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from transformers import PreTrainedModel
import torch
from torch import nn
import os
from einops import rearrange,repeat
from einops.layers.torch import Rearrange
import numpy as np
import torch.nn.functional as F
class shift_patch(nn.Module):
def __init__(self,patch_size=16):
super().__init__()
self.patch_size = patch_size // 2
def forward(self, x):
bs,_, _, chanel = x.size()
shifts = ((-self.patch_size, self.patch_size, -self.patch_size, self.patch_size),
(self.patch_size, -self.patch_size, -self.patch_size, self.patch_size),
(-self.patch_size, self.patch_size, self.patch_size, -self.patch_size),
(self.patch_size, -self.patch_size, self.patch_size, -self.patch_size))
shifted_x = list(map(lambda shift: F.pad(x, shift), shifts))
return shifted_x
def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
assert (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
omega = torch.arange(dim // 4) / (dim // 4 - 1)
omega = 1.0 / (temperature ** omega)
y = y.flatten()[:, None] * omega[None, :]
x = x.flatten()[:, None] * omega[None, :]
pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
return pe.type(dtype)
class patch_embedding(nn.Module):
def __init__(self,height=224,width=224,n_channels=3,patch_size=16,batch_size=2,dim=512):
super().__init__()
assert height%patch_size==0 and width%patch_size==0 ,"Height and Width should be multiples of patch size wich is {0}".format(patch_size)
#self.class_token = nn.Parameter(torch.randn(1, 1, dim))
self.pos_embedding = posemb_sincos_2d(
h = height // patch_size,
w = width // patch_size,
dim = dim,
)
self.patch_size = patch_size
self.n_patchs = height*width//(patch_size**2)
#self.embedding = torch.nn.Parameter(torch.randn(1,self.n_patchs+1, dim))
self.projection = nn.Sequential(nn.LayerNorm(patch_size*patch_size*n_channels*5),
nn.Linear(patch_size*patch_size*n_channels*5,dim),
nn.LayerNorm(dim)
)
self.shift = shift_patch(patch_size=patch_size)
self.patch = nn.Sequential(
Rearrange("b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1 = patch_size, p2 = patch_size),
)
def forward(self, x):
#x bs,h,w,c
embedding = self.pos_embedding.to(x.device)
left_up,right_up,left_down,right_down = self.shift(x)
x = torch.cat([x,left_up,right_up,left_down,right_down],dim=1)
#first we resize the inputs Bs,Num_patchs,*
x = self.patch(x)
#projection on the dim of model
x = self.projection(x)
outputs = x + embedding
return outputs
class LocallySelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads):
super(LocallySelfAttention, self).__init__()
assert embed_dim % num_heads == 0, "Embedding dimension must be 0 modulo number of heads."
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.norm = nn.LayerNorm(embed_dim)
self.tau = nn.Parameter(torch.sqrt(torch.tensor(self.head_dim)))
self.qkv_proj = nn.Linear(embed_dim, 3*embed_dim,bias = False)
self.o_proj = nn.Linear(embed_dim, embed_dim)
self._reset_parameters()
def _reset_parameters(self):
# Original Transformer initialization, see PyTorch documentation
nn.init.xavier_uniform_(self.qkv_proj.weight)
nn.init.xavier_uniform_(self.o_proj.weight)
self.o_proj.bias.data.fill_(0)
def _masked_softmax(self, attention_scores, attention_mask):
if attention_mask is not None:
attention_scores = attention_scores.masked_fill(attention_mask, float('-inf'))
return F.softmax(attention_scores, dim=-1)
def forward(self,x, need_weights=True, attn_mask=None):
x = self.norm(x)
batch_size, seq_length, _ = x.size()
qkv = self.qkv_proj(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.num_heads), qkv)
attention_scores = torch.matmul(q, k.transpose(-1, -2)) / self.tau
attention_scores = self._masked_softmax(attention_scores, attn_mask)
attention_output = torch.matmul(attention_scores, v)
attention_output = rearrange(attention_output, 'b h n d -> b n (h d)')
return self.o_proj(attention_output)
class feedforward(nn.Module):
def __init__(self,embed_dim=512,ff_hidden_dim=1024,dropout_rate = 0.1):
super().__init__()
self.mlp = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, ff_hidden_dim),
nn.GELU(),
nn.Dropout(p=dropout_rate),
nn.Linear(ff_hidden_dim, embed_dim),
nn.GELU(),
nn.Dropout(p=dropout_rate),
)
def forward(self, x):
outputs = self.mlp(x)
return outputs
class Transformer(nn.Module):
def __init__(self, embed_dim=512, depth=4, heads=2, ff_hidden_dim=1024, dropout_rate = 0.1):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
LocallySelfAttention(embed_dim,heads),
feedforward(embed_dim,ff_hidden_dim, dropout_rate)
]))
def forward(self, x,mask=None):
for attn, ff in self.layers:
x = attn(x,attn_mask = mask) + x
x = ff(x) + x
return x
class vit(nn.Module):
def __init__(self,height=224,width=224,n_channels=3,patch_size=16,batch_size=2,dim=512,n_head=2,feed_forward=1024,num_blocks=4,num_classes=1):
super().__init__()
self.embedding = patch_embedding(height,width,n_channels,patch_size,batch_size,dim)
self.n_patchs = height*width//(patch_size**2)
# Create a diagonal attention mask
self.diag_attn_mask = torch.eye(self.n_patchs, dtype=torch.bool)
self.transformer_encoder = Transformer(dim,num_blocks,n_head,feed_forward)
self.mlp_block = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, num_classes),
)
def forward(self, inputs):
#x bs,h,w,c
device_ = inputs.device
x = self.embedding(inputs)
x = self.transformer_encoder(x,mask=self.diag_attn_mask.to(device_))
outputs = self.mlp_block(x.mean(dim=1))
return outputs
class VITModelForImageClassification(PreTrainedModel):
config_class = VITConfig
def __init__(self, config : VITConfig):
super().__init__(config)
self.model = vit(config.height,config.width,
config.n_channels,config.patch_size,
config.batch_size,config.dim,
config.n_head,config.feed_forward,
config.num_blocks,config.num_classes)
def forward(self, img,labels = None):
logits = self.model(img)
if labels is not None:
loss_fnc = nn.CrossEntropyLoss()
loss = loss_fnc(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
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