Extracting speccific layers' output from a module list

I’m using a pretrained model in which there are several self_attentions sequentially stacked each one after another and the number of them is 12. I need to extract the output of the fourth and 10th blocks of this sequential layers. In the following script, the BLock represents each self-attention layer:

dpr = [x.item() for x in torch.linspace(0, 0.1, 12)]  # stochastic depth decay rule
    self.blocks = nn.ModuleList([
            dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, attention_type=self.attention_type)
        for i in range(12)])

The self-attention layers (stack of Block) are as follows:

## Attention blocks
    for blk in self.blocks:
        x = blk(x, B, T, W)

How can I extract the fourth and the 10th layers’ output?

You can use forward hooks as described here and register them to the 4th and 10th layers.

@ptrblck ,Thanks a lot .