def __init__(self,
path,
img_size=640,
batch_size=16,
augment=False,
hyp=None,
rect=False,
image_weights=False,
cache_images=False,
single_cls=False,
stride=32,
pad=0.0):
try:
f = [] # image files
for p in path if isinstance(path, list) else [path]:
p = str(Path(p)) # os-agnostic
parent = str(Path(p).parent) + os.sep
if os.path.isfile(p): # file
with open(p, 'r') as t:
t = t.read().splitlines()
f += [
x.replace('./', parent)
if x.startswith('./') else x for x in t
] # local to global path
elif os.path.isdir(p): # folder
f += glob.iglob(p + os.sep + '*.*')
else:
raise Exception('%s does not exist' % p)
self.img_files = [
x.replace('/', os.sep) for x in f
if os.path.splitext(x)[-1].lower() in img_formats
]
except Exception as e:
raise Exception('Error loading data from %s: %s\nSee %s' %
(path, e, help_url))
n = len(self.img_files)
assert n > 0, 'No images found in %s. See %s' % (path, help_url)
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
self.n = n # number of images
self.batch = bi # batch index of image
self.img_size = img_size
self.augment = augment
self.hyp = hyp
self.image_weights = image_weights
self.rect = False if image_weights else rect
self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
self.mosaic_border = [-img_size // 2, -img_size // 2]
self.stride = stride
# Define labels
self.label_files = [
x.replace('images',
'labels').replace(os.path.splitext(x)[-1], '.txt')
for x in self.img_files
]
# Check cache
cache_path = str(Path(
self.label_files[0]).parent) + '.cache' # cached labels
if os.path.isfile(cache_path):
cache = torch.load(cache_path) # load
if cache['hash'] != get_hash(self.label_files +
self.img_files): # dataset changed
cache = self.cache_labels(cache_path) # re-cache
else:
cache = self.cache_labels(cache_path) # cache
# Get labels
labels, shapes = zip(*[cache[x] for x in self.img_files])
self.shapes = np.array(shapes, dtype=np.float64)
self.labels = list(labels)
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect:
# Sort by aspect ratio
s = self.shapes # wh
ar = s[:, 1] / s[:, 0] # aspect ratio
irect = ar.argsort()
self.img_files = [self.img_files[i] for i in irect]
self.label_files = [self.label_files[i] for i in irect]
self.labels = [self.labels[i] for i in irect]
self.shapes = s[irect] # wh
ar = ar[irect]
# Set training image shapes
shapes = [[1, 1]] * nb
for i in range(nb):
ari = ar[bi == i]
mini, maxi = ari.min(), ari.max()
if maxi < 1:
shapes[i] = [maxi, 1]
elif mini > 1:
shapes[i] = [1, 1 / mini]
self.batch_shapes = np.ceil(
np.array(shapes) * img_size / stride + pad).astype(
np.int) * stride
# Cache labels
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
pbar = tqdm(self.label_files)
for i, file in enumerate(pbar):
l = self.labels[i] # label
if l.shape[0]:
assert l.shape[1] == 5, '> 5 label columns: %s' % file
assert (l >= 0).all(), 'negative labels: %s' % file
assert (l[:, 1:] <= 1).all(
), 'non-normalized or out of bounds coordinate labels: %s' % file
if np.unique(l,
axis=0).shape[0] < l.shape[0]: # duplicate rows
nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
if single_cls:
l[:, 0] = 0 # force dataset into single-class mode
self.labels[i] = l
nf += 1 # file found
# Create subdataset (a smaller dataset)
if create_datasubset and ns < 1E4:
if ns == 0:
create_folder(path='./datasubset')
os.makedirs('./datasubset/images')
exclude_classes = 43
if exclude_classes not in l[:, 0]:
ns += 1
# shutil.copy(src=self.img_files[i], dst='./datasubset/images/') # copy image
with open('./datasubset/images.txt', 'a') as f:
f.write(self.img_files[i] + '\n')
# Extract object detection boxes for a second stage classifier
if extract_bounding_boxes:
p = Path(self.img_files[i])
img = cv2.imread(str(p))
h, w = img.shape[:2]
for j, x in enumerate(l):
f = '%s%sclassifier%s%g_%g_%s' % (
p.parent.parent, os.sep, os.sep, x[0], j, p.name)
if not os.path.exists(Path(f).parent):
os.makedirs(
Path(f).parent) # make new output folder
b = x[1:] * [w, h, w, h] # box
b[2:] = b[2:].max() # rectangle to square
b[2:] = b[2:] * 1.3 + 30 # pad
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
