Suppose that in a MobileNet v2 Bottleneck block the input volume has shape 64×64×1664×64×16. If we use 3232 filters for the expansion and 1616 filters for the projection. What is the size of the input and output volume of the depthwise convolution, assuming a pad='same'?1 point

In a MobileNet v2 Bottleneck block, the input volume has a shape of 64×64×16.

Step 1: Expansion The first step in the bottleneck block is the expansion. Here, a 1x1 convolution is applied to the input with 32 filters. This expands the number of channels in the input. The size of the output volume after the expansion is 64x64x32.

Step 2: Depthwise Convolution The next step is the depthwise convolution. In this step, a 3x3 depthwise convolution is applied. Since the padding is 'same', the spatial dimensions of the output volume will remain the same as the input volume. Therefore, the size of the output volume after the depthwise convolution is 64x64x32.

Step 3: Projection The final step in the bottleneck block is the projection. Here, a 1x1 convolution is applied to the output of the depthwise convolution with 16 filters. This reduces the number of channels in the output volume. The size of the output volume after the projection is 64x64x16.

So, the size of the input volume for the depthwise convolution is 64x64x32 and the size of the output volume is 64x64x32.

In a MobileNet v2 Bottleneck block, the input volume is first passed through a 1x1 convolution (expansion) layer, then a 3x3 depthwise convolution layer, and finally another 1x1 convolution (projection) layer.

1. The expansion layer expands the number of channels in the input. So, if we use 32 filters for the expansion, the output volume of the expansion layer will be 64x64x32.

2. The depthwise convolution layer applies a single filter to each input channel. The 'same' padding means that the spatial dimensions of the output volume will be the same as the input volume. So, the size of the input volume to the depthwise convolution will be 64x64x32 (the output volume of the expansion layer), and the size of the output volume of the depthwise convolution will also be 64x64x32.

3. The projection layer reduces the number of channels in the output from the depthwise convolution. If we use 16 filters for the projection, the output volume of the projection layer (and the entire bottleneck block) will be 64x64x16.