ResNet modifications to output shape [batch, timestep, num_outputs]

username: Mole_m7b5
cooked: <p>Hi,</p>
<p>I’m looking to use ResNet for a multivariate regression problem. In itself this seems fairly straight forward as the <code>num_classes</code> argument just needs to be set to the number of output variables to be predicted.</p>
<p>I, however, am using audio data and want to make a prediction for each timestep/frame, so my output data needs to be of shape<code> [batch, timestep, num_outputs]</code> as opposed to ResNet’s default output of <code>[batch, num_outputs]</code>.</p>
<p>I’m using the provided example code from the PyTorch docs <a href="https://pytorch.org/vision/0.8/_modules/torchvision/models/resnet.html" rel="noopener nofollow ugc">here</a></p>
<p>I’ve adapted some of the code outlined below and state at the start of each code block the original defaults and what I’ve changed them to. I wonder if anyone can see a more sensible/less disruptive way of achieving my desired output shape.</p>
<p>I’ve changed padding from dilation → ‘same’</p>
<pre><code class="lang-auto">def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding='same', groups=groups, bias=False, dilation=dilation)
</code></pre>
<p>I’ve changed the following:<br>
all strides from 2 → 1<br>
nn.Maxpool2D kernal size from 3 → (1,3)<br>
fc input size from 512*block.expansion ----&gt; whatever I need it to be</p>
<pre><code class="lang-auto">class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=1, padding='same',
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=(1,3), stride=None, padding=0)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=1,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(21504, num_classes) # input was originally 512*block.expansion
</code></pre>
<p>Changes made:<br>
Permuted the tensor after self.layer4() to ensure correct dimension ordering for output<br>
Took out pooling before self.fc()<br>
Flatten starting from dim=2 to keep the timestep dimension intact.</p>
<pre><code class="lang-auto"> def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x) #(batch, channel, time, freq)

        x = torch.permute(x,(0,2,1,3)) # (batch,time,filt_channel,freq)

        # x = self.avgpool(x)

        x = torch.flatten(x, start_dim=2)
        x = self.fc(x)

        return x
</code></pre>