Add docstrings to YOLOv5 functions (#12760)
* Add docstrings to top level files * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Update activations.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@@ -31,7 +31,11 @@ matplotlib.use("Agg") # for writing to files only
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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"""
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Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.
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Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`.
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"""
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hexs = (
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"FF3838",
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"FF9D97",
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@@ -58,6 +62,7 @@ class Colors:
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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"""Returns color from palette by index `i`, in BGR format if `bgr=True`, else RGB; `i` is an integer index."""
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@@ -100,7 +105,11 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detec
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def hist2d(x, y, n=100):
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# 2d histogram used in labels.png and evolve.png
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"""
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Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions.
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Used in used in labels.png and evolve.png.
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"""
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xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
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hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
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xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
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@@ -109,6 +118,7 @@ def hist2d(x, y, n=100):
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def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
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"""Applies a low-pass Butterworth filter to `data` with specified `cutoff`, `fs`, and `order`."""
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from scipy.signal import butter, filtfilt
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# https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
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@@ -122,7 +132,9 @@ def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
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def output_to_target(output, max_det=300):
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# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
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"""Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections
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to `max_det`.
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"""
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targets = []
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for i, o in enumerate(output):
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box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
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@@ -133,7 +145,7 @@ def output_to_target(output, max_det=300):
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@threaded
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def plot_images(images, targets, paths=None, fname="images.jpg", names=None):
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# Plot image grid with labels
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"""Plots an image grid with labels from YOLOv5 predictions or targets, saving to `fname`."""
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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if isinstance(targets, torch.Tensor):
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@@ -197,7 +209,7 @@ def plot_images(images, targets, paths=None, fname="images.jpg", names=None):
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def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""):
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# Plot LR simulating training for full epochs
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"""Plots learning rate schedule for given optimizer and scheduler, saving plot to `save_dir`."""
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optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
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y = []
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for _ in range(epochs):
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@@ -295,7 +307,7 @@ def plot_val_study(file="", dir="", x=None): # from utils.plots import *; plot_
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@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
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def plot_labels(labels, names=(), save_dir=Path("")):
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# plot dataset labels
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"""Plots dataset labels, saving correlogram and label images, handles classes, and visualizes bounding boxes."""
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LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
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c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
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nc = int(c.max() + 1) # number of classes
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@@ -340,7 +352,7 @@ def plot_labels(labels, names=(), save_dir=Path("")):
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def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")):
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# Show classification image grid with labels (optional) and predictions (optional)
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"""Displays a grid of images with optional labels and predictions, saving to a file."""
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from utils.augmentations import denormalize
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names = names or [f"class{i}" for i in range(1000)]
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@@ -397,7 +409,11 @@ def plot_evolve(evolve_csv="path/to/evolve.csv"): # from utils.plots import *;
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def plot_results(file="path/to/results.csv", dir=""):
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# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
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"""
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Plots training results from a 'results.csv' file; accepts file path and directory as arguments.
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Example: from utils.plots import *; plot_results('path/to/results.csv')
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"""
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save_dir = Path(file).parent if file else Path(dir)
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fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
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ax = ax.ravel()
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@@ -424,7 +440,11 @@ def plot_results(file="path/to/results.csv", dir=""):
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def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
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# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
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"""
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Plots per-image iDetection logs, comparing metrics like storage and performance over time.
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Example: from utils.plots import *; profile_idetection()
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"""
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ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
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s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"]
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files = list(Path(save_dir).glob("frames*.txt"))
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@@ -455,7 +475,9 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
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def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
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# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
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"""Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts
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for BGR.
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"""
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xyxy = torch.tensor(xyxy).view(-1, 4)
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b = xyxy2xywh(xyxy) # boxes
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if square:
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