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precommit: yapf (ultralytics#5494)
* precommit: yapf * align isort * fix # Conflicts: # utils/plots.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update wandb_utils.py * Update augmentations.py * Update setup.cfg * Update yolo.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update val.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * simplify colorstr * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * val run fix * export.py last comma * Update export.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update hubconf.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * PyTorch Hub tuple fix * PyTorch Hub tuple fix2 * PyTorch Hub tuple fix3 * Update setup Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <[email protected]>
1 parent 2a0ad55 commit 6f6285e

24 files changed

Lines changed: 527 additions & 331 deletions

‎.pre-commit-config.yaml‎

Lines changed: 5 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -36,12 +36,11 @@ repos:
3636
- id: isort
3737
name: Sort imports
3838

39-
# TODO
40-
#- repo: https://github.com/pre-commit/mirrors-yapf
41-
# rev: v0.31.0
42-
# hooks:
43-
# - id: yapf
44-
# name: formatting
39+
- repo: https://github.com/pre-commit/mirrors-yapf
40+
rev: v0.31.0
41+
hooks:
42+
- id: yapf
43+
name: formatting
4544

4645
# TODO
4746
#- repo: https://github.com/executablebooks/mdformat

‎detect.py‎

Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -47,7 +47,8 @@
4747

4848

4949
@torch.no_grad()
50-
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
50+
def run(
51+
weights=ROOT / 'yolov5s.pt', # model.pt path(s)
5152
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
5253
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
5354
imgsz=(640, 640), # inference size (height, width)
@@ -73,7 +74,7 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
7374
hide_conf=False, # hide confidences
7475
half=False, # use FP16 half-precision inference
7576
dnn=False, # use OpenCV DNN for ONNX inference
76-
):
77+
):
7778
source = str(source)
7879
save_img = not nosave and not source.endswith('.txt') # save inference images
7980
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)

‎export.py‎

Lines changed: 65 additions & 45 deletions
Original file line numberDiff line numberDiff line change
@@ -76,16 +76,11 @@
7676

7777
def export_formats():
7878
# YOLOv5 export formats
79-
x = [['PyTorch', '-', '.pt', True],
80-
['TorchScript', 'torchscript', '.torchscript', True],
81-
['ONNX', 'onnx', '.onnx', True],
82-
['OpenVINO', 'openvino', '_openvino_model', False],
83-
['TensorRT', 'engine', '.engine', True],
84-
['CoreML', 'coreml', '.mlmodel', False],
85-
['TensorFlow SavedModel', 'saved_model', '_saved_model', True],
86-
['TensorFlow GraphDef', 'pb', '.pb', True],
87-
['TensorFlow Lite', 'tflite', '.tflite', False],
88-
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
79+
x = [['PyTorch', '-', '.pt', True], ['TorchScript', 'torchscript', '.torchscript', True],
80+
['ONNX', 'onnx', '.onnx', True], ['OpenVINO', 'openvino', '_openvino_model', False],
81+
['TensorRT', 'engine', '.engine', True], ['CoreML', 'coreml', '.mlmodel', False],
82+
['TensorFlow SavedModel', 'saved_model', '_saved_model', True], ['TensorFlow GraphDef', 'pb', '.pb', True],
83+
['TensorFlow Lite', 'tflite', '.tflite', False], ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
8984
['TensorFlow.js', 'tfjs', '_web_model', False]]
9085
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU'])
9186

@@ -119,14 +114,25 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
119114
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
120115
f = file.with_suffix('.onnx')
121116

122-
torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
123-
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
124-
do_constant_folding=not train,
125-
input_names=['images'],
126-
output_names=['output'],
127-
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
128-
'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
129-
} if dynamic else None)
117+
torch.onnx.export(
118+
model,
119+
im,
120+
f,
121+
verbose=False,
122+
opset_version=opset,
123+
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
124+
do_constant_folding=not train,
125+
input_names=['images'],
126+
output_names=['output'],
127+
dynamic_axes={
128+
'images': {
129+
0: 'batch',
130+
2: 'height',
131+
3: 'width'}, # shape(1,3,640,640)
132+
'output': {
133+
0: 'batch',
134+
1: 'anchors'} # shape(1,25200,85)
135+
} if dynamic else None)
130136

131137
# Checks
132138
model_onnx = onnx.load(f) # load onnx model
@@ -140,10 +146,9 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
140146
import onnxsim
141147

