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import time
from functools import partial
import math
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.utils
from timm.models.vision_transformer import PatchEmbed, Block
from models_crossvit import CrossAttentionBlock
from util.pos_embed import get_2d_sincos_pos_embed
class SupervisedMAE(nn.Module):
def __init__(self, img_size=384, patch_size=16, in_chans=3,
embed_dim=1024, depth=24, num_heads=16,
decoder_embed_dim=512, decoder_depth=2, decoder_num_heads=16,
mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
super().__init__()
# --------------------------------------------------------------------------
# MAE encoder specifics
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
num_patches = self.patch_embed.num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim), requires_grad=False) # fixed sin-cos embedding
self.blocks = nn.ModuleList([
Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# --------------------------------------------------------------------------
# --------------------------------------------------------------------------
# MAE decoder specifics
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding
self.shot_token = nn.Parameter(torch.zeros(512))
# Exemplar encoder with CNN
self.decoder_proj1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
nn.InstanceNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2) #[3,64,64]->[64,32,32]
)
self.decoder_proj2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.InstanceNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(2) #[64,32,32]->[128,16,16]
)
self.decoder_proj3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.InstanceNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(2) # [128,16,16]->[256,8,8]
)
self.decoder_proj4 = nn.Sequential(
nn.Conv2d(256, decoder_embed_dim, kernel_size=3, stride=1, padding=1),
nn.InstanceNorm2d(512),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1,1))
# [256,8,8]->[512,1,1]
)
self.decoder_blocks = nn.ModuleList([
CrossAttentionBlock(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
for i in range(decoder_depth)])
self.decoder_norm = norm_layer(decoder_embed_dim)
# Density map regresssion module
self.decode_head0 = nn.Sequential(
nn.Conv2d(decoder_embed_dim, 256, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, 256),
nn.ReLU(inplace=True)
)
self.decode_head1 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, 256),
nn.ReLU(inplace=True)
)
self.decode_head2 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, 256),
nn.ReLU(inplace=True)
)
self.decode_head3 = nn.Sequential(
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(8, 256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 1, kernel_size=1, stride=1)
)
# --------------------------------------------------------------------------
self.norm_pix_loss = norm_pix_loss
self.initialize_weights()
def initialize_weights(self):
# initialization
# initialize (and freeze) pos_embed by sin-cos embedding
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=False)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=False)
self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
# initialize patch_embed like nn.Linear (instead of nn.Conv2d)
w = self.patch_embed.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
torch.nn.init.normal_(self.shot_token, std=.02)
# initialize nn.Linear and nn.LayerNorm
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
# we use xavier_uniform following official JAX ViT:
torch.nn.init.xavier_uniform_(m.weight)
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)
def forward_encoder(self, x):
# embed patches
x = self.patch_embed(x)
# add pos embed w/o cls token
x = x + self.pos_embed
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
def forward_decoder(self, x, y_, shot_num=3):
# embed tokens
x = self.decoder_embed(x)
# add pos embed
x = x + self.decoder_pos_embed
# Exemplar encoder
y_ = y_.transpose(0,1) # y_ [N,3,3,64,64]->[3,N,3,64,64]
y1=[]
C=0
N=0
cnt = 0
for yi in y_:
cnt+=1
if cnt > shot_num:
break
yi = self.decoder_proj1(yi)
yi = self.decoder_proj2(yi)
yi = self.decoder_proj3(yi)
yi = self.decoder_proj4(yi)
N, C,_,_ = yi.shape
y1.append(yi.squeeze(-1).squeeze(-1)) # yi [N,C,1,1]->[N,C]
if shot_num > 0:
y = torch.cat(y1,dim=0).reshape(shot_num,N,C).to(x.device)
else:
y = self.shot_token.repeat(y_.shape[1],1).unsqueeze(0).to(x.device)
y = y.transpose(0,1) # y [3,N,C]->[N,3,C]
# apply Transformer blocks
for blk in self.decoder_blocks:
x = blk(x, y)
x = self.decoder_norm(x)
# Density map regression
n, hw, c = x.shape
h = w = int(math.sqrt(hw))
x = x.transpose(1, 2).reshape(n, c, h, w)
x = F.interpolate(
self.decode_head0(x), size=x.shape[-1]*2, mode='bilinear', align_corners=False)
x = F.interpolate(
self.decode_head1(x), size=x.shape[-1]*2, mode='bilinear', align_corners=False)
x = F.interpolate(
self.decode_head2(x), size=x.shape[-1]*2, mode='bilinear', align_corners=False)
x = F.interpolate(
self.decode_head3(x), size=x.shape[-1]*2, mode='bilinear', align_corners=False)
x = x.squeeze(-3)
return x
def forward(self, imgs, boxes, shot_num):
# if boxes.nelement() > 0:
# torchvision.utils.save_image(boxes[0], f"data/out/crops/box_{time.time()}_{random.randint(0, 99999):>5}.png")
with torch.no_grad():
latent = self.forward_encoder(imgs)
pred = self.forward_decoder(latent, boxes, shot_num) # [N, 384, 384]
return pred
def mae_vit_base_patch16_dec512d8b(**kwargs):
model = SupervisedMAE(
patch_size=16, embed_dim=768, depth=12, num_heads=12,
decoder_embed_dim=512, decoder_depth=2, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mae_vit_large_patch16_dec512d8b(**kwargs):
model = SupervisedMAE(
patch_size=16, embed_dim=1024, depth=24, num_heads=16,
decoder_embed_dim=512, decoder_depth=2, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mae_vit_huge_patch14_dec512d8b(**kwargs):
model = SupervisedMAE(
patch_size=14, embed_dim=1280, depth=32, num_heads=16,
decoder_embed_dim=512, decoder_depth=2, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mae_vit_base_patch16_fim4(**kwargs):
model = SupervisedMAE(
patch_size=16, embed_dim=768, depth=12, num_heads=12,
decoder_embed_dim=512, decoder_depth=4, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mae_vit_base_patch16_fim6(**kwargs):
model = SupervisedMAE(
patch_size=16, embed_dim=768, depth=12, num_heads=12,
decoder_embed_dim=512, decoder_depth=6, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
# set recommended archs
mae_vit_base_patch16 = mae_vit_base_patch16_dec512d8b
mae_vit_base4_patch16 = mae_vit_base_patch16_fim4 # decoder: 4 blocks
mae_vit_base6_patch16 = mae_vit_base_patch16_fim6 # decoder: 6 blocks
mae_vit_large_patch16 = mae_vit_large_patch16_dec512d8b
mae_vit_huge_patch14 = mae_vit_huge_patch14_dec512d8b
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