Cuda out of Memory error Training ImageNet

KanZa: <p>I am trying and testing a repository on ImageNet datasets which is actually designed for small datasets.<br>
I am trying for ILSVRC 2012 (Training Image are 1.2 Million)</p>
<p>I tried with Batch Size = 64 <span class="hashtag">#32</span> and 128 also<br>
I also tried my experiment with ResNet18 and RestNet50 both<br>
I tried with a bigger GPU which has 128GB RAM and with 256GB RAM<br>
I am only doing Image Classification by Random Method</p>
<p>CUDA_VISIBLE_DEVICES = 0<br>
NUM_TRAIN = 120000<br>
BATCH = 64<br>
SUBSET    = 100000<br>
ADDENDUM  = 2500</p>
<pre><code class="lang-auto">elif dataset == 'imagenet':
        test_transform = T.Compose([
            T.ToTensor(),               
            T.CenterCrop((224, 224)),   
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) 
        ])

        data_train = ImageFolder(root='./data/toy_dataset/train', transform=train_transform) # todo: change back to train
        data_test = ImageFolder(root='./data/toy_dataset/train', transform=test_transform)

        data_unlabeled = MyDataset(dataset, True, test_transform)
        NO_CLASSES = 200
        adden = 100
        no_train = NUM_TRAIN

    return data_train, data_unlabeled, data_test, adden, NO_CLASSES, no_train
</code></pre>
<pre><code class="lang-auto">if method == 'Random':
        arg = np.random.randint(SUBSET, size=SUBSET)
</code></pre>

eqy: <p>Could you post a small script that reproduces the issue please? I’m also curious what the exact hardware setup is as I’m not familiar with any GPU hardware with 256GiB or even 128GiB RAM.</p>
<p>I would consider decreasing the batch size further (though keep in mind at very small sizes normalizations like batchnorm might misbehave).</p>

KanZa: <pre><code class="lang-auto">Traceback (most recent call last):
  File "main.py", line 151, in &lt;module&gt;
    train(models, method, criterion, optimizers, schedulers, dataloaders, args.no_of_epochs, EPOCHL)
  File "/po1/kanza.ali/workspace/OtherDataSets/ImageNet/train_test.py", line 112, in train
    loss = train_epoch(models, method, criterion, optimizers, dataloaders, epoch, epoch_loss)
  File "/po1/kanza.ali/workspace/OtherDataSets/ImageNet/train_test.py", line 78, in train_epoch
    scores, _, features = models(inputs)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/po1/kanza.ali/workspace/OtherDataSets/ImageNet/models/resnet.py", line 113, in forward
    out2 = self.layer2(out1)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/po1/kanza.ali/workspace/OtherDataSets/ImageNet/models/resnet.py", line 31, in forward
    out += self.shortcut(x)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 446, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/kanza.ali/anaconda3/envs/optuna/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 442, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 10.76 GiB total capacity; 9.27 GiB already allocated; 129.44 MiB fre$

</code></pre>
<p>The image size is different in ImageNet datasets, So I did data augmentations and cropped (224,224), when I use batch size 32 then it gives me the following error</p>
<pre><code class="lang-auto">RuntimeError: mat1 and mat2 shapes cannot be multiplied (32x25088 and 512x1000)

</code></pre>

eqy: <p>What is the exact model architecture in this case? Something seems incorrect as the batch size shouldn’t affect shape compatibility in matmuls in a classification model.</p>

KanZa: <p>are you talking about case 2 with batch 32? If yes I am using ResNet32</p>

eqy: <p>Could you post the code defining the model? I’m not sure it is expected that a reference implementation would encounter a shape mismatch depending on the batch size.</p>

KanZa: <pre><code class="lang-auto">class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        #print(self.conv1)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        #print(self.conv2)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        #print(out)
        return out
class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):    #200 Imagenet Classes 
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)
        #self.linear2 = nn.Linear(1000, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out1 = self.layer1(out)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out4 = self.layer4(out3)
        out = F.avg_pool2d(out4, 4)
        outf = out.view(out.size(0), -1)
        # outl = self.linear(outf)
        out = self.linear(outf)
        return out, outf, [out1, out2, out3, out4]

def ResNet18(num_classes = 10):    #200 Imagenet Classes 
    return ResNet(BasicBlock, [2,2,2,2], num_classes)
</code></pre>

eqy: <p>Where is the definition of <code>ResNet</code>?</p>

KanZa: <p>Can you please check now the above code</p>

eqy: <p>The use of average pooling here looks strange, as I’m not sure it is shape-agnostic. For example, check the reference implementation in <code>torchvision</code> which uses adaptive average pooling to guarantee an output spatial shape of 1x1: <a href="https://pytorch.org/vision/stable/_modules/torchvision/models/resnet.html#resnet18" class="inline-onebox" rel="noopener nofollow ugc">torchvision.models.resnet — Torchvision 0.12 documentation</a></p>

someshfengde: <p>What batch size are you using for training your model ?</p>

KanZa: <p>I have tried with batch size 64 and 128 , both are giving same error</p>

someshfengde: <p>Try reducing batch size maybe input data is consuming lot of memory</p>

KanZa: <p>Thank you so much for your response, I resolved the issue by using Data Parallel</p>