142148
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
143-
model_onnx, check = onnxsim.simplify(
144-
model_onnx,
145-
dynamic_input_shape=dynamic,
146-
input_shapes={'images': list(im.shape)} if dynamic else None)
149+
model_onnx, check = onnxsim.simplify(model_onnx,
150+
dynamic_input_shape=dynamic,
151+
input_shapes={'images': list(im.shape)} if dynamic else None)
147152
assert check, 'assert check failed'
148153
onnx.save(model_onnx, f)
149154
except Exception as e:
@@ -246,9 +251,18 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
246251
LOGGER.info(f'\n{prefix} export failure: {e}')
247252

248253

249-
def export_saved_model(model, im, file, dynamic,
250-
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
251-
conf_thres=0.25, keras=False, prefix=colorstr('TensorFlow SavedModel:')):
254+
def export_saved_model(model,
255+
im,
256+
file,
257+
dynamic,
258+
tf_nms=False,
259+
agnostic_nms=False,
260+
topk_per_class=100,
261+
topk_all=100,
262+
iou_thres=0.45,
263+
conf_thres=0.25,
264+
keras=False,
265+
prefix=colorstr('TensorFlow SavedModel:')):
252266
# YOLOv5 TensorFlow SavedModel export
253267
try:
254268
import tensorflow as tf
@@ -278,11 +292,10 @@ def export_saved_model(model, im, file, dynamic,
278292
tfm = tf.Module()
279293
tfm.__call__ = tf.function(lambda x: frozen_func(x)[0], [spec])
280294
tfm.__call__(im)
281-
tf.saved_model.save(
282-
tfm,
283-
f,
284-
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if
285-
check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
295+
tf.saved_model.save(tfm,
296+
f,
297+
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
298+
if check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
286299
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
287300
return keras_model, f
288301
except Exception as e:
@@ -352,10 +365,10 @@ def export_edgetpu(keras_model, im, file, prefix=colorstr('Edge TPU:')):
352365
if subprocess.run(cmd + ' >/dev/null', shell=True).returncode != 0:
353366
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
354367
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
355-
for c in ['curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
356-
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
357-
'sudo apt-get update',
358-
'sudo apt-get install edgetpu-compiler']:
368+
for c in (
369+
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
370+
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
371+
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
359372
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
360373
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
361374

@@ -395,12 +408,10 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
395408
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
396409
r'"Identity.?.?": {"name": "Identity.?.?"}, '
397410
r'"Identity.?.?": {"name": "Identity.?.?"}, '
398-
r'"Identity.?.?": {"name": "Identity.?.?"}}}',
399-
r'{"outputs": {"Identity": {"name": "Identity"}, '
411+
r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
400412
r'"Identity_1": {"name": "Identity_1"}, '
401413
r'"Identity_2": {"name": "Identity_2"}, '
402-
r'"Identity_3": {"name": "Identity_3"}}}',
403-
json)
414+
r'"Identity_3": {"name": "Identity_3"}}}', json)
404415
j.write(subst)
405416

406417
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
@@ -410,7 +421,8 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
410421

411422

412423
@torch.no_grad()
413-
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
424+
def run(
425+
data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
414426
weights=ROOT / 'yolov5s.pt', # weights path
415427
imgsz=(640, 640), # image (height, width)
416428
batch_size=1, # batch size
@@ -431,8 +443,8 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
431443
topk_per_class=100, # TF.js NMS: topk per class to keep
432444
topk_all=100, # TF.js NMS: topk for all classes to keep
433445
iou_thres=0.45, # TF.js NMS: IoU threshold
434-
conf_thres=0.25 # TF.js NMS: confidence threshold
435-
):
446+
conf_thres=0.25, # TF.js NMS: confidence threshold
447+
):
436448
t = time.time()
437449
include = [x.lower() for x in include] # to lowercase
438450
formats = tuple(export_formats()['Argument'][1:]) # --include arguments
@@ -495,9 +507,16 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
495507
if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
496508
check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
497509
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
498-
model, f[5] = export_saved_model(model.cpu(), im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
499-
agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class,
500-
topk_all=topk_all, conf_thres=conf_thres, iou_thres=iou_thres) # keras model
510+
model, f[5] = export_saved_model(model.cpu(),
511+
im,
512+
file,
513+
dynamic,
514+
tf_nms=nms or agnostic_nms or tfjs,
515+
agnostic_nms=agnostic_nms or tfjs,
516+
topk_per_class=topk_per_class,
517+
topk_all=topk_all,
518+
conf_thres=conf_thres,
519+
iou_thres=iou_thres) # keras model
501520
if pb or tfjs: # pb prerequisite to tfjs
502521
f[6] = export_pb(model, im, file)
503522
if tflite or edgetpu:
@@ -542,7 +561,8 @@ def parse_opt():
542561
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
543562
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
544563
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
545-
parser.add_argument('--include', nargs='+',
564+
parser.add_argument('--include',
565+
nargs='+',
546566
default=['torchscript', 'onnx'],
547567
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
548568
opt = parser.parse_args()