b[[0, 2]] = np.clip(b[[0, 2]], 0,
w) # clip boxes outside of image
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
assert cv2.imwrite(f, img[
b[1]:b[3],
b[0]:b[2]]), 'Failure extracting classifier boxes'
else:
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
cache_path, nf, nm, ne, nd, n)
if nf == 0:
s = 'WARNING: No labels found in %s. See %s' % (
os.path.dirname(file) + os.sep, help_url)
print(s)
assert not augment, '%s. Can not train without labels.' % s
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
self.imgs = [None] * n
if cache_images:
gb = 0 # Gigabytes of cached images
pbar = tqdm(range(len(self.img_files)), desc='Caching images')
self.img_hw0, self.img_hw = [None] * n, [None] * n
for i in pbar: # max 10k images
self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(
self, i) # img, hw_original, hw_resized
gb += self.imgs[i].nbytes
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
def make_results_test(model, dataset, device='cuda'):
model.eval()
results = []
seen, stats = 0, []
iouv = torch.linspace(0.5, 0.95, 10)
niou = iouv.numel()
nb, _, height, width = dataset[0].shape
whwh = torch.Tensor([width, height, width, height])
# model output
with torch.no_grad():
outputs = model((dataset[0] / 255.).to(device))
# results list
output = []
for out in outputs:
output.append(
torch.cat([
out['boxes'], out['scores'].unsqueeze(1),
out['labels'].unsqueeze(1).type(torch.float) - 1
],
axis=1))
targets = dataset[1]
for si, pred in enumerate(output):
pred = pred.cpu()
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else []
if pred is None:
if nl:
stats.append(torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls)
stats_return = [(torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls)]
stats = [np.concatenate(x, 0)
for x in zip(*stats_return)] # to numpy
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats)
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(
1) # [P, R, [email protected], [email protected]:0.95]
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(
), ap.mean()
nt = np.bincount(stats[3].astype(
np.int64), minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
source_path = str(dataset[2][si].split(
os.sep)[-1].split('__')[0])
results.append((source_path, dataset[2][si], mp, mr, map50, nl,
stats_return))
continue
# clip boxes
clip_coords(pred, (height, width))
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
if nl:
detected = []
tcls_tensor = labels[:, 0]
tbox = xywh2xyxy(labels[:, 1:5]) * whwh
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero().view(-1)
pi = (cls == pred[:, 5]).nonzero().view(-1)
if pi.shape[0]:
ious, j = box_iou(pred[pi, :4], tbox[ti]).max(1)
for k in (ious > iouv[0]).nonzero():
d = ti[j[k]]
if d not in detected:
detected.append(d)
correct[pi[k]] = ious[k].cpu() > iouv.cpu()
if len(detected) == nl:
break
stats_return = [(correct.cpu(), pred[:, 4].cpu(), pred[:,
5].cpu(), tcls)]
stats = [np.concatenate(x, 0) for x in zip(*stats_return)]
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats)
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(
1) # [P, R, [email protected], [email protected]:0.95]
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(np.int64),
minlength=1) # number of targets per class
else:
nt = torch.zeros(1)
source_path = str(dataset[2][si].split(os.sep)[-1].split('__')[0])
results.append(
(source_path, dataset[2][si], mp, mr, map50, nl, stats_return))
return results
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_images=False, single_cls=False, stride=32, pad=0.0):
try:
path = str(Path(path)) # os-agnostic
parent = str(Path(path).parent) + os.sep
if os.path.isfile(path): # file
with open(path, 'r') as f:
f = f.read().splitlines()
f = [x.replace('./', parent) if x.startswith('./') else x for x in f] # local to global path
elif os.path.isdir(path): # folder
f = glob.iglob(path + os.sep + '*.*')
else:
raise Exception('%s does not exist' % path)
self.img_files = [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats]
except:
