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# Adapted from https://github.com/MCG-NJU/EMA-VFI/blob/main/model/feature_extractor.py
import torch
import torch.nn as nn
import math
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
def window_partition(x, window_size):
B, H, W, C = x.shape
x = x.view(B, H // window_size[0], window_size[0], W // window_size[1], window_size[1], C)
windows = (
x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0]*window_size[1], C)
)
return windows
def window_reverse(windows, window_size, H, W):
nwB, N, C = windows.shape
windows = windows.view(-1, window_size[0], window_size[1], C)
B = int(nwB / (H * W / window_size[0] / window_size[1]))
x = windows.view(
B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -1
)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
def pad_if_needed(x, size, window_size):
n, h, w, c = size
pad_h = math.ceil(h / window_size[0]) * window_size[0] - h
pad_w = math.ceil(w / window_size[1]) * window_size[1] - w
if pad_h > 0 or pad_w > 0: # center-pad the feature on H and W axes
img_mask = torch.zeros((1, h+pad_h, w+pad_w, 1)) # 1 H W 1
h_slices = (
slice(0, pad_h//2),
slice(pad_h//2, h+pad_h//2),
slice(h+pad_h//2, None),
)
w_slices = (
slice(0, pad_w//2),
slice(pad_w//2, w+pad_w//2),
slice(w+pad_w//2, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(
img_mask, window_size
) # nW, window_size*window_size, 1
mask_windows = mask_windows.squeeze(-1)
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 nn.functional.pad(
x,
(0, 0, pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2),
), attn_mask
return x, None
def depad_if_needed(x, size, window_size):
n, h, w, c = size
pad_h = math.ceil(h / window_size[0]) * window_size[0] - h
pad_w = math.ceil(w / window_size[1]) * window_size[1] - w
if pad_h > 0 or pad_w > 0: # remove the center-padding on feature
return x[:, pad_h // 2 : pad_h // 2 + h, pad_w // 2 : pad_w // 2 + w, :].contiguous()
return x
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
self.relu = nn.ReLU(inplace=True)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
x = self.dwconv(x, H, W)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class InterFrameAttention(nn.Module):
def __init__(self, dim, motion_dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.motion_dim = motion_dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.cor_embed = nn.Linear(2, motion_dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.motion_proj = nn.Linear(motion_dim, motion_dim)
self.proj_drop = nn.Dropout(proj_drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x1, x2, cor, H, W, mask=None):
B, N, C = x1.shape
B, N, C_c = cor.shape
q = self.q(x1).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
kv = self.kv(x2).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
cor_embed_ = self.cor_embed(cor)
cor_embed = cor_embed_.reshape(B, N, self.num_heads, self.motion_dim // self.num_heads).permute(0, 2, 1, 3)
k, v = kv[0], kv[1]
attn = (q @ k.transpose(-2, -1)) * self.scale
if mask is not None:
nW = mask.shape[0] # mask: nW, N, N
attn = attn.view(B // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
1
).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = attn.softmax(dim=-1)
else:
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
c_reverse = (attn @ cor_embed).transpose(1, 2).reshape(B, N, -1)
motion = self.motion_proj(c_reverse-cor_embed_)
x = self.proj(x)
x = self.proj_drop(x)
return x, motion
class MotionFormerBlock(nn.Module):
def __init__(self, dim, motion_dim, num_heads, window_size=0, shift_size=0, mlp_ratio=4., bidirectional=True, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,):
super().__init__()
