YOLOv5 Segmentation Dataloader Updates (#2188)
* Update C3 module * Update C3 module * Update C3 module * Update C3 module * update * update * update * update * update * update * update * update * update * updates * updates * updates * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * updates * updates * updates * updates * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update datasets * update * update * update * update attempt_downlaod() * merge * merge * update * update * update * update * update * update * update * update * update * update * parameterize eps * comments * gs-multiple * update * max_nms implemented * Create one_cycle() function * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * update * GitHub API rate limit fix * update * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * ComputeLoss * astuple * epochs * update * update * ComputeLoss() * update * update * update * update * update * update * update * update * update * update * update * merge * merge * merge * merge * update * update * update * update * commit=tag == tags[-1] * Update cudnn.benchmark * update * update * update * updates * updates * updates * updates * updates * updates * updates * update * update * update * update * update * mosaic9 * update * update * update * update * update * update * institute cache versioning * only display on existing cache * reverse cache exists booleanspull/2219/head
Glenn Jocher
GitHub
parent
404749a33c
commit
bdd88e1ed7
data/scripts
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@ -10,7 +10,7 @@
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# Download/unzip labels
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d='../' # unzip directory
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url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
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f='coco2017labels.zip' # 68 MB
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f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
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echo 'Downloading' $url$f ' ...'
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curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
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@ -20,7 +20,8 @@ from PIL import Image, ExifTags
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from utils.general import xyxy2xywh, xywh2xyxy, xywhn2xyxy, clean_str
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from utils.general import xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, resample_segments, \
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clean_str
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from utils.torch_utils import torch_distributed_zero_first
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# Parameters
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@ -374,21 +375,23 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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self.label_files = img2label_paths(self.img_files) # labels
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cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels
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if cache_path.is_file():
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cache = torch.load(cache_path) # load
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if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed
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cache = self.cache_labels(cache_path, prefix) # re-cache
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cache, exists = torch.load(cache_path), True # load
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if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed
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cache, exists = self.cache_labels(cache_path, prefix), False # re-cache
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else:
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cache = self.cache_labels(cache_path, prefix) # cache
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cache, exists = self.cache_labels(cache_path, prefix), False # cache
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# Display cache
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[nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total
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desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
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tqdm(None, desc=prefix + desc, total=n, initial=n)
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nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total
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if exists:
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d = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
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tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results
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assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
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# Read cache
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cache.pop('hash') # remove hash
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labels, shapes = zip(*cache.values())
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cache.pop('version') # remove version
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labels, shapes, self.segments = zip(*cache.values())
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self.labels = list(labels)
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self.shapes = np.array(shapes, dtype=np.float64)
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self.img_files = list(cache.keys()) # update
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@ -451,6 +454,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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im = Image.open(im_file)
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im.verify() # PIL verify
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shape = exif_size(im) # image size
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segments = [] # instance segments
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assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
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assert im.format.lower() in img_formats, f'invalid image format {im.format}'
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@ -458,7 +462,12 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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if os.path.isfile(lb_file):
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nf += 1 # label found
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with open(lb_file, 'r') as f:
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l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
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l = [x.split() for x in f.read().strip().splitlines()]
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if any([len(x) > 8 for x in l]): # is segment
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classes = np.array([x[0] for x in l], dtype=np.float32)
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segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
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l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
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l = np.array(l, dtype=np.float32)
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if len(l):
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assert l.shape[1] == 5, 'labels require 5 columns each'
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assert (l >= 0).all(), 'negative labels'
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@ -470,7 +479,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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else:
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nm += 1 # label missing
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l = np.zeros((0, 5), dtype=np.float32)
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x[im_file] = [l, shape]
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x[im_file] = [l, shape, segments]
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except Exception as e:
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nc += 1
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print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
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@ -482,7 +491,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
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x['hash'] = get_hash(self.label_files + self.img_files)
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x['results'] = [nf, nm, ne, nc, i + 1]
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x['results'] = nf, nm, ne, nc, i + 1
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x['version'] = 0.1 # cache version
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torch.save(x, path) # save for next time
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logging.info(f'{prefix}New cache created: {path}')
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return x
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@ -652,7 +662,7 @@ def hist_equalize(img, clahe=True, bgr=False):
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def load_mosaic(self, index):
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# loads images in a 4-mosaic
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labels4 = []
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labels4, segments4 = [], []
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s = self.img_size
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yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
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indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices
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@ -680,19 +690,21 @@ def load_mosaic(self, index):
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padh = y1a - y1b
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# Labels
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labels = self.labels[index].copy()
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labels, segments = self.labels[index].copy(), self.segments[index].copy()
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if labels.size:
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labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
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segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
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labels4.append(labels)
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segments4.extend(segments)
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# Concat/clip labels
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if len(labels4):
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labels4 = np.concatenate(labels4, 0)
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np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective
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# img4, labels4 = replicate(img4, labels4) # replicate
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labels4 = np.concatenate(labels4, 0)
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for x in (labels4[:, 1:], *segments4):
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np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
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# img4, labels4 = replicate(img4, labels4) # replicate
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# Augment
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img4, labels4 = random_perspective(img4, labels4,
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img4, labels4 = random_perspective(img4, labels4, segments4,
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degrees=self.hyp['degrees'],
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translate=self.hyp['translate'],
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scale=self.hyp['scale'],
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@ -706,7 +718,7 @@ def load_mosaic(self, index):
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def load_mosaic9(self, index):
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# loads images in a 9-mosaic
