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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 upsample_flow_with_mask(flow, up_mask, upsample_factor, is_depth=False):
# convex upsampling following raft
mask = up_mask
b, flow_channel, h, w = flow.shape
mask = mask.view(b, 1, 9, upsample_factor, upsample_factor, h, w) # [B, 1, 9, K, K, H, W]
mask = torch.softmax(mask, dim=2)
multiplier = 1 if is_depth else upsample_factor
up_flow = F.unfold(multiplier * flow, [3, 3], padding=1)
up_flow = up_flow.view(b, flow_channel, 9, 1, 1, h, w) # [B, 2, 9, 1, 1, H, W]
up_flow = torch.sum(mask * up_flow, dim=2) # [B, 2, K, K, H, W]
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) # [B, 2, K, H, K, W]
up_flow = up_flow.reshape(
b, flow_channel, upsample_factor * h, upsample_factor * w
) # [B, 2, K*H, K*W]
return up_flow
def split_feature_1d(
feature,
num_splits=2,
):
# feature: [B, W, C]
b, w, c = feature.size()
assert w % num_splits == 0
b_new = b * num_splits
w_new = w // num_splits
feature = feature.view(b, num_splits, w // num_splits, c).view(
b_new, w_new, c
) # [B*K, W/K, C]
return feature
def merge_splits_1d(
splits,
h,
num_splits=2,
):
b, w, c = splits.size()
new_b = b // num_splits // h
splits = splits.view(new_b, h, num_splits, w, c)
merge = splits.view(new_b, h, num_splits * w, c) # [B, H, W, C]
return merge
def window_partition_1d(x, window_size_w):
"""
Args:
x: (B, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, C)
"""
B, W, C = x.shape
x = x.view(B, W // window_size_w, window_size_w, C).view(-1, window_size_w, C)
return x
def generate_shift_window_attn_mask_1d(
input_w, window_size_w, shift_size_w, device=torch.device("cuda")
):
# calculate attention mask for SW-MSA
img_mask = torch.zeros((1, input_w, 1)).to(device) # 1 W 1
w_slices = (
slice(0, -window_size_w),
slice(-window_size_w, -shift_size_w),
slice(-shift_size_w, None),
)
cnt = 0
for w in w_slices:
img_mask[:, w, :] = cnt
cnt += 1
mask_windows = window_partition_1d(img_mask, window_size_w) # nW, window_size, 1
mask_windows = mask_windows.view(-1, window_size_w)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(
2
) # nW, window_size, window_size
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
attn_mask == 0, float(0.0)
)
return attn_mask
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