layer):
|
||||
# CSP Bottleneck with 3 convolutions
|
||||
"""CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""
|
||||
Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
|
||||
|
|
@ -313,7 +324,8 @@ class TFC3(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFC3x(keras.layers.Layer):
|
||||
# 3 module with cross-convolutions
|
||||
"""A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models."""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
"""
|
||||
Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
|
||||
|
|
@ -335,7 +347,8 @@ class TFC3x(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFSPP(keras.layers.Layer):
|
||||
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||||
"""Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes."""
|
||||
|
||||
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
|
||||
"""Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
|
||||
super().__init__()
|
||||
|
|
@ -351,7 +364,8 @@ class TFSPP(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFSPPF(keras.layers.Layer):
|
||||
# Spatial pyramid pooling-Fast layer
|
||||
"""Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction."""
|
||||
|
||||
def __init__(self, c1, c2, k=5, w=None):
|
||||
"""Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
|
||||
weights.
|
||||
|
|
@ -373,7 +387,8 @@ class TFSPPF(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFDetect(keras.layers.Layer):
|
||||
# TF YOLOv5 Detect layer
|
||||
"""Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
|
||||
"""Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
|
||||
size.
|
||||
|
|
@ -427,7 +442,8 @@ class TFDetect(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFSegment(TFDetect):
|
||||
# YOLOv5 Segment head for segmentation models
|
||||
"""YOLOv5 segmentation head for TensorFlow, combining detection and segmentation."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
|
||||
"""Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
|
||||
models.
|
||||
|
|
@ -450,6 +466,8 @@ class TFSegment(TFDetect):
|
|||
|
||||
|
||||
class TFProto(keras.layers.Layer):
|
||||
"""Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation."""
|
||||
|
||||
def __init__(self, c1, c_=256, c2=32, w=None):
|
||||
"""Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
|
||||
transformation.
|
||||
|
|
@ -466,7 +484,8 @@ class TFProto(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFUpsample(keras.layers.Layer):
|
||||
# TF version of torch.nn.Upsample()
|
||||
"""Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode."""
|
||||
|
||||
def __init__(self, size, scale_factor, mode, w=None):
|
||||
"""
|
||||
Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is
|
||||
|
|
@ -488,7 +507,8 @@ class TFUpsample(keras.layers.Layer):
|
|||
|
||||
|
||||
class TFConcat(keras.layers.Layer):
|
||||
# TF version of torch.concat()
|
||||
"""Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension."""
|
||||
|
||||
def __init__(self, dimension=1, w=None):
|
||||
"""Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
|
||||
super().__init__()
|
||||
|
|
@ -581,7 +601,8 @@ def parse_model(d, ch, model, imgsz):
|
|||
|
||||
|
||||
class TFModel:
|
||||
# TF YOLOv5 model
|
||||
"""Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection."""
|
||||
|
||||
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
|
||||
"""Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input
|
||||
size.
|
||||
|
|
@ -653,7 +674,8 @@ class TFModel:
|
|||
|
||||
|
||||
class AgnosticNMS(keras.layers.Layer):
|
||||
# TF Agnostic NMS
|
||||
"""Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds."""
|
||||
|
||||
def call(self, input, topk_all, iou_thres, conf_thres):
|
||||
"""Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
|
||||
return tf.map_fn(
|
||||
|
|
|
|||
|
|
@ -70,7 +70,8 @@ except ImportError:
|
|||
|
||||
|
||||
class Detect(nn.Module):
|
||||
# YOLOv5 Detect head for detection models
|
||||
"""YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
|
||||
|
||||
stride = None # strides computed during build
|
||||
dynamic = False # force grid reconstruction
|
||||
export = False # export mode
|
||||
|
|
@ -127,7 +128,8 @@ class Detect(nn.Module):
|
|||
|
||||
|
||||
class Segment(Detect):
|
||||
# YOLOv5 Segment head for segmentation models
|
||||
"""YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
|
||||
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
|
||||
"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
|
||||
super().__init__(nc, anchors, ch, inplace)
|
||||
|
|
@ -214,7 +216,8 @@ class BaseModel(nn.Module):
|
|||
|
||||
|
||||
class DetectionModel(BaseModel):
|
||||
# YOLOv5 detection model
|
||||
"""YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
|
||||
|
||||
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
|
||||
"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
|
||||
super().__init__()
|
||||
|
|
@ -332,14 +335,16 @@ Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibilit
|
|||
|
||||
|
||||
class SegmentationModel(DetectionModel):
|
||||
# YOLOv5 segmentation model
|
||||
"""YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
|
||||
|
||||
def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
|
||||
"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
|
||||
super().__init__(cfg, ch, nc, anchors)
|
||||
|
||||
|
||||
class ClassificationModel(BaseModel):
|
||||
# YOLOv5 classification model
|
||||
"""YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
|
||||
|
||||
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
|
||||
"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
|
||||
index.
