Update setup.cfg (#11814)
* Update setup.cfg Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update .pre-commit-config.yaml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update export.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update downloads.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update yolo.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>pull/11818/head^2
Glenn Jocher
GitHub
parent
0b3f751a6f
commit
459dd49c02
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@ -16,7 +16,7 @@ repos:
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- id: check-case-conflict
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- id: check-yaml
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# - id: check-yaml
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- id: check-docstring-first
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- id: double-quote-string-fixer
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- id: detect-private-key
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@ -26,7 +26,6 @@ repos:
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hooks:
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- id: pyupgrade
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name: Upgrade code
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args: [--py37-plus]
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- repo: https://github.com/PyCQA/isort
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rev: 5.12.0
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@ -63,7 +62,12 @@ repos:
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args:
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- --ignore-words-list=crate,nd,strack,dota
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#- repo: https://github.com/asottile/yesqa
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# rev: v1.4.0
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# hooks:
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# - id: yesqa
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# - repo: https://github.com/asottile/yesqa
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# rev: v1.4.0
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# hooks:
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# - id: yesqa
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# - repo: https://github.com/asottile/dead
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# rev: v1.5.0
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# hooks:
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# - id: dead
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@ -110,7 +110,7 @@ def export_formats():
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['TensorFlow Lite', 'tflite', '.tflite', True, False],
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['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
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['TensorFlow.js', 'tfjs', '_web_model', False, False],
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['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
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['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
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return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
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@ -221,7 +221,7 @@ def export_openvino(file, metadata, half, int8, data, prefix=colorstr('OpenVINO:
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import numpy as np
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from openvino.runtime import Core
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from utils.dataloaders import create_dataloader, letterbox
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from utils.dataloaders import create_dataloader
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core = Core()
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onnx_model = core.read_model(f_onnx) # export
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@ -501,7 +501,7 @@ def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
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'10',
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'--out_dir',
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str(file.parent),
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f_tfl,], check=True)
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f_tfl, ], check=True)
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return f, None
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@ -522,7 +522,7 @@ def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
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'--quantize_uint8' if int8 else '',
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'--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
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str(f_pb),
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str(f),]
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str(f), ]
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subprocess.run([arg for arg in args if arg], check=True)
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json = Path(f_json).read_text()
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@ -310,7 +310,7 @@ class TFDetect(keras.layers.Layer):
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y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1)
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z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
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return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
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return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), )
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@staticmethod
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def _make_grid(nx=20, ny=20):
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@ -486,7 +486,7 @@ class TFModel:
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iou_thres,
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conf_thres,
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clip_boxes=False)
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return (nms,)
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return (nms, )
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return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]
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# x = x[0] # [x(1,6300,85), ...] to x(6300,85)
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# xywh = x[..., :4] # x(6300,4) boxes
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@ -21,8 +21,8 @@ if str(ROOT) not in sys.path:
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if platform.system() != 'Windows':
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import *
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from models.experimental import *
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from models.common import * # noqa
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from models.experimental import * # noqa
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from utils.autoanchor import check_anchor_order
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from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
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from utils.plots import feature_visualization
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@ -76,7 +76,7 @@ class Detect(nn.Module):
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y = torch.cat((xy, wh, conf), 4)
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z.append(y.view(bs, self.na * nx * ny, self.no))
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return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
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return x if self.training else (torch.cat(z, 1), ) if self.export else (torch.cat(z, 1), x)
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def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
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d = self.anchors[i].device
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@ -126,7 +126,7 @@ class BaseModel(nn.Module):
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def _profile_one_layer(self, m, x, dt):
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c = m == self.model[-1] # is final layer, copy input as inplace fix
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o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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o = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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t = time_sync()
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for _ in range(10):
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m(x.copy() if c else x)
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@ -605,7 +605,7 @@ def main(opt, callbacks=Callbacks()):
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'gsutil',
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'cp',
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f'gs://{opt.bucket}/evolve.csv',
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str(evolve_csv),])
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str(evolve_csv), ])
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for _ in range(opt.evolve): # generations to evolve
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if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
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14
setup.cfg
14
setup.cfg
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@ -3,7 +3,7 @@
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# Local usage: pip install pre-commit, pre-commit run --all-files
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[metadata]
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license_file = LICENSE
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license_files = LICENSE
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description_file = README.md
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[tool:pytest]
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@ -25,17 +25,19 @@ verbose = 2
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# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
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format = pylint
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# see: https://www.flake8rules.com/
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ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403
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ignore = E731,F405,E402,W504,E501
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# E731: Do not assign a lambda expression, use a def
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# F405: name may be undefined, or defined from star imports: module
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# E402: module level import not at top of file
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# F401: module imported but unused
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# W504: line break after binary operator
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# E127: continuation line over-indented for visual indent
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# E231: missing whitespace after ‘,’, ‘;’, or ‘:’
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# E501: line too long
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# removed:
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# F401: module imported but unused
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# E231: missing whitespace after ‘,’, ‘;’, or ‘:’
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# E127: continuation line over-indented for visual indent
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# F403: ‘from module import *’ used; unable to detect undefined names
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[isort]
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# https://pycqa.github.io/isort/docs/configuration/options.html
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line_length = 120
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@ -48,7 +50,7 @@ spaces_before_comment = 2
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COLUMN_LIMIT = 120
