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YOLOv5 Segmentation Dataloader Updates (#2188)

* Update C3 module

* Update C3 module

* Update C3 module

* Update C3 module

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* update datasets

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* update attempt_downlaod()

* merge

* merge

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* parameterize eps

* comments

* gs-multiple

* update

* max_nms implemented

* Create one_cycle() function

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* GitHub API rate limit fix

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* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* ComputeLoss

* astuple

* epochs

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* ComputeLoss()

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* merge

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* commit=tag == tags[-1]

* Update cudnn.benchmark

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* mosaic9

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* institute cache versioning

* only display on existing cache

* reverse cache exists booleans
5.0
Glenn Jocher GitHub hace 3 años
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bdd88e1ed7
No se encontró ninguna clave conocida en la base de datos para esta firma ID de clave GPG: 4AEE18F83AFDEB23
Se han modificado 4 ficheros con 113 adiciones y 61 borrados
  1. +1
    -1
      data/scripts/get_coco.sh
  2. +76
    -58
      utils/datasets.py
  3. +35
    -1
      utils/general.py
  4. +1
    -1
      utils/loss.py

+ 1
- 1
data/scripts/get_coco.sh Ver fichero

@@ -10,7 +10,7 @@
# Download/unzip labels
d='../' # unzip directory
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
f='coco2017labels.zip' # 68 MB
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background


+ 76
- 58
utils/datasets.py Ver fichero

@@ -20,7 +20,8 @@ from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm

from utils.general import xyxy2xywh, xywh2xyxy, xywhn2xyxy, clean_str
from utils.general import xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, resample_segments, \
clean_str
from utils.torch_utils import torch_distributed_zero_first

# Parameters
@@ -374,21 +375,23 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.label_files = img2label_paths(self.img_files) # labels
cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels
if cache_path.is_file():
cache = torch.load(cache_path) # load
if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed
cache = self.cache_labels(cache_path, prefix) # re-cache
cache, exists = torch.load(cache_path), True # load
if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed
cache, exists = self.cache_labels(cache_path, prefix), False # re-cache
else:
cache = self.cache_labels(cache_path, prefix) # cache
cache, exists = self.cache_labels(cache_path, prefix), False # cache

# Display cache
[nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total
desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
tqdm(None, desc=prefix + desc, total=n, initial=n)
nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total
if exists:
d = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results
assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'

# Read cache
cache.pop('hash') # remove hash
labels, shapes = zip(*cache.values())
cache.pop('version') # remove version
labels, shapes, self.segments = zip(*cache.values())
self.labels = list(labels)
self.shapes = np.array(shapes, dtype=np.float64)
self.img_files = list(cache.keys()) # update
@@ -451,6 +454,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
im = Image.open(im_file)
im.verify() # PIL verify
shape = exif_size(im) # image size
segments = [] # instance segments
assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
assert im.format.lower() in img_formats, f'invalid image format {im.format}'

@@ -458,7 +462,12 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if os.path.isfile(lb_file):
nf += 1 # label found
with open(lb_file, 'r') as f:
l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
l = [x.split() for x in f.read().strip().splitlines()]
if any([len(x) > 8 for x in l]): # is segment
classes = np.array([x[0] for x in l], dtype=np.float32)
segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
l = np.array(l, dtype=np.float32)
if len(l):
assert l.shape[1] == 5, 'labels require 5 columns each'
assert (l >= 0).all(), 'negative labels'
@@ -470,7 +479,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
else:
nm += 1 # label missing
l = np.zeros((0, 5), dtype=np.float32)
x[im_file] = [l, shape]
x[im_file] = [l, shape, segments]
except Exception as e:
nc += 1
print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
@@ -482,7 +491,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')

x['hash'] = get_hash(self.label_files + self.img_files)
x['results'] = [nf, nm, ne, nc, i + 1]
x['results'] = nf, nm, ne, nc, i + 1
x['version'] = 0.1 # cache version
torch.save(x, path) # save for next time
logging.info(f'{prefix}New cache created: {path}')
return x
@@ -652,7 +662,7 @@ def hist_equalize(img, clahe=True, bgr=False):
def load_mosaic(self, index):
# loads images in a 4-mosaic

labels4 = []
labels4, segments4 = [], []
s = self.img_size
yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y
indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices
@@ -680,19 +690,21 @@ def load_mosaic(self, index):
padh = y1a - y1b

# Labels
labels = self.labels[index].copy()
labels, segments = self.labels[index].copy(), self.segments[index].copy()
if labels.size:
labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
labels4.append(labels)
segments4.extend(segments)

