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wgnbase-Copy1.py
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wgnbase-Copy1.py
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import torch
from torch import nn
from torchvision import models
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from timm.models import create_model
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
# class ResModel(nn.Module):
# def __init__(self):
# super(ResModel, self).__init__()
# self.premodel = models.resnet18(pretrained=True)
# self.model = nn.Sequential(*list(self.premodel.children())[:-1]) ###[:-2]
# out_chann = 512 ### 2048x4x4
# print ("resnet18 model loaded")
# self.compress = nn.Linear(out_chann, 768, bias=True) ## False
# def forward(self, x_input):
# x = self.model(x_input) ##### 25 512 7 7
# # print ('000:',x.shape)
# x_comp = self.compress(x) ### 25 2048
# return x_comp
# class vitembeding(nn.Module):
# def __init__(self):
# super(vitembeding, self).__init__()
# self.original_model = create_model(
# 'vit_base_patch16_224',
# pretrained=True,
# num_classes=1000,
# drop_rate=0.0,
# drop_path_rate=0.0,
# drop_block_rate=None,
# )
# def forward(self, x_input):
# for p in self.original_model.parameters():
# p.requires_grad = False
# x = self.original_model.forward_features(x_input)[:,0,:]
# return x
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.attend = nn.Softmax(dim = -1)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
out = torch.matmul(attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
class WGNbase(nn.Module):
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
super().__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
self.num_classes = num_classes
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width), ### 1,64,1024x3
nn.Linear(patch_dim, dim), ### 1,64,1024
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) ### 1,65,1024
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(emb_dropout)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
# self.input_embedding = vitembeding() ### ResModel()
self.pool = pool
self.to_latent = nn.Identity()
self.mlp_head = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, num_classes)
)
##########################
self.acti_softmax = nn.Softmax(dim=-1)
self.acti_Sig = nn.Sigmoid()
self.pre_out = nn.Linear(768, self.num_classes)
def l2_normalize(self, x, dim=None, epsilon=1e-12):
"""Normalizes a given vector or matrix."""
square_sum = torch.sum(x ** 2, dim=dim, keepdim=True)
# print('square_sum shape:',square_sum.shape) ### [10,1] for prompt [16,1] for x_embed_mean
x_inv_norm = torch.rsqrt(torch.maximum(square_sum, torch.tensor(epsilon, device=x.device)))
return x * x_inv_norm
def trans_norm(self,x,dim=None):
ttt_mean = torch.mean(x, dim=dim)
ttt_var = torch.var(x, dim=dim)
# # # print('ttmax:',ttt_max.shape)
ttt_mean = repeat(ttt_mean, '... -> ... n', n = x.shape[dim])
ttt_var = repeat(ttt_var, '... -> ... n', n = x.shape[dim])
return (x-ttt_mean)/ttt_var
def line_norm(self,x,dim=None):
ttt_max, ttindex_max = torch.max(x, dim=dim)
ttt_min, ttindex_min = torch.min(x, dim=dim)
# # # print('ttmax:',ttt_max.shape)
ttt_max = repeat(ttt_max, '... -> ... n', n = x.shape[dim])
ttt_min = repeat(ttt_min, '... -> ... n', n = x.shape[dim])
return (x-ttt_min)/(ttt_max- ttt_min)
# def forward(self, embedding, prompt):
# # x = self.to_patch_embedding(img) ### 1,64 ,1024
# # with torch.no_grad():
# # x = self.input_embedding(img)
# x_embed = embedding.unsqueeze(1)
# b,_,wh = x_embed.shape
# x_embed_norm_l = self.trans_norm(x_embed, dim=-1)
# prompt_norm_l = self.trans_norm(prompt, dim=-1)
# x = torch.cat((x_embed_norm_l, prompt_norm_l), dim=1)
# # cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) ## 1,1,1024
# # x = torch.cat((cls_tokens, x), dim=1) ###### 1,65 ,1024
# # x += self.pos_embedding[:, :(n + 1)] ## 1,65,1024
# # x = self.dropout(x)
# x = self.transformer(x)
# x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
# x = self.to_latent(x)
# weight_pre = self.pre_out(x)
# weight_pre = self.acti_Sig(weight_pre)
# ttt_max, ttindex_max = torch.max(weight_pre, dim=-1)
# ttt_max_B = repeat(ttt_max, 'b -> b n', n =self.num_classes)
# # weight_pre = self.acti_Sig(weight_pre)
# weight_pre_ = weight_pre/ttt_max_B
# weight_pre_Si = repeat(weight_pre_, 'b n -> b n d', d = 768)
# weight_prompt= prompt*weight_pre_Si
# return weight_prompt
def forward(self, input_embed ,prompt_embed):
x_embed = input_embed.unsqueeze(1)
b,_,wh = x_embed.shape
key = self.key_w(x_embed)
# key = x_embed
# print(key.size())
# query = self.query_w(prompt_embed)
query = prompt_embed
# print(query.size())
Q = query
K = torch.transpose(key, 1, 2)
V = prompt_embed
scores = torch.matmul(Q, K)
scores = (scores / math.sqrt(self.head_size)).squeeze()
probs = torch.sigmoid(scores)
# ttt_max, ttindex_max = torch.max(probs, dim=-1)
# ttt_max_B = repeat(ttt_max, 'b -> b n', n =25)
# probs_ = probs/ttt_max_B
probs = repeat(probs, 'b n -> b n d', d = 768)
prompt_embedding = torch.mul(V, probs)
return prompt_embedding