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torch.cuda.amp bug fix #2750

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Apr 9, 2021
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torch.cuda.amp bug fix #2750

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24 changes: 13 additions & 11 deletions models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp

from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
Expand Down Expand Up @@ -237,7 +238,6 @@ def autoshape(self):
return self

@torch.no_grad()
@torch.cuda.amp.autocast(torch.cuda.is_available())
def forward(self, imgs, size=640, augment=False, profile=False):
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
# filename: imgs = 'data/samples/zidane.jpg'
Expand All @@ -251,7 +251,8 @@ def forward(self, imgs, size=640, augment=False, profile=False):
t = [time_synchronized()]
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
with amp.autocast(enabled=p.device.type != 'cpu'):
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference

# Pre-process
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
Expand All @@ -278,17 +279,18 @@ 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_synchronized())

# Inference
y = self.model(x, augment, profile)[0] # forward
t.append(time_synchronized())
with amp.autocast(enabled=p.device.type != 'cpu'):
# Inference
y = self.model(x, augment, profile)[0] # forward
t.append(time_synchronized())

# Post-process
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])
# Post-process
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])

t.append(time_synchronized())
return Detections(imgs, y, files, t, self.names, x.shape)
t.append(time_synchronized())
return Detections(imgs, y, files, t, self.names, x.shape)


class Detections:
Expand Down