mirror of https://github.com/facebookresearch/deit
419 lines
15 KiB
Python
419 lines
15 KiB
Python
# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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import torch
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import torch.nn as nn
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from functools import partial
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import torch.nn.functional as F
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from timm.models.registry import register_model
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from timm.models.efficientnet_blocks import SqueezeExcite
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__all__ = [
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'S60','S120',
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'B60','B120',
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'L60','L120','S60_multi'
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]
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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class Learned_Aggregation_Layer(nn.Module):
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def __init__(self, dim, num_heads=1, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.k = nn.Linear(dim, dim, bias=qkv_bias)
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self.v = nn.Linear(dim, dim, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x ):
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B, N, C = x.shape
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q = self.q(x[:,0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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q = q * self.scale
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v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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attn = (q @ k.transpose(-2, -1))
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
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x_cls = self.proj(x_cls)
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x_cls = self.proj_drop(x_cls)
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return x_cls
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class Learned_Aggregation_Layer_multi(nn.Module):
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,num_classes=1000):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.k = nn.Linear(dim, dim, bias=qkv_bias)
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self.v = nn.Linear(dim, dim, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.num_classes =num_classes
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def forward(self, x ):
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B, N, C = x.shape
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q = self.q(x[:,:self.num_classes]).reshape(B, self.num_classes, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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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)
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q = q * self.scale
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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)
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attn = (q @ k.transpose(-2, -1))
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x_cls = (attn @ v).transpose(1, 2).reshape(B, self.num_classes, C)
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x_cls = self.proj(x_cls)
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x_cls = self.proj_drop(x_cls)
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return x_cls
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class Layer_scale_init_Block_only_token(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, Attention_block = Learned_Aggregation_Layer, Mlp_block=Mlp,
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init_values=1e-4):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention_block(
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
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self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
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def forward(self, x, x_cls):
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u = torch.cat((x_cls,x),dim=1)
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x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
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x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
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return x_cls
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class Conv_blocks_se(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.qkv_pos = nn.Sequential(
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nn.Conv2d(dim, dim, kernel_size = 1),
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nn.GELU(),
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nn.Conv2d(dim, dim, groups = dim, kernel_size = 3, padding = 1, stride = 1, bias = True),
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nn.GELU(),
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SqueezeExcite(dim, rd_ratio=0.25),
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nn.Conv2d(dim, dim, kernel_size=1),
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)
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def forward(self, x):
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B, N, C = x.shape
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H = W = int(N**0.5)
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x = x.transpose(-1,-2)
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x = x.reshape(B,C,H,W)
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x = self.qkv_pos(x)
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x = x.reshape(B,C,N)
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x = x.transpose(-1,-2)
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return x
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class Layer_scale_init_Block(nn.Module):
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def __init__(self, dim,drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm,Attention_block = None,init_values=1e-4):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention_block(dim)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
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def forward(self, x):
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return x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return torch.nn.Sequential(
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nn.Conv2d(
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in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
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),
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)
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class ConvStem(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
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self.img_size = img_size
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self.patch_size = patch_size
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self.num_patches = num_patches
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self.proj = torch.nn.Sequential(
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conv3x3(in_chans, embed_dim // 8, 2),
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nn.GELU(),
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conv3x3(embed_dim // 8, embed_dim // 4, 2),
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nn.GELU(),
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conv3x3(embed_dim // 4, embed_dim // 2, 2),
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nn.GELU(),
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conv3x3(embed_dim // 2, embed_dim, 2),
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)
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def forward(self, x, padding_size=None):
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B, C, H, W = x.shape
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x = self.proj(x).flatten(2).transpose(1, 2)
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return x
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class PatchConvnet(nn.Module):
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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num_heads=1, qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
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drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
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block_layers = Layer_scale_init_Block,
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block_layers_token = Layer_scale_init_Block_only_token,
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Patch_layer=ConvStem,act_layer=nn.GELU,
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Attention_block = Conv_blocks_se ,
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dpr_constant=True,init_scale=1e-4,
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Attention_block_token_only=Learned_Aggregation_Layer,
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Mlp_block_token_only= Mlp,
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depth_token_only=1,
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mlp_ratio_clstk = 3.0,
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multiclass=False):
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super().__init__()
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self.multiclass = multiclass
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self.patch_size = patch_size
