deit/patchconvnet_models.py

546 lines
17 KiB
Python

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
from functools import partial
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.efficientnet_blocks import SqueezeExcite
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
__all__ = ['S60', 'S120', 'B60', 'B120', 'L60', 'L120', 'S60_multi']
class Mlp(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
act_layer: nn.Module = nn.GELU,
drop: float = 0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Learned_Aggregation_Layer(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 1,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
):
super().__init__()
self.num_heads = num_heads
head_dim: int = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim**-0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.k = nn.Linear(dim, dim, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.id = nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
q = q * self.scale
v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
attn = q @ k.transpose(-2, -1)
attn = self.id(attn)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
x_cls = self.proj(x_cls)
x_cls = self.proj_drop(x_cls)
return x_cls
class Learned_Aggregation_Layer_multi(nn.Module):
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_drop: float = 0.0,
proj_drop: float = 0.0,
num_classes: int = 1000,
):
super().__init__()
self.num_heads = num_heads
head_dim: int = dim // num_heads
self.scale = qk_scale or head_dim**-0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.k = nn.Linear(dim, dim, bias=qkv_bias)
self.v = nn.Linear(dim, dim, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.num_classes = num_classes
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
q = (
self.q(x[:, : self.num_classes])
.reshape(B, self.num_classes, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
k = (
self.k(x[:, self.num_classes:])
.reshape(B, N - self.num_classes, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
q = q * self.scale
v = (
self.v(x[:, self.num_classes:])
.reshape(B, N - self.num_classes, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x_cls = (attn @ v).transpose(1, 2).reshape(B, self.num_classes, C)
x_cls = self.proj(x_cls)
x_cls = self.proj_drop(x_cls)
return x_cls
class Layer_scale_init_Block_only_token(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.0,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
drop: float = 0.0,
attn_drop: float = 0.0,
drop_path: float = 0.0,
act_layer: nn.Module = nn.GELU,
norm_layer=nn.LayerNorm,
Attention_block=Learned_Aggregation_Layer,
Mlp_block=Mlp,
init_values: float = 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 > 0.0 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: torch.Tensor, x_cls: torch.Tensor) -> torch.Tensor:
u = torch.cat((x_cls, x), dim=1)
x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
return x_cls
class Conv_blocks_se(nn.Module):
def __init__(self, dim: int):
super().__init__()
self.qkv_pos = nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=1),
nn.GELU(),
nn.Conv2d(dim, dim, groups=dim, kernel_size=3, padding=1, stride=1, bias=True),
nn.GELU(),
SqueezeExcite(dim, rd_ratio=0.25),
nn.Conv2d(dim, dim, kernel_size=1),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
H = W = int(N ** 0.5)
x = x.transpose(-1, -2)
x = x.reshape(B, C, H, W)
x = self.qkv_pos(x)
x = x.reshape(B, C, N)
x = x.transpose(-1, -2)
return x
class Layer_scale_init_Block(nn.Module):
def __init__(
self,
dim: int,
drop_path: float = 0.0,
act_layer: nn.Module = nn.GELU,
norm_layer=nn.LayerNorm,
Attention_block=None,
init_values: float = 1e-4,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention_block(dim)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Sequential:
"""3x3 convolution with padding"""
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
)
class ConvStem(nn.Module):
"""Image to Patch Embedding"""
def __init__(self, img_size: int = 224, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768):
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 = nn.Sequential(
conv3x3(in_chans, embed_dim // 8, 2),
nn.GELU(),
conv3x3(embed_dim // 8, embed_dim // 4, 2),
nn.GELU(),
conv3x3(embed_dim // 4, embed_dim // 2, 2),
nn.GELU(),
conv3x3(embed_dim // 2, embed_dim, 2),
)
def forward(self, x: torch.Tensor, padding_size: Optional[int] = None) -> torch.Tensor:
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class PatchConvnet(nn.Module):
def __init__(
self,
img_size: int = 224,
patch_size: int = 16,
in_chans: int = 3,
num_classes: int = 1000,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 1,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
