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ultralytics 8.0.50 AMP check and YOLOv5u YAMLs (#1263)

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Glenn Jocher 2023-03-06 11:39:26 +01:00 • committed by GitHub
parent 3861e6c82a
commit f0d8e4718b
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29 changed files with 440 additions and 83 deletions
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27
ultralytics/nn/autobackend.py
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@ -27,6 +27,10 @@ def check_class_names(names):
if isinstance(names, dict):
if not all(isinstance(k, int) for k in names.keys()): # convert string keys to int, i.e. '0' to 0
names = {int(k): v for k, v in names.items()}
n = len(names)
if max(names.keys()) >= n:
raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '
f'{min(names.keys())}-{max(names.keys())} defined in your dataset YAML.')
if isinstance(names[0], str) and names[0].startswith('n0'): # imagenet class codes, i.e. 'n01440764'
map = yaml_load(ROOT / 'datasets/ImageNet.yaml')['map'] # human-readable names
names = {k: map[v] for k, v in names.items()}
@ -35,12 +39,14 @@ def check_class_names(names):
class AutoBackend(nn.Module):
def _apply_default_class_names(self, data):
with contextlib.suppress(Exception):
return yaml_load(check_yaml(data))['names']
return {i: f'class{i}' for i in range(999)} # return default if above errors
def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
def __init__(self,
weights='yolov8n.pt',
device=torch.device('cpu'),
dnn=False,
data=None,
fp16=False,
fuse=True,
verbose=True):
"""
MultiBackend class for python inference on various platforms using Ultralytics YOLO.
@ -51,6 +57,7 @@ class AutoBackend(nn.Module):
data (str), (Path): Additional data.yaml file for class names, optional
fp16 (bool): If True, use half precision. Default: False
fuse (bool): Whether to fuse the model or not. Default: True
verbose (bool): Whether to run in verbose mode or not. Default: True
Supported formats and their naming conventions:
| Format | Suffix |
@ -83,7 +90,7 @@ class AutoBackend(nn.Module):
# NOTE: special case: in-memory pytorch model
if nn_module:
model = weights.to(device)
model = model.fuse() if fuse else model
model = model.fuse(verbose=verbose) if fuse else model
names = model.module.names if hasattr(model, 'module') else model.names # get class names
stride = max(int(model.stride.max()), 32) # model stride
model.half() if fp16 else model.float()
@ -410,6 +417,12 @@ class AutoBackend(nn.Module):
for _ in range(2 if self.jit else 1): #
self.forward(im) # warmup
@staticmethod
def _apply_default_class_names(data):
with contextlib.suppress(Exception):
return yaml_load(check_yaml(data))['names']
return {i: f'class{i}' for i in range(999)} # return default if above errors
@staticmethod
def _model_type(p='path/to/model.pt'):
"""