From b5de52c4cdfefb3c7acfbff7d7f450a46b4aaada Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Fri, 9 Apr 2021 18:19:49 +0200
Subject: [PATCH] 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.
---
 models/common.py | 24 +++++++++++++-----------
 1 file changed, 13 insertions(+), 11 deletions(-)

diff --git a/models/common.py b/models/common.py
index c77ecbeceace..1130471e904b 100644
--- a/models/common.py
+++ b/models/common.py
@@ -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
@@ -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'
@@ -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
@@ -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: