heads/hypercolumn_linear/head.py

"""Hypercolumn Linear: concatenate features from intermediate blocks, single linear layer."""

import torch
import torch.nn as nn
import torch.nn.functional as F

N_PREFIX = 5


class HypercolumnLinear(nn.Module):
    name = "hypercolumn_linear"
    needs_intermediates = True

    def __init__(self, feat_dim=768, num_classes=150, n_blocks=4):
        super().__init__()
        self.n_blocks = n_blocks
        self.conv = nn.Conv2d(feat_dim * n_blocks, num_classes, 1)

    def forward(self, spatial, inter=None):
        B, C, H, W = spatial.shape
        if inter is None:
            raise ValueError("hypercolumn_linear requires intermediate block features")
        spatials = []
        for feat in inter:
            patches = feat[:, N_PREFIX:, :]
            s = patches.permute(0, 2, 1).reshape(B, C, H, W)
            spatials.append(s)
        stacked = torch.cat(spatials, dim=1)
        return self.conv(stacked)