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COALESCE_BRACKETS = True
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SPACES_AROUND_POWER_OPERATOR = True
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SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
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SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = True
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SPLIT_BEFORE_CLOSING_BRACKET = False
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SPLIT_BEFORE_FIRST_ARGUMENT = False
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# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
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2
train.py
2
train.py
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@ -584,7 +584,7 @@ def main(opt, callbacks=Callbacks()):
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'gsutil',
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'cp',
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f'gs://{opt.bucket}/evolve.csv',
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str(evolve_csv),])
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str(evolve_csv), ])
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for _ in range(opt.evolve): # generations to evolve
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if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
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@ -330,7 +330,7 @@ def classify_albumentations(
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if vflip > 0:
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T += [A.VerticalFlip(p=vflip)]
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if jitter > 0:
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color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
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color_jitter = (float(jitter), ) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
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T += [A.ColorJitter(*color_jitter, 0)]
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else: # Use fixed crop for eval set (reproducibility)
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T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
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@ -32,7 +32,7 @@ class Callbacks:
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'on_model_save': [],
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'on_train_end': [],
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'on_params_update': [],
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'teardown': [],}
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'teardown': [], }
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self.stop_training = False # set True to interrupt training
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def register_action(self, hook, name='', callback=None):
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@ -4,7 +4,6 @@ Download utils
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"""
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import logging
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import os
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import subprocess
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import urllib
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from pathlib import Path
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@ -53,7 +52,7 @@ def curl_download(url, filename, *, silent: bool = False) -> bool:
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'--retry',
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'9',
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'-C',
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'-',])
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'-', ])
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return proc.returncode == 0
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@ -139,12 +139,12 @@ def set_logging(name=LOGGING_NAME, verbose=True):
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name: {
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'class': 'logging.StreamHandler',
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'formatter': name,
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'level': level,}},
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'level': level, }},
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'loggers': {
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name: {
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'level': level,
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'handlers': [name],
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'propagate': False,}}})
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'propagate': False, }}})
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set_logging(LOGGING_NAME) # run before defining LOGGER
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@ -416,7 +416,7 @@ def check_imshow(warn=False):
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return False
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def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''):
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def check_suffix(file='yolov5s.pt', suffix=('.pt', ), msg=''):
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# Check file(s) for acceptable suffix
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if file and suffix:
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if isinstance(suffix, str):
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@ -90,7 +90,7 @@ class CometLogger:
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'log_code': False,
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'log_env_gpu': True,
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'log_env_cpu': True,
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'project_name': COMET_PROJECT_NAME,}
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'project_name': COMET_PROJECT_NAME, }
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self.default_experiment_kwargs.update(experiment_kwargs)
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self.experiment = self._get_experiment(self.comet_mode, run_id)
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self.experiment.set_name(self.opt.name)
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@ -153,7 +153,7 @@ class CometLogger:
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'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
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'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
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'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
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'comet_model_name': COMET_MODEL_NAME,})
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'comet_model_name': COMET_MODEL_NAME, })
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# Check if running the Experiment with the Comet Optimizer
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if hasattr(self.opt, 'comet_optimizer_id'):
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@ -170,7 +170,7 @@ class CometLogger:
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**self.default_experiment_kwargs,
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)
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return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
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return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, )
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else:
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try:
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@ -214,7 +214,7 @@ class CometLogger:
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'fitness_score': fitness_score[-1],
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'epochs_trained': epoch + 1,
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'save_period': opt.save_period,
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'total_epochs': opt.epochs,}
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'total_epochs': opt.epochs, }
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model_files = glob.glob(f'{path}/*.pt')
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for model_path in model_files:
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@ -270,7 +270,7 @@ class CometLogger:
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'x': xyxy[0],
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'y': xyxy[1],
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'x2': xyxy[2],
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'y2': xyxy[3]},})
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'y2': xyxy[3]}, })
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for *xyxy, conf, cls in filtered_detections.tolist():
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metadata.append({
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'label': f'{self.class_names[int(cls)]}',
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@ -279,7 +279,7 @@ class CometLogger:
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'x': xyxy[0],
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'y': xyxy[1],
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'x2': xyxy[2],
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'y2': xyxy[3]},})
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'y2': xyxy[3]}, })
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self.metadata_dict[image_name] = metadata
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self.logged_images_count += 1
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@ -196,7 +196,7 @@ KEYS = [
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'val/cls_loss',
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'x/lr0',
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'x/lr1',
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'x/lr2',]
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'x/lr2', ]
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BEST_KEYS = [
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'best/epoch',
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@ -207,4 +207,4 @@ BEST_KEYS = [
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'best/precision(M)',
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'best/recall(M)',
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'best/mAP_0.5(M)',
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'best/mAP_0.5:0.95(M)',]
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'best/mAP_0.5:0.95(M)', ]
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@ -170,7 +170,7 @@ def profile(input, ops, n=10, device=None):
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m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
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tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
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try:
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flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
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flops = thop.profile(m, inputs=(x, ), verbose=False)[0] / 1E9 * 2 # GFLOPs
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except Exception:
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flops = 0
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@ -284,7 +284,7 @@ def model_info(model, verbose=False, imgsz=640):
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p = next(model.parameters())
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stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
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im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
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flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
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flops = thop.profile(deepcopy(model), inputs=(im, ), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
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imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
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fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs
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except Exception:
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