raise Exception('Error loading data from %s. See %s' % (path, help_url))
n = len(self.img_files)
assert n > 0, 'No images found in %s. See %s' % (path, help_url)
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
self.n = n # number of images
self.batch = bi # batch index of image
self.img_size = img_size
self.augment = augment
self.hyp = hyp
self.image_weights = image_weights
self.rect = False if image_weights else rect
self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
# Define labels
self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
for x in self.img_files]
# Read image shapes (wh)
sp = path.replace('.txt', '') + '.shapes' # shapefile path
try:
with open(sp, 'r') as f: # read existing shapefile
s = [x.split() for x in f.read().splitlines()]
assert len(s) == n, 'Shapefile out of sync'
except:
s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
np.savetxt(sp, s, fmt='%g') # overwrites existing (if any)
self.shapes = np.array(s, dtype=np.float64)
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect:
# Sort by aspect ratio
s = self.shapes # wh
ar = s[:, 1] / s[:, 0] # aspect ratio
irect = ar.argsort()
self.img_files = [self.img_files[i] for i in irect]
self.label_files = [self.label_files[i] for i in irect]
self.shapes = s[irect] # wh
ar = ar[irect]
# Set training image shapes
shapes = [[1, 1]] * nb
for i in range(nb):
ari = ar[bi == i]
mini, maxi = ari.min(), ari.max()
if maxi < 1:
shapes[i] = [maxi, 1]
elif mini > 1:
shapes[i] = [1, 1 / mini]
self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
# Cache labels
self.imgs = [None] * n
self.labels = [np.zeros((0, 5), dtype=np.float32)] * n
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
np_labels_path = str(Path(self.label_files[0]).parent) + '.npy' # saved labels in *.npy file
if os.path.isfile(np_labels_path):
s = np_labels_path # print string
x = np.load(np_labels_path, allow_pickle=True)
if len(x) == n:
self.labels = x
labels_loaded = True
else:
s = path.replace('images', 'labels')
pbar = tqdm(self.label_files)
for i, file in enumerate(pbar):
if labels_loaded:
l = self.labels[i]
# np.savetxt(file, l, '%g') # save *.txt from *.npy file
else:
try:
with open(file, 'r') as f:
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
except:
nm += 1 # print('missing labels for image %s' % self.img_files[i]) # file missing
continue
if l.shape[0]:
assert l.shape[1] == 5, '> 5 label columns: %s' % file
assert (l >= 0).all(), 'negative labels: %s' % file
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows
nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
if single_cls:
l[:, 0] = 0 # force dataset into single-class mode
self.labels[i] = l
nf += 1 # file found
# Create subdataset (a smaller dataset)
if create_datasubset and ns < 1E4:
if ns == 0:
create_folder(path='./datasubset')
os.makedirs('./datasubset/images')
exclude_classes = 43
if exclude_classes not in l[:, 0]:
ns += 1
# shutil.copy(src=self.img_files[i], dst='./datasubset/images/') # copy image
with open('./datasubset/images.txt', 'a') as f:
f.write(self.img_files[i] + '\n')
# Extract object detection boxes for a second stage classifier
if extract_bounding_boxes:
p = Path(self.img_files[i])
img = cv2.imread(str(p))
h, w = img.shape[:2]
for j, x in enumerate(l):
f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
if not os.path.exists(Path(f).parent):
os.makedirs(Path(f).parent) # make new output folder
b = x[1:] * [w, h, w, h] # box
b[2:] = b[2:].max() # rectangle to square
b[2:] = b[2:] * 1.3 + 30 # pad
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
assert cv2.imwrite(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
else:
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
pbar.desc = 'Caching labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
s, nf, nm, ne, nd, n)
assert nf > 0 or n == 20288, 'No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url)
if not labels_loaded and n > 1000:
print('Saving labels to %s for faster future loading' % np_labels_path)
np.save(np_labels_path, self.labels) # save for next time
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