# Concat/clip labels
if len(labels4):
labels4 = np.concatenate(labels4, 0)
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective
# img4, labels4 = replicate(img4, labels4) # replicate
labels4 = np.concatenate(labels4, 0)
for x in (labels4[:, 1:], *segments4):
np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
# img4, labels4 = replicate(img4, labels4) # replicate

# Augment
img4, labels4 = random_perspective(img4, labels4,
img4, labels4 = random_perspective(img4, labels4, segments4,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],
scale=self.hyp['scale'],
@@ -706,7 +718,7 @@ def load_mosaic(self, index):
def load_mosaic9(self, index):
# loads images in a 9-mosaic

labels9 = []
labels9, segments9 = [], []
s = self.img_size
indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices
for i, index in enumerate(indices):
@@ -739,30 +751,34 @@ def load_mosaic9(self, index):
x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords

# Labels
labels = self.labels[index].copy()
labels, segments = self.labels[index].copy(), self.segments[index].copy()
if labels.size:
labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
labels9.append(labels)
segments9.extend(segments)

# Image
img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
hp, wp = h, w # height, width previous

# Offset
yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y
yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y
img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]

# Concat/clip labels
if len(labels9):
labels9 = np.concatenate(labels9, 0)
labels9[:, [1, 3]] -= xc
labels9[:, [2, 4]] -= yc
labels9 = np.concatenate(labels9, 0)
labels9[:, [1, 3]] -= xc
labels9[:, [2, 4]] -= yc
c = np.array([xc, yc]) # centers
segments9 = [x - c for x in segments9]

np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective
# img9, labels9 = replicate(img9, labels9) # replicate
for x in (labels9[:, 1:], *segments9):
np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
# img9, labels9 = replicate(img9, labels9) # replicate

# Augment
img9, labels9 = random_perspective(img9, labels9,
img9, labels9 = random_perspective(img9, labels9, segments9,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],
scale=self.hyp['scale'],
@@ -823,7 +839,8 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale
return img, ratio, (dw, dh)


def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
border=(0, 0)):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]

@@ -875,37 +892,38 @@ def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shea
# Transform label coordinates
n = len(targets)
if n:
# warp points
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
if perspective:
xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale
else: # affine
xy = xy[:, :2].reshape(n, 8)

# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

# # apply angle-based reduction of bounding boxes
# radians = a * math.pi / 180
# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
# x = (xy[:, 2] + xy[:, 0]) / 2
# y = (xy[:, 3] + xy[:, 1]) / 2
# w = (xy[:, 2] - xy[:, 0]) * reduction
# h = (xy[:, 3] - xy[:, 1]) * reduction
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

# clip boxes
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
use_segments = any(x.any() for x in segments)
new = np.zeros((n, 4))
if use_segments: # warp segments
segments = resample_segments(segments) # upsample
for i, segment in enumerate(segments):
xy = np.ones((len(segment), 3))
xy[:, :2] = segment
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine

# clip
new[i] = segment2box(xy, width, height)

else: # warp boxes
xy = np.ones((n * 4, 3))
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine

# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

# clip
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)

# filter candidates
i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
targets = targets[i]
targets[:, 1:5] = xy[i]
targets[:, 1:5] = new[i]

return img, targets


+ 35
- 1
utils/general.py Ver fichero

@@ -225,7 +225,7 @@ def xywh2xyxy(x):
return y


def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x
@@ -235,6 +235,40 @@ def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32):
return y


def xyn2xy(x, w=640, h=640, padw=0, padh=0):
# Convert normalized segments into pixel segments, shape (n,2)
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = w * x[:, 0] + padw # top left x
y[:, 1] = h * x[:, 1] + padh # top left y
return y


def segment2box(segment, width=640, height=640):
# Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
x, y = segment.T # segment xy
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
x, y, = x[inside], y[inside]
return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # cls, xyxy


def segments2boxes(segments):
# Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
boxes = []
for s in segments:
x, y = s.T # segment xy
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
return xyxy2xywh(np.array(boxes)) # cls, xywh


def resample_segments(segments, n=1000):
# Up-sample an (n,2) segment
for i, s in enumerate(segments):
x = np.linspace(0, len(s) - 1, n)
xp = np.arange(len(s))
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
return segments


def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape

+ 1
- 1
utils/loss.py Ver fichero

@@ -105,7 +105,7 @@ class ComputeLoss:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)

det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
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]
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]
self.ssi = (det.stride == 16).nonzero(as_tuple=False).item() # stride 16 index
self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
for k in 'na', 'nc', 'nl', 'anchors':

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