|
||||
|
|
|
|||
|
|
@ -12,7 +12,8 @@ def emojis(str=""):
|
|||
|
||||
|
||||
class TryExcept(contextlib.ContextDecorator):
|
||||
# YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
|
||||
"""A context manager and decorator for error handling that prints an optional message with emojis on exception."""
|
||||
|
||||
def __init__(self, msg=""):
|
||||
"""Initializes TryExcept with an optional message, used as a decorator or context manager for error handling."""
|
||||
self.msg = msg
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@ import torch.nn.functional as F
|
|||
|
||||
|
||||
class SiLU(nn.Module):
|
||||
"""Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish."""
|
||||
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
"""
|
||||
|
|
@ -18,6 +20,8 @@ class SiLU(nn.Module):
|
|||
|
||||
|
||||
class Hardswish(nn.Module):
|
||||
"""Applies the Hardswish activation function, which is efficient for mobile and embedded devices."""
|
||||
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
"""
|
||||
|
|
@ -38,7 +42,11 @@ class Mish(nn.Module):
|
|||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
"""Efficiently applies the Mish activation function using custom autograd for reduced memory usage."""
|
||||
|
||||
class F(torch.autograd.Function):
|
||||
"""Implements a custom autograd function for memory-efficient Mish activation."""
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
"""Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`."""
|
||||
|
|
|
|||
|
|
@ -18,7 +18,8 @@ IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
|
|||
|
||||
|
||||
class Albumentations:
|
||||
# YOLOv5 Albumentations class (optional, only used if package is installed)
|
||||
"""Provides optional data augmentation for YOLOv5 using Albumentations library if installed."""
|
||||
|
||||
def __init__(self, size=640):
|
||||
"""Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size."""
|
||||
self.transform = None
|
||||
|
|
@ -378,7 +379,8 @@ def classify_transforms(size=224):
|
|||
|
||||
|
||||
class LetterBox:
|
||||
# YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
"""Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing."""
|
||||
|
||||
def __init__(self, size=(640, 640), auto=False, stride=32):
|
||||
"""Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride
|
||||
adjustment.
|
||||
|
|
@ -405,7 +407,8 @@ class LetterBox:
|
|||
|
||||
|
||||
class CenterCrop:
|
||||
# YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
|
||||
"""Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio."""
|
||||
|
||||
def __init__(self, size=640):
|
||||
"""Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640."""
|
||||
super().__init__()
|
||||
|
|
@ -424,7 +427,8 @@ class CenterCrop:
|
|||
|
||||
|
||||
class ToTensor:
|
||||
# YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
"""Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True."""
|
||||
|
||||
def __init__(self, half=False):
|
||||
"""Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16)."""
|
||||
super().__init__()
|
||||
|
|
|
|||
|
|
@ -131,6 +131,8 @@ def seed_worker(worker_id):
|
|||
# Inherit from DistributedSampler and override iterator
|
||||
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
|
||||
class SmartDistributedSampler(distributed.DistributedSampler):
|
||||
"""A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs."""
|
||||
|
||||
def __iter__(self):
|
||||
"""Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
|
||||
g = torch.Generator()
|
||||
|
|
@ -259,7 +261,8 @@ class _RepeatSampler:
|
|||
|
||||
|
||||
class LoadScreenshots:
|
||||
# YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
|
||||
"""Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss."""
|
||||
|
||||
def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
|
||||
"""
|
||||
Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
|
||||
|
|
@ -428,7 +431,8 @@ class LoadImages:
|
|||
|
||||
|
||||
class LoadStreams:
|
||||
# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
|
||||
"""Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras."""
|
||||
|
||||
def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
|
||||
"""Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
|
||||
YouTube.
|
||||
|
|
@ -531,7 +535,8 @@ def img2label_paths(img_paths):
|
|||
|
||||
|
||||
class LoadImagesAndLabels(Dataset):
|
||||
# YOLOv5 train_loader/val_loader, loads images and labels for training and validation
|
||||
"""Loads images and their corresponding labels for training and validation in YOLOv5."""
|
||||
|
||||
cache_version = 0.6 # dataset labels *.cache version
|
||||
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
|
||||
|
||||
|
|
|
|||
|
|
@ -188,7 +188,8 @@ CONFIG_DIR = user_config_dir() # Ultralytics settings dir
|
|||
|
||||
|
||||
class Profile(contextlib.ContextDecorator):
|
||||
# YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
|
||||
"""Context manager and decorator for profiling code execution time, with optional CUDA synchronization."""
|
||||
|
||||
def __init__(self, t=0.0, device: torch.device = None):
|
||||
"""Initializes a profiling context for YOLOv5 with optional timing threshold and device specification."""
|
||||
self.t = t
|
||||
|
|
@ -213,7 +214,8 @@ class Profile(contextlib.ContextDecorator):
|
|||
|
||||
|
||||
class Timeout(contextlib.ContextDecorator):
|
||||
# YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
|
||||
"""Enforces a timeout on code execution, raising TimeoutError if the specified duration is exceeded."""