if cache_images: # if training
gb = 0 # Gigabytes of cached images
pbar = tqdm(range(len(self.img_files)), desc='Caching images')
self.img_hw0, self.img_hw = [None] * n, [None] * n
for i in pbar: # max 10k images
self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(self, i) # img, hw_original, hw_resized
gb += self.imgs[i].nbytes
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
# Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
detect_corrupted_images = False
if detect_corrupted_images:
from skimage import io # conda install -c conda-forge scikit-image
for file in tqdm(self.img_files, desc='Detecting corrupted images'):
try:
_ = io.imread(file)
except:
print('Corrupted image detected: %s' % file)
def __init__(self,
imgs,
opt,
batch_size,
augment=False,
hyp=None,
flip=True,
cache_images=False,
single_cls=False,
stride=32,
pad=0.0):
self.img_size = opt['img_size'][0]
self.img_files = imgs.reshape(-1).tolist()
n = len(self.img_files)
assert n > 0, 'No images found'
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
nb = bi[-1] + 1 # number of batches
self.n = n # number of images
self.batch = bi # batch index of image
self.augment = augment
self.hyp = hyp
self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
self.mosaic_border = [-self.img_size // 2, -self.img_size // 2]
self.stride = stride
self.flip = flip
# Define labels
self.label_files = [
x.replace('images',
'labels').replace(os.path.splitext(x)[-1], '.txt')
for x in self.img_files
]
# Check cache
cache_path = str(Path(
self.label_files[0]).parent) + '.cache' # cached labels
if os.path.isfile(cache_path):
cache = torch.load(cache_path) # load
if cache['hash'] != self.get_hash(
self.label_files + self.img_files): # dataset changed
cache = self.cache_labels(cache_path) # re-cache
else:
cache = self.cache_labels(cache_path) # cache
# Get labels
labels, shapes = zip(*[cache[x] for x in self.img_files])
self.shapes = np.array(shapes, dtype=np.float64)
self.labels = list(labels)
# Cache labels
extract_bounding_boxes, labels_loaded = False, False
nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
pbar = tqdm(self.label_files)
for i, file in enumerate(pbar):
l = self.labels[i] # label
if l.shape[0]:
assert l.shape[1] == 5, '> 5 label columns: %s' % file
assert (l >= 0).all(), 'negative labels: %s' % file
assert (l[:, 1:] <= 1).all(
), 'non-normalized or out of bounds coordinate labels: %s' % file
if np.unique(l,
axis=0).shape[0] < l.shape[0]: # duplicate rows
nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
if single_cls:
l[:, 0] = 0 # force dataset into single-class mode
self.labels[i] = l
nf += 1 # file found
# Extract object detection boxes for a second stage classifier
if extract_bounding_boxes:
p = Path(self.img_files[i])
img = cv2.imread(str(p))
h, w = img.shape[:2]
for j, x in enumerate(l):
f = '%s%sclassifier%s%g_%g_%s' % (
p.parent.parent, os.sep, os.sep, x[0], j, p.name)
if not os.path.exists(Path(f).parent):
os.makedirs(
Path(f).parent) # make new output folder
b = x[1:] * [w, h, w, h] # box
b[2:] = b[2:].max() # rectangle to square
b[2:] = b[2:] * 1.3 + 30 # pad
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
b[[0, 2]] = np.clip(b[[0, 2]], 0,
w) # clip boxes outside of image
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
assert cv2.imwrite(f, img[
b[1]:b[3],
b[0]:b[2]]), 'Failure extracting classifier boxes'
else:
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
cache_path, nf, nm, ne, nd, n)
if nf == 0:
s = 'WARNING: No labels found in %s' % (os.path.dirname(file) +
os.sep)
print(s)
assert not augment, '%s. Can not train without labels.' % s
# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
self.imgs = [None] * n
if cache_images:
gb = 0 # Gigabytes of cached images
pbar = tqdm(range(len(self.img_files)), desc='Caching images')
self.img_hw0, self.img_hw = [None] * n, [None] * n
for i in pbar: # max 10k images
self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(
self, i) # img, hw_original, hw_resized
gb += self.imgs[i].nbytes
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)