Keep-rate scheduling of DropBlock in a multi-GPU environment

[beomjin-ahn]

Hello, I found an issue while trying to train your model. In your code, the variable 'self.num_batches_tracked' should count the progress of the episode by increasing when the model is called. But in the multi-GPU environment, the modification of the variable in the forward() is ignored because a DataParallel replicates the model into each GPU and the updates are destroyed after forward(). So the variable just moves up and down with 0 and 1. I think this should be fixed. Thanks :)

[kjunelee]

Thank you for pointing this out. I will try to fix this problem.

[creiser]

This effectively deactivates dropout when using multiple GPUs. State variables that are modified in the forward pass need to be registered as buffer. Here is the fix:

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, drop_rate=0.0, drop_block=False,
                 block_size=1, use_se=False):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.LeakyReLU(0.1)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv3x3(planes, planes)
        self.bn3 = nn.BatchNorm2d(planes)
        self.maxpool = nn.MaxPool2d(stride)
        self.downsample = downsample
        self.stride = stride
        self.drop_rate = drop_rate
        #self.num_batches_tracked = 0 # DataParallel will only keep changes that were made to registered buffers
        self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
        self.drop_block = drop_block
        self.block_size = block_size
        self.DropBlock = DropBlock(block_size=self.block_size)
        self.use_se = use_se
        if self.use_se:
            self.se = SELayer(planes, 4)

    def forward(self, x):
        self.num_batches_tracked += 1

        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)
        if self.use_se:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        out = self.maxpool(out)

        if self.drop_rate > 0:
            if self.drop_block == True:
                feat_size = out.size()[2]
                keep_rate = max(1.0 - self.drop_rate / (20*2000) * (self.num_batches_tracked.item()), 1.0 - self.drop_rate)
                gamma = (1 - keep_rate) / self.block_size**2 * feat_size**2 / (feat_size - self.block_size + 1)**2
                out = self.DropBlock(out, gamma=gamma)
            else:
                out = F.dropout(out, p=self.drop_rate, training=self.training, inplace=True)

        return out