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import os.path |
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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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from timm.models.vision_transformer import Mlp, PatchEmbed , _cfg |
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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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class Attention(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.): |
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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.qkv = nn.Linear(dim, dim * 3, 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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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) |
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q, k, v = qkv[0], qkv[1], qkv[2] |
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q = q * self.scale |
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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 = (attn @ v).transpose(1, 2).reshape(B, N, C) |
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x = self.proj(x) |
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x = self.proj_drop(x) |
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return x |
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class Block(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 = Attention,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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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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def forward(self, x): |
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x = x + self.drop_path(self.attn(self.norm1(x))) |
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x = x + self.drop_path(self.mlp(self.norm2(x))) |
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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, 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 = Attention,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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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): |
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x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) |
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x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) |
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return x |
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class Layer_scale_init_Block_paralx2(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 = Attention,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.norm11 = 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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self.attn1 = 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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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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self.norm21 = 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.mlp1 = 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_1_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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self.gamma_2_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) |
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def forward(self, x): |
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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))) |
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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))) |
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return x |
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class Block_paralx2(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 = Attention,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.norm11 = 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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self.attn1 = 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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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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self.norm21 = 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.mlp1 = Mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) |
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def forward(self, x): |
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x = x + self.drop_path(self.attn(self.norm1(x))) + self.drop_path(self.attn1(self.norm11(x))) |
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x = x + self.drop_path(self.mlp(self.norm2(x))) + self.drop_path(self.mlp1(self.norm21(x))) |
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return x |
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class hMLP_stem(nn.Module): |
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""" hMLP_stem: https://arxiv.org/pdf/2203.09795.pdf |
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taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py |
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with slight modifications |
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""" |
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768,norm_layer=nn.SyncBatchNorm): |
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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(*[nn.Conv2d(in_chans, embed_dim//4, kernel_size=4, stride=4), |
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norm_layer(embed_dim//4), |
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nn.GELU(), |
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nn.Conv2d(embed_dim//4, embed_dim//4, kernel_size=2, stride=2), |
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norm_layer(embed_dim//4), |
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nn.GELU(), |
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nn.Conv2d(embed_dim//4, embed_dim, kernel_size=2, stride=2), |
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norm_layer(embed_dim), |
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]) |
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def forward(self, x): |
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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 vit_models(nn.Module): |
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""" Vision Transformer with LayerScale (https://arxiv.org/abs/2103.17239) support |
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taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py |
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with slight modifications |
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""" |
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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=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., |
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drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None, |
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block_layers = Block, |
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Patch_layer=PatchEmbed,act_layer=nn.GELU, |
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Attention_block = Attention, Mlp_block=Mlp, |
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dpr_constant=True,init_scale=1e-4, |
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mlp_ratio_clstk = 4.0): |
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super().__init__() |
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self.dropout_rate = drop_rate |
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self.depth = depth |
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self.num_classes = num_classes |
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self.num_features = self.embed_dim = embed_dim |
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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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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) |
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) |
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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, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, |
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drop=0.0, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, |
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act_layer=act_layer,Attention_block=Attention_block,Mlp_block=Mlp_block,init_values=init_scale) |
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for i in range(depth)]) |
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self.norm = norm_layer(embed_dim) |
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self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')] |
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() |
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trunc_normal_(self.pos_embed, std=.02) |
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trunc_normal_(self.cls_token, std=.02) |
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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=.02) |
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if isinstance(m, nn.Linear) and 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 {'pos_embed', '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 extract_block_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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x = x + self.pos_embed |
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x = torch.cat((cls_tokens, x), dim=1) |
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outs = {} |
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for i, blk in enumerate(self.blocks): |
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x = blk(x) |
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outs[i] = x.detach() |
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return outs |
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def selective_forward(self, x, begin, end): |
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for i, blk in enumerate(self.blocks): |
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if i < begin: |
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continue |
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if i > end: |
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break |
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x = blk(x) |
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return x |
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def forward_until(self, x, blk_id): |