|
||||
|
||||
def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
|
||||
"""Initializes a timeout context/decorator with defined seconds, optional message, and error suppression."""
|
||||
self.seconds = int(seconds)
|
||||
|
|
@ -239,7 +241,8 @@ class Timeout(contextlib.ContextDecorator):
|
|||
|
||||
|
||||
class WorkingDirectory(contextlib.ContextDecorator):
|
||||
# Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
|
||||
"""Context manager/decorator to temporarily change the working directory within a 'with' statement or decorator."""
|
||||
|
||||
def __init__(self, new_dir):
|
||||
"""Initializes a context manager/decorator to temporarily change the working directory."""
|
||||
self.dir = new_dir # new dir
|
||||
|
|
|
|||
|
|
@ -76,7 +76,8 @@ def _json_default(value):
|
|||
|
||||
|
||||
class Loggers:
|
||||
# YOLOv5 Loggers class
|
||||
"""Initializes and manages various logging utilities for tracking YOLOv5 training and validation metrics."""
|
||||
|
||||
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
|
||||
"""Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
|
||||
self.save_dir = save_dir
|
||||
|
|
|
|||
|
|
@ -14,7 +14,8 @@ def smooth_BCE(eps=0.1):
|
|||
|
||||
|
||||
class BCEBlurWithLogitsLoss(nn.Module):
|
||||
# BCEwithLogitLoss() with reduced missing label effects.
|
||||
"""Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing."""
|
||||
|
||||
def __init__(self, alpha=0.05):
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"""Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
|
||||
parameter.
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|
|
@ -37,7 +38,8 @@ class BCEBlurWithLogitsLoss(nn.Module):
|
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|
||||
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class FocalLoss(nn.Module):
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# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
|
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"""Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters."""
|
||||
|
||||
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
|
||||
"""Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
|
||||
'none'.
|
||||
|
|
@ -71,7 +73,8 @@ class FocalLoss(nn.Module):
|
|||
|
||||
|
||||
class QFocalLoss(nn.Module):
|
||||
# Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
|
||||
"""Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence."""
|
||||
|
||||
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
|
||||
"""Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
|
||||
super().__init__()
|
||||
|
|
@ -101,6 +104,8 @@ class QFocalLoss(nn.Module):
|
|||
|
||||
|
||||
class ComputeLoss:
|
||||
"""Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses."""
|
||||
|
||||
sort_obj_iou = False
|
||||
|
||||
# Compute losses
|
||||
|
|
|
|||
|
|
@ -122,7 +122,8 @@ def compute_ap(recall, precision):
|
|||
|
||||
|
||||
class ConfusionMatrix:
|
||||
# Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
|
||||
"""Generates and visualizes a confusion matrix for evaluating object detection classification performance."""
|
||||
|
||||
def __init__(self, nc, conf=0.25, iou_thres=0.45):
|
||||
"""Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold."""
|
||||
self.matrix = np.zeros((nc + 1, nc + 1))
|
||||
|
|
|
|||
|
|
@ -29,7 +29,8 @@ matplotlib.use("Agg") # for writing to files only
|
|||
|
||||
|
||||
class Colors:
|
||||
# Ultralytics color palette https://ultralytics.com/
|
||||
"""Provides an RGB color palette derived from Ultralytics color scheme for visualization tasks."""
|
||||
|
||||
def __init__(self):
|
||||
"""
|
||||
Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.
|
||||
|
|
|
|||
|
|
@ -83,6 +83,8 @@ def create_dataloader(
|
|||
|
||||
|
||||
class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
|
||||
"""Loads images, labels, and segmentation masks for training and testing YOLO models with augmentation support."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
path,
|
||||
|
|
|
|||
|
|
@ -12,7 +12,8 @@ from .general import crop_mask
|
|||
|
||||
|
||||
class ComputeLoss:
|
||||
# Compute losses
|
||||
"""Computes the YOLOv5 model's loss components including classification, objectness, box, and mask losses."""
|
||||
|
||||
def __init__(self, model, autobalance=False, overlap=False):
|
||||
"""Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap
|
||||
handling.
|
||||
|
|
|
|||
|
|
@ -54,6 +54,8 @@ def ap_per_class_box_and_mask(
|
|||
|
||||
|
||||
class Metric:
|
||||
"""Computes performance metrics like precision, recall, F1 score, and average precision for model evaluation."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""Initializes performance metric attributes for precision, recall, F1 score, average precision, and class
|
||||
indices.
|
||||
|
|
|
|||
|
|
@ -420,7 +420,8 @@ def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, re
|
|||
|
||||
|
||||
class EarlyStopping:
|
||||
# YOLOv5 simple early stopper
|
||||
"""Implements early stopping to halt training when no improvement is observed for a specified number of epochs."""
|
||||
|
||||
def __init__(self, patience=30):
|
||||
"""Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs."""
|
||||
self.best_fitness = 0.0 # i.e. mAP
|
||||
|
|
|
|||
Loading…
Reference in New Issue