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precommit: Black #5490

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3d0dab7
precommit: Black
Borda Nov 3, 2021
a574d53
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Nov 9, 2021
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11 changes: 5 additions & 6 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -36,12 +36,11 @@ repos:
- id: isort
name: Sort imports

# TODO
#- repo: https://github.com/pre-commit/mirrors-yapf
# rev: v0.31.0
# hooks:
# - id: yapf
# name: formatting
- repo: https://github.com/psf/black
rev: 21.7b0
hooks:
- id: black
name: Black code

# TODO
#- repo: https://github.com/executablebooks/mdformat
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214 changes: 118 additions & 96 deletions detect.py
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282 changes: 164 additions & 118 deletions export.py
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62 changes: 32 additions & 30 deletions hubconf.py
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Expand Up @@ -34,93 +34,93 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
from utils.torch_utils import select_device

file = Path(__file__).resolve()
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
check_requirements(exclude=("tensorboard", "thop", "opencv-python"))
set_logging(verbose=verbose)

save_dir = Path('') if str(name).endswith('.pt') else file.parent
path = (save_dir / name).with_suffix('.pt') # checkpoint path
save_dir = Path("") if str(name).endswith(".pt") else file.parent
path = (save_dir / name).with_suffix(".pt") # checkpoint path
try:
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
device = select_device(("0" if torch.cuda.is_available() else "cpu") if device is None else device)

if pretrained and channels == 3 and classes == 80:
model = attempt_load(path, map_location=device) # download/load FP32 model
else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
cfg = list((Path(__file__).parent / "models").rglob(f"{name}.yaml"))[0] # model.yaml path
model = Model(cfg, channels, classes) # create model
if pretrained:
ckpt = torch.load(attempt_download(path), map_location=device) # load
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect
model.load_state_dict(csd, strict=False) # load
if len(ckpt['model'].names) == classes:
model.names = ckpt['model'].names # set class names attribute
if len(ckpt["model"].names) == classes:
model.names = ckpt["model"].names # set class names attribute
if autoshape:
model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
return model.to(device)

except Exception as e:
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
help_url = "https://github.com/ultralytics/yolov5/issues/36"
s = "Cache may be out of date, try `force_reload=True`. See %s for help." % help_url
raise Exception(s) from e


def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
def custom(path="path/to/model.pt", autoshape=True, verbose=True, device=None):
# YOLOv5 custom or local model
return _create(path, autoshape=autoshape, verbose=verbose, device=device)


def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-nano model https://github.com/ultralytics/yolov5
return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5n", pretrained, channels, classes, autoshape, verbose, device)


def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-small model https://github.com/ultralytics/yolov5
return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5s", pretrained, channels, classes, autoshape, verbose, device)


def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-medium model https://github.com/ultralytics/yolov5
return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5m", pretrained, channels, classes, autoshape, verbose, device)


def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-large model https://github.com/ultralytics/yolov5
return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5l", pretrained, channels, classes, autoshape, verbose, device)


def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5x", pretrained, channels, classes, autoshape, verbose, device)


def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5n6", pretrained, channels, classes, autoshape, verbose, device)


def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5s6", pretrained, channels, classes, autoshape, verbose, device)


def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5m6", pretrained, channels, classes, autoshape, verbose, device)


def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5l6", pretrained, channels, classes, autoshape, verbose, device)


def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
return _create("yolov5x6", pretrained, channels, classes, autoshape, verbose, device)


if __name__ == '__main__':
model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
if __name__ == "__main__":
model = _create(name="yolov5s", pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
# model = custom(path='path/to/model.pt') # custom

# Verify inference
Expand All @@ -130,12 +130,14 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tr
import numpy as np
from PIL import Image

imgs = ['data/images/zidane.jpg', # filename
Path('data/images/zidane.jpg'), # Path
'https://ultralytics.com/images/zidane.jpg', # URI
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
Image.open('data/images/bus.jpg'), # PIL
np.zeros((320, 640, 3))] # numpy
imgs = [
"data/images/zidane.jpg", # filename
Path("data/images/zidane.jpg"), # Path
"https://ultralytics.com/images/zidane.jpg", # URI
cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
Image.open("data/images/bus.jpg"), # PIL
np.zeros((320, 640, 3)),
] # numpy

results = model(imgs) # batched inference
results.print()
Expand Down
89 changes: 53 additions & 36 deletions models/common.py
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Expand Up @@ -169,7 +169,7 @@ def __init__(self, c1, c2, k=(5, 9, 13)):
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


