Spaces:
Sleeping
Sleeping
File size: 9,280 Bytes
c92c0ec |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from migc.migc_layers import CBAM, CrossAttention, LayoutAttention
class FourierEmbedder():
def __init__(self, num_freqs=64, temperature=100):
self.num_freqs = num_freqs
self.temperature = temperature
self.freq_bands = temperature ** ( torch.arange(num_freqs) / num_freqs )
@ torch.no_grad()
def __call__(self, x, cat_dim=-1):
out = []
for freq in self.freq_bands:
out.append( torch.sin( freq*x ) )
out.append( torch.cos( freq*x ) )
return torch.cat(out, cat_dim) # torch.Size([5, 30, 64])
class PositionNet(nn.Module):
def __init__(self, in_dim, out_dim, fourier_freqs=8):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy
# -------------------------------------------------------------- #
self.linears_position = nn.Sequential(
nn.Linear(self.position_dim, 512),
nn.SiLU(),
nn.Linear(512, 512),
nn.SiLU(),
nn.Linear(512, out_dim),
)
def forward(self, boxes):
# embedding position (it may includes padding as placeholder)
xyxy_embedding = self.fourier_embedder(boxes) # B*1*4 --> B*1*C torch.Size([5, 1, 64])
xyxy_embedding = self.linears_position(xyxy_embedding) # B*1*C --> B*1*768 torch.Size([5, 1, 768])
return xyxy_embedding
class SAC(nn.Module):
def __init__(self, C, number_pro=30):
super().__init__()
self.C = C
self.number_pro = number_pro
self.conv1 = nn.Conv2d(C + 1, C, 1, 1)
self.cbam1 = CBAM(C)
self.conv2 = nn.Conv2d(C, 1, 1, 1)
self.cbam2 = CBAM(number_pro, reduction_ratio=1)
def forward(self, x, guidance_mask, sac_scale=None):
'''
:param x: (B, phase_num, HW, C)
:param guidance_mask: (B, phase_num, H, W)
:return:
'''
B, phase_num, HW, C = x.shape
_, _, H, W = guidance_mask.shape
guidance_mask = guidance_mask.view(guidance_mask.shape[0], phase_num, -1)[
..., None] # (B, phase_num, HW, 1)
null_x = torch.zeros_like(x[:, [0], ...]).to(x.device)
null_mask = torch.zeros_like(guidance_mask[:, [0], ...]).to(guidance_mask.device)
x = torch.cat([x, null_x], dim=1)
guidance_mask = torch.cat([guidance_mask, null_mask], dim=1)
phase_num += 1
scale = torch.cat([x, guidance_mask], dim=-1) # (B, phase_num, HW, C+1)
scale = scale.view(-1, H, W, C + 1) # (B * phase_num, H, W, C+1)
scale = scale.permute(0, 3, 1, 2) # (B * phase_num, C+1, H, W)
scale = self.conv1(scale) # (B * phase_num, C, H, W)
scale = self.cbam1(scale) # (B * phase_num, C, H, W)
scale = self.conv2(scale) # (B * phase_num, 1, H, W)
scale = scale.view(B, phase_num, H, W) # (B, phase_num, H, W)
null_scale = scale[:, [-1], ...]
scale = scale[:, :-1, ...]
x = x[:, :-1, ...]
pad_num = self.number_pro - phase_num + 1
ori_phase_num = scale[:, 1:-1, ...].shape[1]
phase_scale = torch.cat([scale[:, 1:-1, ...], null_scale.repeat(1, pad_num, 1, 1)], dim=1)
shuffled_order = torch.randperm(phase_scale.shape[1])
inv_shuffled_order = torch.argsort(shuffled_order)
random_phase_scale = phase_scale[:, shuffled_order, ...]
scale = torch.cat([scale[:, [0], ...], random_phase_scale, scale[:, [-1], ...]], dim=1)
# (B, number_pro, H, W)
scale = self.cbam2(scale) # (B, number_pro, H, W)
scale = scale.view(B, self.number_pro, HW)[..., None] # (B, number_pro, HW)
random_phase_scale = scale[:, 1: -1, ...]
