Skip to content

PyTorch 1.11.0 compatibility updates #6932

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
glenn-jocher merged 1 commit into from
Mar 10, 2022
Merged

PyTorch 1.11.0 compatibility updates #6932

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 10 additions & 9 deletions models/experimental.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -94,21 +94,22 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
ckpt = torch.load(attempt_download(w), map_location=map_location) # load
if fuse:
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
else:
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse
ckpt = (ckpt['ema'] or ckpt['model']).float() # FP32 model
model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) # fused or un-fused model in eval mode

# Compatibility updates
for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
m.inplace = inplace # pytorch 1.7.0 compatibility
if type(m) is Detect:
t = type(m)
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
m.inplace = inplace # torch 1.7.0 compatibility
if t is Detect:
if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility
delattr(m, 'anchor_grid')
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
elif type(m) is Conv:
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
elif t is nn.Upsample:
m.recompute_scale_factor = None # torch 1.11.0 compatibility
elif t is Conv:
m._non_persistent_buffers_set = set() # torch 1.6.0 compatibility

if len(model) == 1:
return model[-1] # return model
Expand Down