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torch.cuda.amp bug fix (#2750) 

PR https://github.com/ultralytics/yolov5/pull/2725 introduced a very specific bug that only affects multi-GPU trainings. Apparently the cause was using the torch.cuda.amp decorator in the autoShape forward method. I've implemented amp more traditionally in this PR, and the bug is resolved.
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Glenn Jocher 2021-04-09 18:19:49 +02:00 committed by GitHub
parent fca5e2a48f
commit b5de52c4cd
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1 changed files with 13 additions and 11 deletions
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24
models/common.py
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@ -10,6 +10,7 @@ import requests
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
@ -237,7 +238,6 @@ class autoShape(nn.Module):
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'
@ -251,7 +251,8 @@ class autoShape(nn.Module):
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
@ -278,17 +279,18 @@ class autoShape(nn.Module):
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: