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PyTorch 1.11.0 compatibility updates (#1914)

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* PyTorch 1.11.0 compatibility updates

Resolves `AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'` first raised in https://github.com/ultralytics/yolov5/issues/5499 and observed in all CI runs on just-released PyTorch 1.11.0.

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* Update experimental.py

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Glenn Jocher 2022-03-10 12:59:47 +01:00 committed by GitHub
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1 changed files with 10 additions and 9 deletions
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19
models/experimental.py
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@ -94,21 +94,22 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
model = Ensemble() model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]: for w in weights if isinstance(weights, list) else [weights]:
ckpt = torch.load(attempt_download(w), map_location=map_location) # load ckpt = torch.load(attempt_download(w), map_location=map_location) # load
if fuse: ckpt = (ckpt['ema'] or ckpt['model']).float() # FP32 model
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) # fused or un-fused model in eval mode
else:
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse
# Compatibility updates # Compatibility updates
for m in model.modules(): for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: t = type(m)
m.inplace = inplace # pytorch 1.7.0 compatibility if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
if type(m) is Detect: m.inplace = inplace # torch 1.7.0 compatibility
if t is Detect:
if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility
delattr(m, 'anchor_grid') delattr(m, 'anchor_grid')
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
elif type(m) is Conv: elif t is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility m._non_persistent_buffers_set = set() # torch 1.6.0 compatibility
elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
m.recompute_scale_factor = None # torch 1.11.0 compatibility
if len(model) == 1: if len(model) == 1:
return model[-1] # return model return model[-1] # return model