gmflow_module/gmflow/utils.py

import torch
from .position import PositionEmbeddingSine


def split_feature(feature,
                  num_splits=2,
                  channel_last=False,
                  ):
    if channel_last:  # [B, H, W, C]
        b, h, w, c = feature.size()
        assert h % num_splits == 0 and w % num_splits == 0

        b_new = b * num_splits * num_splits
        h_new = h // num_splits
        w_new = w // num_splits

        feature = feature.view(b, num_splits, h // num_splits, num_splits, w // num_splits, c
                               ).permute(0, 1, 3, 2, 4, 5).reshape(b_new, h_new, w_new, c)  # [B*K*K, H/K, W/K, C]
    else:  # [B, C, H, W]
        b, c, h, w = feature.size()
        assert h % num_splits == 0 and w % num_splits == 0

        b_new = b * num_splits * num_splits
        h_new = h // num_splits
        w_new = w // num_splits

        feature = feature.view(b, c, num_splits, h // num_splits, num_splits, w // num_splits
                               ).permute(0, 2, 4, 1, 3, 5).reshape(b_new, c, h_new, w_new)  # [B*K*K, C, H/K, W/K]

    return feature


def merge_splits(splits,
                 num_splits=2,
                 channel_last=False,
                 ):
    if channel_last:  # [B*K*K, H/K, W/K, C]
        b, h, w, c = splits.size()
        new_b = b // num_splits // num_splits

        splits = splits.view(new_b, num_splits, num_splits, h, w, c)
        merge = splits.permute(0, 1, 3, 2, 4, 5).contiguous().view(
            new_b, num_splits * h, num_splits * w, c)  # [B, H, W, C]
    else:  # [B*K*K, C, H/K, W/K]
        b, c, h, w = splits.size()
        new_b = b // num_splits // num_splits

        splits = splits.view(new_b, num_splits, num_splits, c, h, w)
        merge = splits.permute(0, 3, 1, 4, 2, 5).contiguous().view(
            new_b, c, num_splits * h, num_splits * w)  # [B, C, H, W]

    return merge


def normalize_img(img0, img1):
    # loaded images are in [0, 255]
    # normalize by ImageNet mean and std
    mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(img1.device)
    std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(img1.device)
    img0 = (img0 / 255. - mean) / std
    img1 = (img1 / 255. - mean) / std

    return img0, img1


def feature_add_position(feature0, feature1, attn_splits, feature_channels):
    pos_enc = PositionEmbeddingSine(num_pos_feats=feature_channels // 2)

    if attn_splits > 1:  # add position in splited window
        feature0_splits = split_feature(feature0, num_splits=attn_splits)
        feature1_splits = split_feature(feature1, num_splits=attn_splits)

        position = pos_enc(feature0_splits)

        feature0_splits = feature0_splits + position
        feature1_splits = feature1_splits + position

        feature0 = merge_splits(feature0_splits, num_splits=attn_splits)
        feature1 = merge_splits(feature1_splits, num_splits=attn_splits)
    else:
        position = pos_enc(feature0)

        feature0 = feature0 + position
        feature1 = feature1 + position

    return feature0, feature1