Shorten module paths with new 'nn' dir (#96)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>single_channel
Glenn Jocher
2 years ago
committed by
GitHub
parent
4fb04be20b
commit
48cffa176e
@ -1 +1,5 @@
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__version__ = "8.0.0.dev0"
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from ultralytics.yolo.engine.model import YOLO
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__all__ = ["__version__", "YOLO"] # allow simpler import
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@ -1,7 +0,0 @@
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from ultralytics.yolo import v8
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from .engine.model import YOLO
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from .engine.trainer import BaseTrainer
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from .engine.validator import BaseValidator
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__all__ = ["BaseTrainer", "BaseValidator", "YOLO"] # allow simpler import
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@ -1,109 +0,0 @@
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import contextlib
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import torchvision
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from ultralytics.yolo.utils.downloads import attempt_download
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from ultralytics.yolo.utils.modeling.modules import *
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def attempt_load_weights(weights, device=None, inplace=True, fuse=True):
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# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
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model = Ensemble()
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for w in weights if isinstance(weights, list) else [weights]:
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ckpt = torch.load(attempt_download(w), map_location='cpu') # load
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ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
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# Model compatibility updates
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if not hasattr(ckpt, 'stride'):
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ckpt.stride = torch.tensor([32.])
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if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
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ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
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model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
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# Module compatibility updates
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for m in model.modules():
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t = type(m)
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if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Segment):
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m.inplace = inplace # torch 1.7.0 compatibility
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elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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# Return model
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if len(model) == 1:
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return model[-1]
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# Return detection ensemble
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print(f'Ensemble created with {weights}\n')
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for k in 'names', 'nc', 'yaml':
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setattr(model, k, getattr(model[0], k))
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model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
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assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
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return model
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def parse_model(d, ch): # model_dict, input_channels(3)
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# Parse a YOLOv5 model.yaml dictionary
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LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<50}{'arguments':<30}")
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nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
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if act:
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Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
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LOGGER.info(f"{colorstr('activation:')} {act}") # print
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no = nc + 4 # number of outputs = classes + box
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layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
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for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
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m = eval(m) if isinstance(m, str) else m # eval strings
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for j, a in enumerate(args):
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with contextlib.suppress(NameError):
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
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if m in {
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Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, BottleneckCSP,
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C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
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c1, c2 = ch[f], args[0]
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if c2 != no: # if not output
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c2 = make_divisible(c2 * gw, 8)
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args = [c1, c2, *args[1:]]
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if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
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args.insert(2, n) # number of repeats
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n = 1
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elif m is nn.BatchNorm2d:
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args = [ch[f]]
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elif m is Concat:
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c2 = sum(ch[x] for x in f)
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# TODO: channel, gw, gd
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elif m in {Detect, Segment}:
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args.append([ch[x] for x in f])
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if m is Segment:
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args[3] = make_divisible(args[3] * gw, 8)
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else:
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c2 = ch[f]
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m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
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t = str(m)[8:-2].replace('__main__.', '') # module type
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m.np = sum(x.numel() for x in m_.parameters()) # number params
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m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
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LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{m.np:10.0f} {t:<50}{str(args):<30}') # print
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save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
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layers.append(m_)
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if i == 0:
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ch = []
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ch.append(c2)
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return nn.Sequential(*layers), sorted(save)
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def get_model(model='s.pt', pretrained=True):
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# Load a YOLO model locally, from torchvision, or from Ultralytics assets
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if model.endswith(".pt"):
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model = model.split(".")[0]
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if Path(f"{model}.pt").is_file(): # local file
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return attempt_load_weights(f"{model}.pt", device='cpu')
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elif model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
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return torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
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else: # Ultralytics assets
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return attempt_load_weights(f"{model}.pt", device='cpu')
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