self.window_size = window_size
if not isinstance(self.window_size, (tuple, list)):
self.window_size = to_2tuple(window_size)
self.shift_size = shift_size
if not isinstance(self.shift_size, (tuple, list)):
self.shift_size = to_2tuple(shift_size)
self.bidirectional = bidirectional
self.norm1 = norm_layer(dim)
self.attn = InterFrameAttention(
dim,
motion_dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, cor, H, W, B):
x = x.view(2*B, H, W, -1)
x_pad, mask = pad_if_needed(x, x.size(), self.window_size)
cor_pad, _ = pad_if_needed(cor, cor.size(), self.window_size)
if self.shift_size[0] or self.shift_size[1]:
_, H_p, W_p, C = x_pad.shape
x_pad = torch.roll(x_pad, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
cor_pad = torch.roll(cor_pad, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
if hasattr(self, 'HW') and self.HW.item() == H_p * W_p:
shift_mask = self.attn_mask
else:
shift_mask = torch.zeros((1, H_p, W_p, 1)) # 1 H W 1
h_slices = (slice(0, -self.window_size[0]),
slice(-self.window_size[0], -self.shift_size[0]),
slice(-self.shift_size[0], None))
w_slices = (slice(0, -self.window_size[1]),
slice(-self.window_size[1], -self.shift_size[1]),
slice(-self.shift_size[1], None))
cnt = 0
for h in h_slices:
for w in w_slices:
shift_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(shift_mask, self.window_size).squeeze(-1)
shift_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
shift_mask = shift_mask.masked_fill(shift_mask != 0,
float(-100.0)).masked_fill(shift_mask == 0,
float(0.0))
if mask is not None:
shift_mask = shift_mask.masked_fill(mask != 0,
float(-100.0))
self.register_buffer("attn_mask", shift_mask)
self.register_buffer("HW", torch.Tensor([H_p*W_p]))
else:
shift_mask = mask
if shift_mask is not None:
shift_mask = shift_mask.to(x_pad.device)
_, Hw, Ww, C = x_pad.shape
x_win = window_partition(x_pad, self.window_size)
cor_win = window_partition(cor_pad, self.window_size)
nwB = x_win.shape[0]
x_norm = self.norm1(x_win)
x_reverse = torch.cat([x_norm[nwB//2:], x_norm[:nwB//2]])
x_appearence, x_motion = self.attn(x_norm, x_reverse, cor_win, H, W, shift_mask)
x_norm = x_norm + self.drop_path(x_appearence)
x_back = x_norm
x_back_win = window_reverse(x_back, self.window_size, Hw, Ww)
x_motion = window_reverse(x_motion, self.window_size, Hw, Ww)
if self.shift_size[0] or self.shift_size[1]:
x_back_win = torch.roll(x_back_win, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))
x_motion = torch.roll(x_motion, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2))
x = depad_if_needed(x_back_win, x.size(), self.window_size).view(2*B, H * W, -1)
x_motion = depad_if_needed(x_motion, cor.size(), self.window_size).view(2*B, H * W, -1)
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
return x, x_motion
class ConvBlock(nn.Module):
def __init__(self, in_dim, out_dim, depths=2,act_layer=nn.PReLU):
super().__init__()
layers = []
for i in range(depths):
if i == 0:
layers.append(nn.Conv2d(in_dim, out_dim, 3,1,1))
else:
layers.append(nn.Conv2d(out_dim, out_dim, 3,1,1))
layers.extend([
act_layer(out_dim),
])
self.conv = nn.Sequential(*layers)
def _init_weights(self, m):
if isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.conv(x)
return x
class OverlapPatchEmbed(nn.Module):
def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
patch_size = to_2tuple(patch_size)
self.patch_size = patch_size
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class CrossScalePatchEmbed(nn.Module):
def __init__(self, in_dims=[16,32,64], embed_dim=768):
super().__init__()
base_dim = in_dims[0]
layers = []
for i in range(len(in_dims)):
for j in range(2 ** i):
layers.append(nn.Conv2d(in_dims[-1-i], base_dim, 3, 2**(i+1), 1+j, 1+j))
