annotator/uniformer/mmseg/ops/encoding.py

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


class Encoding(nn.Module):
    """Encoding Layer: a learnable residual encoder.

    Input is of shape  (batch_size, channels, height, width).
    Output is of shape (batch_size, num_codes, channels).

    Args:
        channels: dimension of the features or feature channels
        num_codes: number of code words
    """

    def __init__(self, channels, num_codes):
        super(Encoding, self).__init__()
        # init codewords and smoothing factor
        self.channels, self.num_codes = channels, num_codes
        std = 1. / ((num_codes * channels)**0.5)
        # [num_codes, channels]
        self.codewords = nn.Parameter(
            torch.empty(num_codes, channels,
                        dtype=torch.float).uniform_(-std, std),
            requires_grad=True)
        # [num_codes]
        self.scale = nn.Parameter(
            torch.empty(num_codes, dtype=torch.float).uniform_(-1, 0),
            requires_grad=True)

    @staticmethod
    def scaled_l2(x, codewords, scale):
        num_codes, channels = codewords.size()
        batch_size = x.size(0)
        reshaped_scale = scale.view((1, 1, num_codes))
        expanded_x = x.unsqueeze(2).expand(
            (batch_size, x.size(1), num_codes, channels))
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))

        scaled_l2_norm = reshaped_scale * (
            expanded_x - reshaped_codewords).pow(2).sum(dim=3)
        return scaled_l2_norm

    @staticmethod
    def aggregate(assignment_weights, x, codewords):
        num_codes, channels = codewords.size()
        reshaped_codewords = codewords.view((1, 1, num_codes, channels))
        batch_size = x.size(0)

        expanded_x = x.unsqueeze(2).expand(
            (batch_size, x.size(1), num_codes, channels))
        encoded_feat = (assignment_weights.unsqueeze(3) *
                        (expanded_x - reshaped_codewords)).sum(dim=1)
        return encoded_feat

    def forward(self, x):
        assert x.dim() == 4 and x.size(1) == self.channels
        # [batch_size, channels, height, width]
        batch_size = x.size(0)
        # [batch_size, height x width, channels]
        x = x.view(batch_size, self.channels, -1).transpose(1, 2).contiguous()
        # assignment_weights: [batch_size, channels, num_codes]
        assignment_weights = F.softmax(
            self.scaled_l2(x, self.codewords, self.scale), dim=2)
        # aggregate
        encoded_feat = self.aggregate(assignment_weights, x, self.codewords)
        return encoded_feat

    def __repr__(self):
        repr_str = self.__class__.__name__
        repr_str += f'(Nx{self.channels}xHxW =>Nx{self.num_codes}' \
                    f'x{self.channels})'
        return repr_str