‎hubconf.py‎

Lines changed: 7 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -132,12 +132,13 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tr
132132

133133
from utils.general import cv2
134134

135-
imgs = ['data/images/zidane.jpg', # filename
136-
Path('data/images/zidane.jpg'), # Path
137-
'https://ultralytics.com/images/zidane.jpg', # URI
138-
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
139-
Image.open('data/images/bus.jpg'), # PIL
140-
np.zeros((320, 640, 3))] # numpy
135+
imgs = [
136+
'data/images/zidane.jpg', # filename
137+
Path('data/images/zidane.jpg'), # Path
138+
'https://ultralytics.com/images/zidane.jpg', # URI
139+
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
140+
Image.open('data/images/bus.jpg'), # PIL
141+
np.zeros((320, 640, 3))] # numpy
141142

142143
results = model(imgs, size=320) # batched inference
143144
results.print()

‎models/common.py‎

Lines changed: 24 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -227,11 +227,12 @@ class GhostBottleneck(nn.Module):
227227
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
228228
super().__init__()
229229
c_ = c2 // 2
230-
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
231-
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
232-
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
233-
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
234-
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
230+
self.conv = nn.Sequential(
231+
GhostConv(c1, c_, 1, 1), # pw
232+
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
233+
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
234+
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
235+
act=False)) if s == 2 else nn.Identity()
235236

236237
def forward(self, x):
237238
return self.conv(x) + self.shortcut(x)
@@ -387,9 +388,10 @@ def wrap_frozen_graph(gd, inputs, outputs):
387388
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
388389
if edgetpu: # Edge TPU https://coral.ai/software/#edgetpu-runtime
389390
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
390-
delegate = {'Linux': 'libedgetpu.so.1',
391-
'Darwin': 'libedgetpu.1.dylib',
392-
'Windows': 'edgetpu.dll'}[platform.system()]
391+
delegate = {
392+
'Linux': 'libedgetpu.so.1',
393+
'Darwin': 'libedgetpu.1.dylib',
394+
'Windows': 'edgetpu.dll'}[platform.system()]
393395
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
394396
else: # Lite
395397
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
@@ -531,7 +533,7 @@ def forward(self, imgs, size=640, augment=False, profile=False):
531533
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
532534

533535
# Pre-process
534-
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
536+
n, imgs = (len(imgs), list(imgs)) if isinstance(imgs, (list, tuple)) else (1, [imgs]) # number, list of images
535537
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
536538
for i, im in enumerate(imgs):
537539
f = f'image{i}' # filename
@@ -561,8 +563,13 @@ def forward(self, imgs, size=640, augment=False, profile=False):
561563
t.append(time_sync())
562564

563565
# Post-process
564-
y = non_max_suppression(y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic,
565-
self.multi_label, max_det=self.max_det) # NMS
566+
y = non_max_suppression(y if self.dmb else y[0],
567+
self.conf,
568+
self.iou,
569+
self.classes,
570+
self.agnostic,
571+
self.multi_label,
572+
max_det=self.max_det) # NMS
566573
for i in range(n):
567574
scale_coords(shape1, y[i][:, :4], shape0[i])
568575

@@ -603,8 +610,12 @@ def display(self, pprint=False, show=False, save=False, crop=False, render=False
603610
label = f'{self.names[int(cls)]} {conf:.2f}'
604611
if crop:
605612
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
606-
crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
607-
'im': save_one_box(box, im, file=file, save=save)})
613+
crops.append({
614+
'box': box,
615+
'conf': conf,
616+
'cls': cls,
617+
'label': label,
618+
'im': save_one_box(box, im, file=file, save=save)})
608619
else: # all others
609620
annotator.box_label(box, label if labels else '', color=colors(cls))
610621
im = annotator.im

‎models/experimental.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -63,8 +63,8 @@ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kern
6363
a[0] = 1
6464
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
6565

66-
self.m = nn.ModuleList(
67-
[nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
66+
self.m = nn.ModuleList([
67+
nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
6868
self.bn = nn.BatchNorm2d(c2)
6969
self.act = nn.SiLU()
7070

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