hybrid_backbone: Optional = None,
norm_layer=nn.LayerNorm,
global_pool: Optional[str] = None,
block_layers=Layer_scale_init_Block,
block_layers_token=Layer_scale_init_Block_only_token,
Patch_layer=ConvStem,
act_layer: nn.Module = nn.GELU,
Attention_block=Conv_blocks_se,
dpr_constant: bool = True,
init_scale: float = 1e-4,
Attention_block_token_only=Learned_Aggregation_Layer,
Mlp_block_token_only=Mlp,
depth_token_only: int = 1,
mlp_ratio_clstk: float = 3.0,
multiclass: bool = False,
):
super().__init__()
self.multiclass = multiclass
self.patch_size = patch_size
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.patch_embed = Patch_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim
)
if not self.multiclass:
self.cls_token = nn.Parameter(torch.zeros(1, 1, int(embed_dim)))
else:
self.cls_token = nn.Parameter(torch.zeros(1, num_classes, int(embed_dim)))
if not dpr_constant:
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
else:
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList(
[
block_layers(
dim=embed_dim,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
Attention_block=Attention_block,
init_values=init_scale,
)
for i in range(depth)
]
)
self.blocks_token_only = nn.ModuleList(
[
block_layers_token(
dim=int(embed_dim),
num_heads=num_heads,
mlp_ratio=mlp_ratio_clstk,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=0.0,
norm_layer=norm_layer,
act_layer=act_layer,
Attention_block=Attention_block_token_only,
Mlp_block=Mlp_block_token_only,
init_values=init_scale,
)
for i in range(depth_token_only)
]
)
self.norm = norm_layer(int(embed_dim))
self.total_len = depth_token_only + depth
self.feature_info = [dict(num_chs=int(embed_dim), reduction=0, module='head')]
if not self.multiclass:
self.head = nn.Linear(int(embed_dim), num_classes) if num_classes > 0 else nn.Identity()
else:
self.head = nn.ModuleList([nn.Linear(int(embed_dim), 1) for _ in range(num_classes)])
self.rescale: float = 0.02
trunc_normal_(self.cls_token, std=self.rescale)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=self.rescale)
if 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 {'cls_token'}
def get_classifier(self):
return self.head
def get_num_layers(self):
return len(self.blocks)
def reset_classifier(self, num_classes: int, global_pool: str = ''):
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: torch.Tensor) -> torch.Tensor:
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
for i, blk in enumerate(self.blocks):
x = blk(x)
for i, blk in enumerate(self.blocks_token_only):
cls_tokens = blk(x, cls_tokens)
x = torch.cat((cls_tokens, x), dim=1)
x = self.norm(x)
if not self.multiclass:
return x[:, 0]
else:
return x[:, : self.num_classes].reshape(B, self.num_classes, -1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B = x.shape[0]
x = self.forward_features(x)
if not self.multiclass:
x = self.head(x)
return x
else:
all_results = []
for i in range(self.num_classes):
all_results.append(self.head[i](x[:, i]))
return torch.cat(all_results, dim=1).reshape(B, self.num_classes)
@register_model
def S60(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=384,
depth=60,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
depth_token_only=1,
mlp_ratio_clstk=3.0,
**kwargs
)
return model
@register_model
def S120(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=384,
depth=120,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
init_scale=1e-6,
mlp_ratio_clstk=3.0,
**kwargs
)
return model
@register_model
def B60(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=768,
depth=60,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Attention_block=Conv_blocks_se,
init_scale=1e-6,
**kwargs
)
return model
@register_model
def B120(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=768,
depth=120,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
init_scale=1e-6,
**kwargs
)
return model
@register_model
def L60(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=1024,
depth=60,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
init_scale=1e-6,
mlp_ratio_clstk=3.0,
**kwargs
)
return model
@register_model
def L120(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=1024,
depth=120,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
init_scale=1e-6,
mlp_ratio_clstk=3.0,
**kwargs
)
return model
@register_model
def S60_multi(pretrained: bool = False, **kwargs):
model = PatchConvnet(
patch_size=16,
embed_dim=384,
depth=60,
num_heads=1,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
Patch_layer=ConvStem,
Attention_block=Conv_blocks_se,
Attention_block_token_only=Learned_Aggregation_Layer_multi,
depth_token_only=1,
mlp_ratio_clstk=3.0,
multiclass=True,
**kwargs
)
return model