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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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x = x + self.pos_embed |
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x = torch.cat((cls_tokens, x), dim=1) |
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for i, blk in enumerate(self.blocks): |
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x = blk(x) |
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if i == blk_id: |
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break |
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return x |
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def forward_from(self, x, blk_id): |
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for i, blk in enumerate(self.blocks): |
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if i < blk_id: |
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continue |
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x = blk(x) |
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x = self.norm(x) |
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x = self.head(x[:, 0]) |
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return x |
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def forward_patch_embed(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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x = x + self.pos_embed |
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x = torch.cat((cls_tokens, x), dim=1) |
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return x |
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def forward_norm_head(self, x): |
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x = self.norm(x) |
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x = self.head(x[:, 0]) |
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return x |
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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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x = x + self.pos_embed |
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x = torch.cat((cls_tokens, x), dim=1) |
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for i , blk in enumerate(self.blocks): |
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x = blk(x) |
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x = self.norm(x) |
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return x[:, 0] |
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def forward(self, x): |
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x = self.forward_features(x) |
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if self.dropout_rate: |
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x = F.dropout(x, p=float(self.dropout_rate), training=self.training) |
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x = self.head(x) |
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return x |
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@register_model |
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def deit_tiny_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg_overlay=None, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_small_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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model.default_cfg = _cfg() |
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if pretrained: |
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checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_small_224_21k.pth')) |
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model.load_state_dict(checkpoint["model"]) |
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return model |
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@register_model |
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def deit_medium_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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patch_size=16, embed_dim=512, depth=12, num_heads=8, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) |
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model.default_cfg = _cfg() |
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if pretrained: |
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name = 'https://dl.fbaipublicfiles.com/deit/deit_3_medium_'+str(img_size)+'_' |
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if pretrained_21k: |
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name+='21k.pth' |
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else: |
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name+='1k.pth' |
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checkpoint = torch.hub.load_state_dict_from_url( |
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url=name, |
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map_location="cpu", check_hash=True |
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) |
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model.load_state_dict(checkpoint["model"]) |
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return model |
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@register_model |
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def deit_base_patch16_LS(pretrained=False, pretrained_cfg=None, img_size=224, pretrained_21k = False, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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if pretrained: |
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checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_base_224_21k.pth')) |
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model.load_state_dict(checkpoint["model"]) |
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return model |
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@register_model |
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def deit_large_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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if pretrained: |
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checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_large_224_21k.pth')) |
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model.load_state_dict(checkpoint["model"]) |
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return model |
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@register_model |
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def deit_huge_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) |
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if pretrained: |
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name = 'https://dl.fbaipublicfiles.com/deit/deit_3_huge_'+str(img_size)+'_' |
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if pretrained_21k: |
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name+='21k_v1.pth' |
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else: |
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name+='1k_v1.pth' |
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checkpoint = torch.hub.load_state_dict_from_url( |
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url=name, |
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map_location="cpu", check_hash=True |
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) |
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model.load_state_dict(checkpoint["model"]) |
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return model |
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@register_model |
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def deit_huge_patch14_52_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1280, depth=52, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_huge_patch14_26x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1280, depth=26, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block_paralx2, **kwargs) |
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return model |
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@register_model |
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def deit_Giant_48x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs) |
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return model |
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@register_model |
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def deit_giant_40x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs) |
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return model |
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@register_model |
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def deit_Giant_48_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_giant_40_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_small_patch16_36_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_small_patch16_36(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return model |
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@register_model |
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def deit_small_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs) |
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return model |
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@register_model |
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def deit_small_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs) |
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return model |
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@register_model |
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def deit_base_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs) |
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return model |
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@register_model |
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def deit_base_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs) |
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return model |
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@register_model |
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def deit_base_patch16_36x1_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) |
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return model |
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@register_model |
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def deit_base_patch16_36x1(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): |
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model = vit_models( |
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img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True, |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return model |
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