From 114470361e4c8160f8c27b229494c7f056777134 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 15 Jul 2023 01:00:23 +0200 Subject: [PATCH] Remove deprecated `AutoShape` class (#3743) --- docs/reference/nn/autoshape.md | 14 -- mkdocs.yml | 1 - ultralytics/nn/autoshape.py | 244 --------------------------------- 3 files changed, 259 deletions(-) delete mode 100644 docs/reference/nn/autoshape.md delete mode 100644 ultralytics/nn/autoshape.py diff --git a/docs/reference/nn/autoshape.md b/docs/reference/nn/autoshape.md deleted file mode 100644 index b009e09..0000000 --- a/docs/reference/nn/autoshape.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now. -keywords: Ultralytics, YOLO, docs, autoshape, detections, object detection, customized shapes, bounding boxes, computer vision ---- - -## AutoShape ---- -### ::: ultralytics.nn.autoshape.AutoShape -

- -## Detections ---- -### ::: ultralytics.nn.autoshape.Detections -

diff --git a/mkdocs.yml b/mkdocs.yml index 75f8a74..219a059 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -257,7 +257,6 @@ nav: - utils: reference/hub/utils.md - nn: - autobackend: reference/nn/autobackend.md - - autoshape: reference/nn/autoshape.md - modules: - block: reference/nn/modules/block.md - conv: reference/nn/modules/conv.md diff --git a/ultralytics/nn/autoshape.py b/ultralytics/nn/autoshape.py deleted file mode 100644 index d557f78..0000000 --- a/ultralytics/nn/autoshape.py +++ /dev/null @@ -1,244 +0,0 @@ -# Ultralytics YOLO 🚀, AGPL-3.0 license -""" -Common modules -""" - -from copy import copy -from pathlib import Path - -import cv2 -import numpy as np -import requests -import torch -import torch.nn as nn -from PIL import Image, ImageOps -from torch.cuda import amp - -from ultralytics.nn.autobackend import AutoBackend -from ultralytics.yolo.data.augment import LetterBox -from ultralytics.yolo.utils import LOGGER, colorstr -from ultralytics.yolo.utils.files import increment_path -from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh -from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box -from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode - - -class AutoShape(nn.Module): - """YOLOv8 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS.""" - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - agnostic = False # NMS class-agnostic - multi_label = False # NMS multiple labels per box - classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs - max_det = 1000 # maximum number of detections per image - amp = False # Automatic Mixed Precision (AMP) inference - - def __init__(self, model, verbose=True): - """Initializes object and copies attributes from model object.""" - super().__init__() - if verbose: - LOGGER.info('Adding AutoShape... ') - copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes - self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance - self.pt = not self.dmb or model.pt # PyTorch model - self.model = model.eval() - if self.pt: - m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() - m.inplace = False # Detect.inplace=False for safe multithread inference - m.export = True # do not output loss values - - def _apply(self, fn): - """Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers.""" - self = super()._apply(fn) - if self.pt: - m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() - m.stride = fn(m.stride) - m.grid = list(map(fn, m.grid)) - if isinstance(m.anchor_grid, list): - m.anchor_grid = list(map(fn, m.anchor_grid)) - return self - - @smart_inference_mode() - def forward(self, ims, size=640, augment=False, profile=False): - """Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:.""" - # file: ims = 'data/images/zidane.jpg' # str or PosixPath - # URI: = 'https://ultralytics.com/images/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) - # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) - # numpy: = np.zeros((640,1280,3)) # HWC - # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - dt = (Profile(), Profile(), Profile()) - with dt[0]: - if isinstance(size, int): # expand - size = (size, size) - p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param - autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference - if isinstance(ims, torch.Tensor): # torch - with amp.autocast(autocast): - return self.model(ims.to(p.device).type_as(p), augment=augment) # inference - - # Preprocess - n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images - shape0, shape1, files = [], [], [] # image and inference shapes, filenames - for i, im in enumerate(ims): - f = f'image{i}' # filename - if isinstance(im, (str, Path)): # filename or uri - im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im - im = np.asarray(ImageOps.exif_transpose(im)) - elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = max(size) / max(s) # gain - shape1.append([y * g for y in s]) - ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update - shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape - x = [LetterBox(shape1, auto=False)(image=im)['img'] for im in ims] # pad - x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 - - with amp.autocast(autocast): - # Inference - with dt[1]: - y = self.model(x, augment=augment) # forward - - # Postprocess - with dt[2]: - y = non_max_suppression(y if self.dmb else y[0], - self.conf, - self.iou, - self.classes, - self.agnostic, - self.multi_label, - max_det=self.max_det) # NMS - for i in range(n): - scale_boxes(shape1, y[i][:, :4], shape0[i]) - - return Detections(ims, y, files, dt, self.names, x.shape) - - -class Detections: - """ YOLOv8 detections class for inference results""" - - def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): - """Initialize object attributes for YOLO detection results.""" - super().__init__() - d = pred[0].device # device - gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations - self.ims = ims # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.files = files # image filenames - self.times = times # profiling times - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) # number of images (batch size) - self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) - self.s = tuple(shape) # inference BCHW shape - - def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): - """Return performance metrics and optionally cropped/save images or results.""" - s, crops = '', [] - for i, (im, pred) in enumerate(zip(self.ims, self.pred)): - s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string - if pred.shape[0]: - for c in pred[:, -1].unique(): - n = (pred[:, -1] == c).sum() # detections per class - s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - s = s.rstrip(', ') - if show or save or render or crop: - annotator = Annotator(im, example=str(self.names)) - for *box, conf, cls in reversed(pred): # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' - if crop: - file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None - crops.append({ - 'box': box, - 'conf': conf, - 'cls': cls, - 'label': label, - 'im': save_one_box(box, im, file=file, save=save)}) - else: # all others - annotator.box_label(box, label if labels else '', color=colors(cls)) - im = annotator.im - else: - s += '(no detections)' - - im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np - if show: - im.show(self.files[i]) # show - if save: - f = self.files[i] - im.save(save_dir / f) # save - if i == self.n - 1: - LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") - if render: - self.ims[i] = np.asarray(im) - if pprint: - s = s.lstrip('\n') - return f'{s}\nSpeed: %.1fms preprocess, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t - if crop: - if save: - LOGGER.info(f'Saved results to {save_dir}\n') - return crops - - def show(self, labels=True): - """Displays YOLO results with detected bounding boxes.""" - self._run(show=True, labels=labels) # show results - - def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): - """Save detection results with optional labels to specified directory.""" - save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir - self._run(save=True, labels=labels, save_dir=save_dir) # save results - - def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): - """Crops images into detections and saves them if 'save' is True.""" - save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None - return self._run(crop=True, save=save, save_dir=save_dir) # crop results - - def render(self, labels=True): - """Renders detected objects and returns images.""" - self._run(render=True, labels=labels) # render results - return self.ims - - def pandas(self): - """Return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]).""" - import pandas - new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): - a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update - setattr(new, k, [pandas.DataFrame(x, columns=c) for x in a]) - return new - - def tolist(self): - """Return a list of Detections objects, i.e. 'for result in results.tolist():'.""" - r = range(self.n) # iterable - x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] - # for d in x: - # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: - # setattr(d, k, getattr(d, k)[0]) # pop out of list - return x - - def print(self): - """Print the results of the `self._run()` function.""" - LOGGER.info(self.__str__()) - - def __len__(self): # override len(results) - return self.n - - def __str__(self): # override print(results) - return self._run(pprint=True) # print results - - def __repr__(self): - """Returns a printable representation of the object.""" - return f'YOLOv8 {self.__class__} instance\n' + self.__str__()