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models_v4.py
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models_v4.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial
import math
from timm.models.vision_transformer import Mlp, PatchEmbed , _cfg
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
import torch.nn.functional as F
class Attention(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, lambda_v=1):
# x = x + self.drop_path(self.attn(self.norm1(x))) # origin
x = (2 - lambda_v) * x + lambda_v * self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x))) # origin
# x = (2 - lambda_v) * x + lambda_v * self.drop_path(self.mlp(self.norm2(x)))
return x
class Layer_scale_init_Block(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
class Layer_scale_init_Block_paralx2(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.norm11 = norm_layer(dim)
self.attn = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.attn1 = Attention_block(
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)
self.norm21 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.mlp1 = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_1_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
self.gamma_2_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.gamma_1*self.attn(self.norm1(x))) + self.drop_path(self.gamma_1_1 * self.attn1(self.norm11(x)))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + self.drop_path(self.gamma_2_1 * self.mlp1(self.norm21(x)))
return x
class Block_paralx2(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,Attention_block = Attention,Mlp_block=Mlp
,init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.norm11 = norm_layer(dim)
self.attn = Attention_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.attn1 = Attention_block(
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)
self.norm21 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.mlp1 = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x))) + self.drop_path(self.attn1(self.norm11(x)))
x = x + self.drop_path(self.mlp(self.norm2(x))) + self.drop_path(self.mlp1(self.norm21(x)))
return x
class hMLP_stem(nn.Module):
""" hMLP_stem: https://arxiv.org/pdf/2203.09795.pdf
taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
with slight modifications
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768,norm_layer=nn.SyncBatchNorm):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = torch.nn.Sequential(*[nn.Conv2d(in_chans, embed_dim//4, kernel_size=4, stride=4),
norm_layer(embed_dim//4),
nn.GELU(),
nn.Conv2d(embed_dim//4, embed_dim//4, kernel_size=2, stride=2),
norm_layer(embed_dim//4),
nn.GELU(),
nn.Conv2d(embed_dim//4, embed_dim, kernel_size=2, stride=2),
norm_layer(embed_dim),
])
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class vit_models(nn.Module):
""" Vision Transformer with LayerScale (https://arxiv.org/abs/2103.17239) support
taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
with slight modifications
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None,
block_layers = Block,
Patch_layer=PatchEmbed,act_layer=nn.GELU,
Attention_block = Attention, Mlp_block=Mlp,
dpr_constant=True,init_scale=1e-4,
mlp_ratio_clstk = 4.0,**kwargs):
super().__init__()
self.dropout_rate = drop_rate
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.patch_embed = Patch_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
#self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches+1, embed_dim)) # align timm
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
block_layers(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=0.0, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
act_layer=act_layer,Attention_block=Attention_block,Mlp_block=Mlp_block,init_values=init_scale)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
self.apply(self._init_weights)
# lambda: the momentum value
self.lambda_v = None
self.prune_layer = kwargs['prune_layer']
self.done_layer = kwargs['done_layer']
self.frozen_stages = kwargs['frozen_stages']
self._freeze_stages()
print(f'the pruning layer is {self.prune_layer}, the pruned layer is {self.done_layer}, frozen_stages is {self.frozen_stages}')
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)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def get_num_layers(self):
return len(self.blocks)
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x, lambda_v=1):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
#x = x + self.pos_embed
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed # align timm
for i , blk in enumerate(self.blocks):
#if i in [1, 2, 3, 4]:
if i in self.prune_layer:
#print(i)
x = blk(x,lambda_v)
elif i in self.done_layer:
x = blk(x,lambda_v=0)
else:
x = blk(x,lambda_v=1)
x = self.norm(x)
return x[:, 0]
def forward(self, x, epoch=0):
start_decay = 0
if epoch <=start_decay:
magic_lambda = 1.0
else:
magic_lambda = 1.0 - ( (epoch-start_decay) / 299)
magic_lambda = 0 if magic_lambda < 0 else magic_lambda
if self.lambda_v == None or self.lambda_v !=magic_lambda:
print(f'Magic lambda is {magic_lambda}')
self.lambda_v = magic_lambda
x = self.forward_features(x,self.lambda_v)
if self.dropout_rate:
x = F.dropout(x, p=float(self.dropout_rate), training=self.training)
x = self.head(x)
return x
def _freeze_stages(self):
"""Freeze stages param and norm stats."""
if self.frozen_stages >= 0:
self.cls_token.requires_grad = False
self.pos_embed.requires_grad = False
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
for i in range(0,self.frozen_stages+1):
self.blocks[i].eval()
for p in self.blocks[i].parameters():
p.requires_grad = False
#--------------------------- our models are hear ------------------
@register_model
def deit_base_patch16_224_attn(pretrained=False, img_size=224, pretrained_21k = False, **kwargs):
model = vit_models(
img_size = img_size, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
if pretrained:
name = 'https://dl.fbaipublicfiles.com/deit/deit_3_base_'+str(img_size)+'_'
if pretrained_21k:
name+='21k.pth'
else:
name+='1k.pth'
checkpoint = torch.hub.load_state_dict_from_url(
url=name,
map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
return model