File size: 5,512 Bytes
a6dd040 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | import torch
import torch.nn.functional as F
from .position import PositionEmbeddingSine
def generate_window_grid(h_min, h_max, w_min, w_max, len_h, len_w, device=None):
assert device is not None
x, y = torch.meshgrid(
[
torch.linspace(w_min, w_max, len_w, device=device),
torch.linspace(h_min, h_max, len_h, device=device),
],
)
grid = torch.stack((x, y), -1).transpose(0, 1).float() # [H, W, 2]
return grid
def normalize_coords(coords, h, w):
# coords: [B, H, W, 2]
c = torch.Tensor([(w - 1) / 2.0, (h - 1) / 2.0]).float().to(coords.device)
return (coords - c) / c # [-1, 1]
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 / 255.0 - mean) / std
img1 = (img1 / 255.0 - mean) / std
return img0, img1
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 generate_shift_window_attn_mask(
input_resolution,
window_size_h,
window_size_w,
shift_size_h,
shift_size_w,
device=torch.device("cuda"),
):
# ref: https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
# calculate attention mask for SW-MSA
h, w = input_resolution
img_mask = torch.zeros((1, h, w, 1)).to(device) # 1 H W 1
h_slices = (
slice(0, -window_size_h),
slice(-window_size_h, -shift_size_h),
slice(-shift_size_h, None),
)
w_slices = (
slice(0, -window_size_w),
slice(-window_size_w, -shift_size_w),
slice(-shift_size_w, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = split_feature(
img_mask, num_splits=input_resolution[-1] // window_size_w, channel_last=True
)
mask_windows = mask_windows.view(-1, window_size_h * window_size_w)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
attn_mask == 0, float(0.0)
)
return attn_mask
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
def mv_feature_add_position(features, attn_splits, feature_channels):
pos_enc = PositionEmbeddingSine(num_pos_feats=feature_channels // 2)
assert features.dim() == 4 # [B*V, C, H, W]
if attn_splits > 1: # add position in splited window
features_splits = split_feature(features, num_splits=attn_splits)
position = pos_enc(features_splits)
features_splits = features_splits + position
features = merge_splits(features_splits, num_splits=attn_splits)
else:
position = pos_enc(features)
features = features + position
return features
|