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labels9 = []
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labels9, segments9 = [], []
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s = self.img_size
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indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices
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for i, index in enumerate(indices):
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@ -739,30 +751,34 @@ def load_mosaic9(self, index):
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x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords
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# Labels
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labels = self.labels[index].copy()
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labels, segments = self.labels[index].copy(), self.segments[index].copy()
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if labels.size:
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labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
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segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
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labels9.append(labels)
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segments9.extend(segments)
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# Image
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img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
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hp, wp = h, w # height, width previous
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# Offset
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yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y
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yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y
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img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
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# Concat/clip labels
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if len(labels9):
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labels9 = np.concatenate(labels9, 0)
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labels9[:, [1, 3]] -= xc
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labels9[:, [2, 4]] -= yc
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labels9 = np.concatenate(labels9, 0)
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labels9[:, [1, 3]] -= xc
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labels9[:, [2, 4]] -= yc
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c = np.array([xc, yc]) # centers
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segments9 = [x - c for x in segments9]
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np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective
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# img9, labels9 = replicate(img9, labels9) # replicate
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for x in (labels9[:, 1:], *segments9):
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np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
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# img9, labels9 = replicate(img9, labels9) # replicate
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# Augment
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img9, labels9 = random_perspective(img9, labels9,
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img9, labels9 = random_perspective(img9, labels9, segments9,
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degrees=self.hyp['degrees'],
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translate=self.hyp['translate'],
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scale=self.hyp['scale'],
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@ -823,7 +839,8 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale
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return img, ratio, (dw, dh)
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def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
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def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
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border=(0, 0)):
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
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# targets = [cls, xyxy]
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@ -875,37 +892,38 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea
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# Transform label coordinates
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n = len(targets)
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if n:
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# warp points
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xy = np.ones((n * 4, 3))
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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if perspective:
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xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
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else: # affine
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xy = xy[:, :2].reshape(n, 8)
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use_segments = any(x.any() for x in segments)
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new = np.zeros((n, 4))
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if use_segments: # warp segments
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segments = resample_segments(segments) # upsample
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for i, segment in enumerate(segments):
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xy = np.ones((len(segment), 3))
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xy[:, :2] = segment
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xy = xy @ M.T # transform
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xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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# clip
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new[i] = segment2box(xy, width, height)
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# # apply angle-based reduction of bounding boxes
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# radians = a * math.pi / 180
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# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
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# x = (xy[:, 2] + xy[:, 0]) / 2
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# y = (xy[:, 3] + xy[:, 1]) / 2
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# w = (xy[:, 2] - xy[:, 0]) * reduction
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# h = (xy[:, 3] - xy[:, 1]) * reduction
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# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
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else: # warp boxes
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xy = np.ones((n * 4, 3))
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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# clip boxes
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xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
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xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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# clip
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new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
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new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
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# filter candidates
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
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targets = targets[i]
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targets[:, 1:5] = xy[i]
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targets[:, 1:5] = new[i]
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return img, targets
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@ -225,7 +225,7 @@ def xywh2xyxy(x):
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return y
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def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
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def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
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# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
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@ -235,6 +235,40 @@ def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
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return y
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def xyn2xy(x, w=640, h=640, padw=0, padh=0):
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# Convert normalized segments into pixel segments, shape (n,2)
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y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
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y[:, 0] = w * x[:, 0] + padw # top left x
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y[:, 1] = h * x[:, 1] + padh # top left y
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return y
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def segment2box(segment, width=640, height=640):
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# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
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x, y = segment.T # segment xy
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inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
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x, y, = x[inside], y[inside]
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return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # cls, xyxy
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def segments2boxes(segments):
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# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
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boxes = []
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for s in segments:
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x, y = s.T # segment xy
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boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
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return xyxy2xywh(np.array(boxes)) # cls, xywh
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def resample_segments(segments, n=1000):
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# Up-sample an (n,2) segment
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for i, s in enumerate(segments):
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x = np.linspace(0, len(s) - 1, n)
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xp = np.arange(len(s))
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segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
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return segments
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def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
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# Rescale coords (xyxy) from img1_shape to img0_shape
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if ratio_pad is None: # calculate from img0_shape
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@ -105,7 +105,7 @@ class ComputeLoss:
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BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
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det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
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self.balance = {3: [3.67, 1.0, 0.43], 4: [4.0, 1.0, 0.25, 0.06], 5: [4.0, 1.0, 0.25, 0.06, .02]}[det.nl]
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self.balance = {3: [4.0, 1.0, 0.4], 4: [4.0, 1.0, 0.25, 0.06], 5: [4.0, 1.0, 0.25, 0.06, .02]}[det.nl]
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self.ssi = (det.stride == 16).nonzero(as_tuple=False).item() # stride 16 index
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self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
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for k in 'na', 'nc', 'nl', 'anchors':
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Reference in New Issue