self.layers = nn.ModuleList(layers)
self.proj = nn.Conv2d(base_dim * len(layers), embed_dim, 1, 1)
self.norm = nn.LayerNorm(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, xs):
ys = []
k = 0
for i in range(len(xs)):
for _ in range(2 ** i):
ys.append(self.layers[k](xs[-1-i]))
k += 1
x = self.proj(torch.cat(ys,1))
_, _, H, W = x.shape
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x, H, W
class MotionFormer(nn.Module):
def __init__(self, in_chans=3, embed_dims=[32, 64, 128, 256, 512], motion_dims=64, num_heads=[8, 16],
mlp_ratios=[4, 4], qkv_bias=True, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
depths=[2, 2, 2, 6, 2], window_sizes=[11, 11],**kwarg):
super().__init__()
self.depths = depths
self.num_stages = len(embed_dims)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
self.conv_stages = self.num_stages - len(num_heads)
for i in range(self.num_stages):
if i == 0:
block = ConvBlock(in_chans,embed_dims[i],depths[i])
else:
if i < self.conv_stages:
patch_embed = nn.Sequential(
nn.Conv2d(embed_dims[i-1], embed_dims[i], 3,2,1),
nn.PReLU(embed_dims[i])
)
block = ConvBlock(embed_dims[i],embed_dims[i],depths[i])
else:
if i == self.conv_stages:
patch_embed = CrossScalePatchEmbed(embed_dims[:i],
embed_dim=embed_dims[i])
else:
patch_embed = OverlapPatchEmbed(patch_size=3,
stride=2,
in_chans=embed_dims[i - 1],
embed_dim=embed_dims[i])
block = nn.ModuleList([MotionFormerBlock(
dim=embed_dims[i], motion_dim=motion_dims[i], num_heads=num_heads[i-self.conv_stages], window_size=window_sizes[i-self.conv_stages],
shift_size= 0 if (j % 2) == 0 else window_sizes[i-self.conv_stages] // 2,
mlp_ratio=mlp_ratios[i-self.conv_stages], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + j], norm_layer=norm_layer)
for j in range(depths[i])])
norm = norm_layer(embed_dims[i])
setattr(self, f"norm{i + 1}", norm)
setattr(self, f"patch_embed{i + 1}", patch_embed)
cur += depths[i]
setattr(self, f"block{i + 1}", block)
self.cor = {}
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def get_cor(self, shape, device):
k = (str(shape), str(device))
if k not in self.cor:
tenHorizontal = torch.linspace(-1.0, 1.0, shape[2], device=device).view(
1, 1, 1, shape[2]).expand(shape[0], -1, shape[1], -1).permute(0, 2, 3, 1)
tenVertical = torch.linspace(-1.0, 1.0, shape[1], device=device).view(
1, 1, shape[1], 1).expand(shape[0], -1, -1, shape[2]).permute(0, 2, 3, 1)
self.cor[k] = torch.cat([tenHorizontal, tenVertical], -1).to(device)
return self.cor[k]
def forward(self, x1, x2):
B = x1.shape[0]
x = torch.cat([x1, x2], 0)
motion_features = []
appearence_features = []
xs = []
for i in range(self.num_stages):
motion_features.append([])
patch_embed = getattr(self, f"patch_embed{i + 1}",None)
block = getattr(self, f"block{i + 1}",None)
norm = getattr(self, f"norm{i + 1}",None)
if i < self.conv_stages:
if i > 0:
x = patch_embed(x)
x = block(x)
xs.append(x)
else:
if i == self.conv_stages:
x, H, W = patch_embed(xs)
else:
x, H, W = patch_embed(x)
cor = self.get_cor((x.shape[0], H, W), x.device)
for blk in block:
x, x_motion = blk(x, cor, H, W, B)
motion_features[i].append(x_motion.reshape(2*B, H, W, -1).permute(0, 3, 1, 2).contiguous())
x = norm(x)
x = x.reshape(2*B, H, W, -1).permute(0, 3, 1, 2).contiguous()
motion_features[i] = torch.cat(motion_features[i], 1)
appearence_features.append(x)
return appearence_features, motion_features
class DWConv(nn.Module):
def __init__(self, dim):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).reshape(B, C, H, W)
x = self.dwconv(x)
x = x.reshape(B, C, -1).transpose(1, 2)
return x
def feature_extractor(**kargs):
model = MotionFormer(**kargs)
return model |