Expand All @@ -185,7 +185,7 @@ def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
Expand Down Expand Up @@ -221,11 +221,14 @@ class GhostBottleneck(nn.Module):
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
super().__init__()
c_ = c2 // 2
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
self.conv = nn.Sequential(
GhostConv(c1, c_, 1, 1), # pw
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
GhostConv(c_, c2, 1, 1, act=False),
) # pw-linear
self.shortcut = (
nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
)

def forward(self, x):
return self.conv(x) + self.shortcut(x)
Expand Down Expand Up @@ -282,7 +285,7 @@ def __init__(self, model):
self.model = model.eval()

def autoshape(self):
LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
LOGGER.info("AutoShape already enabled, skipping... ") # model already converted to model.autoshape()
return self

def _apply(self, fn):
Expand All @@ -309,26 +312,26 @@ def forward(self, imgs, size=640, augment=False, profile=False):
t = [time_sync()]
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
with amp.autocast(enabled=p.device.type != 'cpu'):
with amp.autocast(enabled=p.device.type != "cpu"):
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference

# Pre-process
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
for i, im in enumerate(imgs):
f = f'image{i}' # filename
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, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im
im = np.asarray(exif_transpose(im))
elif isinstance(im, Image.Image): # PIL Image
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
files.append(Path(f).with_suffix('.jpg').name)
im, f = np.asarray(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 np.tile(im[..., None], 3) # enforce 3ch input
s = im.shape[:2] # HWC
shape0.append(s) # image shape
g = (size / max(s)) # gain
g = size / max(s) # gain
shape1.append([y * g for y in s])
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
Expand All @@ -338,14 +341,20 @@ def forward(self, imgs, size=640, augment=False, profile=False):
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
t.append(time_sync())

with amp.autocast(enabled=p.device.type != 'cpu'):
with amp.autocast(enabled=p.device.type != "cpu"):
# Inference
y = self.model(x, augment, profile)[0] # forward
t.append(time_sync())

# Post-process
y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes,
multi_label=self.multi_label, max_det=self.max_det) # NMS
y = non_max_suppression(
y,
self.conf,
iou_thres=self.iou,
classes=self.classes,
multi_label=self.multi_label,
max_det=self.max_det,
) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])

Expand All @@ -371,31 +380,38 @@ def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
self.s = shape # inference BCHW shape

def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path("")):
crops = []
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
s = f"image {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
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}'
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)})
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, color=colors(cls))
im = annotator.im
else:
s += '(no detections)'
s += "(no detections)"

im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
if pprint:
LOGGER.info(s.rstrip(', '))
LOGGER.info(s.rstrip(", "))
if show:
im.show(self.files[i]) # show
if save:
Expand All @@ -407,23 +423,24 @@ def display(self, pprint=False, show=False, save=False, crop=False, render=False
self.imgs[i] = np.asarray(im)
if crop:
if save:
LOGGER.info(f'Saved results to {save_dir}\n')
LOGGER.info(f"Saved results to {save_dir}\n")
return crops

def print(self):
self.display(pprint=True) # print results
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
self.t)
LOGGER.info(
f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}" % self.t
)

def show(self):
self.display(show=True) # show results

def save(self, save_dir='runs/detect/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
def save(self, save_dir="runs/detect/exp"):
save_dir = increment_path(save_dir, exist_ok=save_dir != "runs/detect/exp", mkdir=True) # increment save_dir
self.display(save=True, save_dir=save_dir) # save results

def crop(self, save=True, save_dir='runs/detect/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
def crop(self, save=True, save_dir="runs/detect/exp"):
save_dir = increment_path(save_dir, exist_ok=save_dir != "runs/detect/exp", mkdir=True) if save else None
return self.display(crop=True, save=save, save_dir=save_dir) # crop results

def render(self):
Expand All @@ -433,9 +450,9 @@ def render(self):
def pandas(self):
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
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]):
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, [pd.DataFrame(x, columns=c) for x in a])
return new
Expand All @@ -444,7 +461,7 @@ def tolist(self):
# return a list of Detections objects, i.e. 'for result in results.tolist():'
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
for d in x:
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]:
setattr(d, k, getattr(d, k)[0]) # pop out of list
return x

Expand Down
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