我正在尝试使用多个GPU检测视频中的对象。我想将帧分配给GPU进行推理，以增加总处理时间。我成功在单个GPU上运行推理，但未能在多个GPU上运行。
我认为每gpu数划分帧并进行处理推断会减少时间。如果还有其他方法可以减少运行时间，我很乐意收到建议。
我正在使用Pytorch提供的预训练模型。我尝试的方法如下：
1.阅读视频，然后将帧数除以我拥有的GPU数量（目前有两个NVIDIA GeForce GTX 1080 Ti）
2.然后，我将帧分发到GPU并处理对象检测推断。
（后来我计划使用多线程来动态分配每gpu数量的帧，但目前我将其设置为静态）
我尝试使用with tf.device()在Tensorflow中效果很好的同一方法，并且我也试图在Pytorch中使其成为可能。
...
def detection_gpu(frame_list, device_name, device, detect, model):
model.to(device)
model.eval()
for frame in frame_list:
start = time.time()
detect.bounding_box_rcnn(frame, model=model)
end = time.time()
cv2.putText(frame, '{:.2f}ms'.format((end - start) * 1000), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
(255, 0, 0),
2)
cv2.imshow(str(device_name), frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def main():
args = arg_parse()
VIDEO_PATH = args.video
print("Loading network.....")
model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
print("Network successfully loaded")
num_gpus = torch.cuda.device_count()
if torch.cuda.is_available() and num_gpus > 1:
device = ["cuda:{}".format(i) for i in range(num_gpus)]
elif num_gpus == 1:
device = "cuda"
else:
device = "cpu"
# class names ex) person, car, truck, and etc.
PATH_TO_LABELS = "labels/mscoco_labels.names"
# load detection class, default confidence threshold is 0.5
if num_gpus>1:
detect = [DetectBoxes(PATH_TO_LABELS, device[i], conf_threshold=args.confidence) for i in range(num_gpus)]
else:
detect = [DetectBoxes(PATH_TO_LABELS, device, conf_threshold=args.confidence) for i in range(1)]
cap = cv2.VideoCapture(VIDEO_PATH)
# find number of gpus that is available
frame_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# TODO: CPU환경 고려하기
# divide frames of video by number of gpus
div = frame_length // num_gpus
divide_point = [i for i in range(frame_length) if i != 0 and i % div == 0]
divide_point.pop()
frame_list = []
fragments = []
count = 0
while cap.isOpened():
hasFrame, frame = cap.read()
if not hasFrame:
frame_list.append(fragments)
break
if count in divide_point:
frame_list.append(fragments)
fragments = []
fragments.append(frame)
count += 1
cap.release()
detection_gpu(frame_list[0], 0, device[0], detect[0], model)
detection_gpu(frame_list[1], 1, device[1], detect[1], model)
# Process object detection using threading
# thread_detection = [ThreadWithReturnValue(target=detection_gpu,
# args=(frame_list[i], i, detect, model))
# for i in range(num_gpus)]
#
#
# final_list = []
# # Begin operating threads
# for th in thread_detection:
# th.start()
#
# # Once tasks are completed get return value (frames) and put to new list
# for th in thread_detection:
# final_list.extend(th.join())
cv2.destroyAllWindows()
def bounding_box_rcnn(self, frame, model):
print(self.device)
# Image is converted to image Tensor
transform = transforms.Compose([transforms.ToTensor()])
img = transform(frame).to(self.device)
with torch.no_grad():
# The image is passed through model to get predictions
pred = model([img])
# classes, bounding boxes, confidence scores are gained
# only classes and bounding boxes > confThershold are passed to draw_boxes
pred_class = [self.classes[i] for i in list(pred[0]['labels'].cpu().clone().numpy())]
pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().cpu().clone().numpy())]
pred_score = list(pred[0]['scores'].detach().cpu().clone().numpy())
pred_t = [pred_score.index(x) for x in pred_score if x > self.confThreshold][-1]
pred_colors = [i for i in list(pred[0]['labels'].cpu().clone().numpy())]
pred_boxes = pred_boxes[:pred_t + 1]
pred_class = pred_class[:pred_t + 1]
for i in range(len(pred_boxes)):
left = int(pred_boxes[i][0][0])
top = int(pred_boxes[i][0][1])
right = int(pred_boxes[i][1][0])
bottom = int(pred_boxes[i][1][1])
color = STANDARD_COLORS[pred_colors[i] % len(STANDARD_COLORS)]
self.draw_boxes(frame, pred_class[i], pred_score[i], left, top, right, bottom, color)
我得到的错误如下：
Traceback (most recent call last):
File "C:/Users/username/Desktop/Object_Detection_Video_AllInOne/pytorch_multithread.py", line 133, in <module>
main()
File "C:/Users/username/Desktop/Object_Detection_Video_AllInOne/pytorch_multithread.py", line 113, in main
detection_gpu(frame_list[1], 1, device[1], detect[1], model)
File "C:/Users/username/Desktop/Object_Detection_Video_AllInOne/pytorch_multithread.py", line 39, in detection_gpu
detect.bounding_box_rcnn(frame, model=model)
File "C:\Users\username\Desktop\Object_Detection_Video_AllInOne\p_utils\detection_boxes_pytorch.py", line 64, in bounding_box_rcnn
pred = model([img])
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\models\detection\generalized_rcnn.py", line 51, in forward
proposals, proposal_losses = self.rpn(images, features, targets)
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\models\detection\rpn.py", line 409, in forward
proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\models\detection\_utils.py", line 168, in decode
rel_codes.reshape(sum(boxes_per_image), -1), concat_boxes
File "C:\Users\username\AppData\Local\Programs\Python\Python37\lib\site-packages\torchvision\models\detection\_utils.py", line 199, in decode_single
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
RuntimeError: binary_op(): expected both inputs to be on same device, but input a is on cuda:1 and input b is on cuda:0