Skip to content

Default PyTorch Hub to autocast(False) #5926

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
Dec 8, 2021
Merged

Default PyTorch Hub to autocast(False) #5926

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
6 changes: 4 additions & 2 deletions models/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -443,6 +443,7 @@ class AutoShape(nn.Module):
multi_label = False # NMS multiple labels per box
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
max_det = 1000 # maximum number of detections per image
amp = False # Automatic Mixed Precision (AMP) inference

def __init__(self, model):
super().__init__()
Expand Down Expand Up @@ -476,8 +477,9 @@ def forward(self, imgs, size=640, augment=False, profile=False):

t = [time_sync()]
p = next(self.model.parameters()) if self.pt else torch.zeros(1) # for device and type
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
if isinstance(imgs, torch.Tensor): # torch
with amp.autocast(enabled=p.device.type != 'cpu'):
with amp.autocast(enabled=autocast):
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference

# Pre-process
Expand Down Expand Up @@ -506,7 +508,7 @@ def forward(self, imgs, size=640, augment=False, profile=False):
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
t.append(time_sync())

with amp.autocast(enabled=p.device.type != 'cpu'):
with amp.autocast(enabled=autocast):
# Inference
y = self.model(x, augment, profile) # forward
t.append(time_sync())
Expand Down