我正在尝试在pytorch中实现Yolo-v2。但是，我似乎只是通过网络传递数据而内存不足。该模型很大，如下所示。但是，我觉得我在网络上做一些愚蠢的事情（例如不释放内存在某处）。网络在cpu上正常工作。
测试代码（内存用完）是：
x = torch.rand(32,3,416, 416).cuda()
model = Yolov2().cuda()
y = model(x.float())
问题
模型：
import torch
from torch import nn
import torch.nn.functional as F
class Yolov2(nn.Module):
def __init__(self):
super(Yolov2, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm2 = nn.BatchNorm2d(64)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm3 = nn.BatchNorm2d(128)
self.conv4 = nn.Conv2d(in_channels=128, out_channels=64, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm4 = nn.BatchNorm2d(64)
self.conv5 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm5 = nn.BatchNorm2d(128)
self.conv6 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm6 = nn.BatchNorm2d(256)
self.conv7 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm7 = nn.BatchNorm2d(128)
self.conv8 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm8 = nn.BatchNorm2d(256)
self.conv9 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm9 = nn.BatchNorm2d(512)
self.conv10 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm10 = nn.BatchNorm2d(256)
self.conv11 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm11 = nn.BatchNorm2d(512)
self.conv12 = nn.Conv2d(in_channels=512, out_channels=256, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm12 = nn.BatchNorm2d(256)
self.conv13 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm13 = nn.BatchNorm2d(512)
self.conv14 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm14 = nn.BatchNorm2d(1024)
self.conv15 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm15 = nn.BatchNorm2d(512)
self.conv16 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm16 = nn.BatchNorm2d(1024)
self.conv17 = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1, stride=1, padding=0, bias=False)
self.batchnorm17 = nn.BatchNorm2d(512)
self.conv18 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm18 = nn.BatchNorm2d(1024)
self.conv19 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm19 = nn.BatchNorm2d(1024)
self.conv20 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm20 = nn.BatchNorm2d(1024)
self.conv21 = nn.Conv2d(in_channels=3072, out_channels=1024, kernel_size=3, stride=1, padding=1, bias=False)
self.batchnorm21 = nn.BatchNorm2d(1024)
self.conv22 = nn.Conv2d(in_channels=1024, out_channels=125, kernel_size=1, stride=1, padding=0)
def reorg_layer(self, x):
stride = 2
batch_size, channels, height, width = x.size()
new_ht = int(height/stride)
new_wd = int(width/stride)
new_channels = channels * stride * stride
# from IPython.core.debugger import Tracer; Tracer()()
passthrough = x.permute(0, 2, 3, 1)
passthrough = passthrough.contiguous().view(-1, new_ht, stride, new_wd, stride, channels)
passthrough = passthrough.permute(0, 1, 3, 2, 4, 5)
passthrough = passthrough.contiguous().view(-1, new_ht, new_wd, new_channels)
passthrough = passthrough.permute(0, 3, 1, 2)
return passthrough
def forward(self, x):
out = F.max_pool2d(F.leaky_relu(self.batchnorm1(self.conv1(x)), negative_slope=0.1), 2, stride=2)
out = F.max_pool2d(F.leaky_relu(self.batchnorm2(self.conv2(out)), negative_slope=0.1), 2, stride=2)
out = F.leaky_relu(self.batchnorm3(self.conv3(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm4(self.conv4(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm5(self.conv5(out)), negative_slope=0.1)
out = F.max_pool2d(out, 2, stride=2)
out = F.leaky_relu(self.batchnorm6(self.conv6(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm7(self.conv7(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm8(self.conv8(out)), negative_slope=0.1)
out = F.max_pool2d(out, 2, stride=2)
out = F.leaky_relu(self.batchnorm9(self.conv9(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm10(self.conv10(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm11(self.conv11(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm12(self.conv12(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm13(self.conv13(out)), negative_slope=0.1)
# from IPython.core.debugger import Tracer; Tracer()()
passthrough = self.reorg_layer(out)
out = F.max_pool2d(out, 2, stride=2)
out = F.leaky_relu(self.batchnorm14(self.conv14(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm15(self.conv15(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm16(self.conv16(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm17(self.conv17(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm18(self.conv18(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm19(self.conv19(out)), negative_slope=0.1)
out = F.leaky_relu(self.batchnorm20(self.conv20(out)), negative_slope=0.1)
out = torch.cat([passthrough, out], 1)
out = F.leaky_relu(self.batchnorm21(self.conv21(out)), negative_slope=0.1)
out = self.conv22(out)
return out
'0.4.1.post2' 答案 0 :(得分：4)
我会尝试使用较小的批量。从1开始，然后检查最大数。 我也可以尝试减小输入张量的尺寸。 对于GPU来说，您的网络并不小
答案 1 :(得分：0)
以下是您可以尝试的周期：
ma = torch.cuda.memory_allocated()
print(ma)
mc = torch.cuda.memory_cached()
print(mc)
torch.cuda.empty_cache()
ma = torch.cuda.memory_allocated()
print(ma)
mc = torch.cuda.memory_cached()
print(mc)
# 653475518
# 952107008
# 383533568
# 385875968
查看如何释放内存。另一种技术称为按需将批处理加载到GPU。因此，不是整个数据集，而是一次或几个批次。
将单个批处理加载到GPU中，您基本上可以在μ秒内完成测量，而诸如矩阵乘法之类的张量运算则需要m秒。