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self.num_classes = num_classes
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self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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self.patch_embed = Patch_layer(
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.patch_embed.num_patches
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if not self.multiclass:
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self.cls_token = nn.Parameter(torch.zeros(1, 1, int(embed_dim)))
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else:
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self.cls_token = nn.Parameter(torch.zeros(1, num_classes, int(embed_dim)))
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if not dpr_constant:
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
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else:
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dpr = [drop_path_rate for i in range(depth)]
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self.blocks = nn.ModuleList([
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block_layers(
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dim=embed_dim, drop_path=dpr[i], norm_layer=norm_layer,
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act_layer=act_layer,Attention_block=Attention_block,init_values=init_scale)
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for i in range(depth)])
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self.blocks_token_only = nn.ModuleList([
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block_layers_token(
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dim=int(embed_dim), num_heads=num_heads, mlp_ratio=mlp_ratio_clstk,
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qkv_bias=qkv_bias, qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=0.0, norm_layer=norm_layer,
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act_layer=act_layer,Attention_block=Attention_block_token_only,
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Mlp_block=Mlp_block_token_only,init_values=init_scale)
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for i in range(depth_token_only)])
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self.norm = norm_layer(int(embed_dim))
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self.total_len = depth_token_only+depth
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self.feature_info = [dict(num_chs=int(embed_dim ), reduction=0, module='head')]
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if not self.multiclass:
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self.head = nn.Linear(int(embed_dim), num_classes) if num_classes > 0 else nn.Identity()
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else:
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self.head = nn.ModuleList([nn.Linear(int(embed_dim), 1) for _ in range(num_classes)])
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self.rescale = .02
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trunc_normal_(self.cls_token, std=self.rescale)
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=self.rescale)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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nn.init.constant_(m.bias, 0)
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nn.init.constant_(m.weight, 1.0)
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@torch.jit.ignore
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def no_weight_decay(self):
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return {'cls_token'}
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def get_classifier(self):
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return self.head
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def get_num_layers(self):
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return len(self.blocks)
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def reset_classifier(self, num_classes, global_pool=''):
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self.num_classes = num_classes
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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def forward_features(self, x):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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for i , blk in enumerate(self.blocks):
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x = blk(x)
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for i , blk in enumerate(self.blocks_token_only):
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cls_tokens = blk(x,cls_tokens)
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x = torch.cat((cls_tokens, x), dim=1)
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x = self.norm(x)
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if not self.multiclass:
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return x[:, 0]
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else:
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return x[:, :self.num_classes].reshape(B,self.num_classes,-1)
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def forward(self, x):
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B = x.shape[0]
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x = self.forward_features(x)
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if not self.multiclass:
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x = self.head(x)
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return x
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else:
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all_results = []
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for i in range(self.num_classes):
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all_results.append(self.head[i](x[:,i]))
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return torch.cat(all_results,dim=1).reshape(B,self.num_classes)
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@register_model
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def S60(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=384, depth=60, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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depth_token_only=1,
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mlp_ratio_clstk=3.0,**kwargs)
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return model
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@register_model
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def S120(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=384, depth=120, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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init_scale=1e-6,
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mlp_ratio_clstk=3.0,**kwargs)
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return model
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@register_model
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def B60(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=768, depth=60, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Attention_block = Conv_blocks_se,
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init_scale=1e-6,**kwargs)
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return model
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@register_model
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def B120(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=768, depth=120, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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init_scale=1e-6,**kwargs)
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return model
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@register_model
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def L60(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=1024, depth=60, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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init_scale=1e-6,
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mlp_ratio_clstk=3.0,**kwargs)
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return model
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@register_model
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def L120(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=1024, depth=120, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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init_scale=1e-6,
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mlp_ratio_clstk=3.0,**kwargs)
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return model
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@register_model
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def S60_multi(pretrained=False, **kwargs):
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model = PatchConvnet(
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patch_size=16, embed_dim=384, depth=60, num_heads=1, qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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Patch_layer=ConvStem,
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Attention_block = Conv_blocks_se,
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Attention_block_token_only = Learned_Aggregation_Layer_multi,
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depth_token_only=1,
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mlp_ratio_clstk=3.0,
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multiclass=True,**kwargs)
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return model
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