phase_scale = random_phase_scale[:, inv_shuffled_order[:ori_phase_num], :]
if sac_scale is not None:
instance_num = len(sac_scale)
for i in range(instance_num):
phase_scale[:, i, ...] = phase_scale[:, i, ...] * sac_scale[i]
scale = torch.cat([scale[:, [0], ...], phase_scale, scale[:, [-1], ...]], dim=1)
scale = scale.softmax(dim=1) # (B, phase_num, HW, 1)
out = (x * scale).sum(dim=1, keepdims=True) # (B, 1, HW, C)
return out, scale
class MIGC(nn.Module):
def __init__(self, C, attn_type='base', context_dim=768, heads=8):
super().__init__()
self.ea = CrossAttention(query_dim=C, context_dim=context_dim,
heads=heads, dim_head=C // heads,
dropout=0.0)
self.la = LayoutAttention(query_dim=C,
heads=heads, dim_head=C // heads,
dropout=0.0)
self.norm = nn.LayerNorm(C)
self.sac = SAC(C)
self.pos_net = PositionNet(in_dim=768, out_dim=768)
def forward(self, ca_x, guidance_mask, other_info, return_fuser_info=False):
# x: (B, instance_num+1, HW, C)
# guidance_mask: (B, instance_num, H, W)
# box: (instance_num, 4)
# image_token: (B, instance_num+1, HW, C)
full_H = other_info['height']
full_W = other_info['width']
B, _, HW, C = ca_x.shape
instance_num = guidance_mask.shape[1]
down_scale = int(math.sqrt(full_H * full_W // ca_x.shape[2]))
H = full_H // down_scale
W = full_W // down_scale
guidance_mask = F.interpolate(guidance_mask, size=(H, W), mode='bilinear') # (B, instance_num, H, W)
supplement_mask = other_info['supplement_mask'] # (B, 1, 64, 64)
supplement_mask = F.interpolate(supplement_mask, size=(H, W), mode='bilinear') # (B, 1, H, W)
image_token = other_info['image_token']
assert image_token.shape == ca_x.shape
context = other_info['context_pooler']
box = other_info['box']
box = box.view(B * instance_num, 1, -1)
box_token = self.pos_net(box)
context = torch.cat([context[1:, ...], box_token], dim=1)
ca_scale = other_info['ca_scale'] if 'ca_scale' in other_info else None
ea_scale = other_info['ea_scale'] if 'ea_scale' in other_info else None
sac_scale = other_info['sac_scale'] if 'sac_scale' in other_info else None
ea_x, ea_attn = self.ea(self.norm(image_token[:, 1:, ...].view(B * instance_num, HW, C)),
context=context, return_attn=True)
ea_x = ea_x.view(B, instance_num, HW, C)
ea_x = ea_x * guidance_mask.view(B, instance_num, HW, 1)
ca_x[:, 1:, ...] = ca_x[:, 1:, ...] * guidance_mask.view(B, instance_num, HW, 1) # (B, phase_num, HW, C)
if ca_scale is not None:
assert len(ca_scale) == instance_num
for i in range(instance_num):
ca_x[:, i+1, ...] = ca_x[:, i+1, ...] * ca_scale[i] + ea_x[:, i, ...] * ea_scale[i]
else:
ca_x[:, 1:, ...] = ca_x[:, 1:, ...] + ea_x
ori_image_token = image_token[:, 0, ...] # (B, HW, C)
fusion_template = self.la(x=ori_image_token, guidance_mask=torch.cat([guidance_mask[:, :, ...], supplement_mask], dim=1)) # (B, HW, C)
fusion_template = fusion_template.view(B, 1, HW, C) # (B, 1, HW, C)
ca_x = torch.cat([ca_x, fusion_template], dim = 1)
ca_x[:, 0, ...] = ca_x[:, 0, ...] * supplement_mask.view(B, HW, 1)
guidance_mask = torch.cat([
supplement_mask,
guidance_mask,
torch.ones(B, 1, H, W).to(guidance_mask.device)
], dim=1)
out_MIGC, sac_scale = self.sac(ca_x, guidance_mask, sac_scale=sac_scale)
if return_fuser_info:
fuser_info = {}
fuser_info['sac_scale'] = sac_scale.view(B, instance_num + 2, H, W)
fuser_info['ea_attn'] = ea_attn.mean(dim=1).view(B, instance_num, H, W, 2)
return out_MIGC, fuser_info
else:
return out_MIGC
class NaiveFuser(nn.Module):
def __init__(self):
super().__init__()
def forward(self, ca_x, guidance_mask, other_info, return_fuser_info=False):
# ca_x: (B, instance_num+1, HW, C)
# guidance_mask: (B, instance_num, H, W)
# box: (instance_num, 4)
# image_token: (B, instance_num+1, HW, C)
full_H = other_info['height']
full_W = other_info['width']
B, _, HW, C = ca_x.shape
instance_num = guidance_mask.shape[1]
down_scale = int(math.sqrt(full_H * full_W // ca_x.shape[2]))
H = full_H // down_scale
W = full_W // down_scale
guidance_mask = F.interpolate(guidance_mask, size=(H, W), mode='bilinear') # (B, instance_num, H, W)
guidance_mask = torch.cat([torch.ones(B, 1, H, W).to(guidance_mask.device), guidance_mask * 10], dim=1) # (B, instance_num+1, H, W)
guidance_mask = guidance_mask.view(B, instance_num + 1, HW, 1)
out_MIGC = (ca_x * guidance_mask).sum(dim=1) / (guidance_mask.sum(dim=1) + 1e-6)
if return_fuser_info:
return out_MIGC, None
else:
return out_MIGC |