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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| import os.path | |
| import torch | |
| import torch.nn as nn | |
| from functools import partial | |
| 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 | |
| # from xformers.ops import memory_efficient_attention | |
| 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] | |
| # x = memory_efficient_attention(q, k, v).transpose(1, 2).reshape(B, N, C) | |
| 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): | |
| x = x + self.drop_path(self.attn(self.norm1(x))) | |
| x = x + 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): | |
| super().__init__() | |
| self.dropout_rate = drop_rate | |
| self.depth = depth | |
| 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)) | |
| 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) | |
| 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) | |
| 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 extract_block_features(self, x): | |
| 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) | |
| outs = {} | |
| for i, blk in enumerate(self.blocks): | |
| x = blk(x) | |
| outs[i] = x.detach() | |
| return outs | |
| def selective_forward(self, x, begin, end): | |
| for i, blk in enumerate(self.blocks): | |
| if i < begin: | |
| continue | |
| if i > end: | |
| break | |
| x = blk(x) | |
| return x | |
| def forward_until(self, x, blk_id): | |
| 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) | |
| for i, blk in enumerate(self.blocks): | |
| x = blk(x) | |
| if i == blk_id: | |
| break | |
| return x | |
| def forward_from(self, x, blk_id): | |
| for i, blk in enumerate(self.blocks): | |
| if i < blk_id: | |
| continue | |
| x = blk(x) | |
| x = self.norm(x) | |
| x = self.head(x[:, 0]) | |
| return x | |
| def forward_patch_embed(self, x): | |
| 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) | |
| return x | |
| def forward_norm_head(self, x): | |
| x = self.norm(x) | |
| x = self.head(x[:, 0]) | |
| return x | |
| def forward_features(self, x): | |
| 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) | |
| for i , blk in enumerate(self.blocks): | |
| x = blk(x) | |
| x = self.norm(x) | |
| return x[:, 0] | |
| def forward(self, x): | |
| x = self.forward_features(x) | |
| if self.dropout_rate: | |
| x = F.dropout(x, p=float(self.dropout_rate), training=self.training) | |
| x = self.head(x) | |
| return x | |
| # DeiT III: Revenge of the ViT (https://arxiv.org/abs/2204.07118) | |
| def deit_tiny_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg_overlay=None, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) | |
| return model | |
| def deit_small_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) | |
| model.default_cfg = _cfg() | |
| if pretrained: | |
| # name = 'https://dl.fbaipublicfiles.com/deit/deit_3_small_'+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 | |
| # ) | |
| checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_small_224_21k.pth')) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def deit_medium_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| patch_size=16, embed_dim=512, depth=12, num_heads=8, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) | |
| model.default_cfg = _cfg() | |
| if pretrained: | |
| name = 'https://dl.fbaipublicfiles.com/deit/deit_3_medium_'+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 | |
| def deit_base_patch16_LS(pretrained=False, pretrained_cfg=None, img_size=224, pretrained_21k = False, pretrained_deit=None, pretrained_cfg_overlay=None, **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),block_layers=Layer_scale_init_Block, **kwargs) | |
| if pretrained: | |
| # name = 'https://dl.fbaipublicfiles.com/deit/deit_3_small_'+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 | |
| # ) | |
| checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_base_224_21k.pth')) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def deit_large_patch16_LS(pretrained=False, img_size=224, pretrained_21k = False, pretrained_cfg=None, pretrained_deit=None, pretrained_cfg_overlay=None, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) | |
| if pretrained: | |
| # name = 'https://dl.fbaipublicfiles.com/deit/deit_3_large_'+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 | |
| # ) | |
| checkpoint = torch.load(os.path.join(pretrained_deit, 'deit_3_large_224_21k.pth')) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def deit_huge_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) | |
| if pretrained: | |
| name = 'https://dl.fbaipublicfiles.com/deit/deit_3_huge_'+str(img_size)+'_' | |
| if pretrained_21k: | |
| name+='21k_v1.pth' | |
| else: | |
| name+='1k_v1.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 | |
| def deit_huge_patch14_52_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1280, depth=52, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) | |
| return model | |
| def deit_huge_patch14_26x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1280, depth=26, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block_paralx2, **kwargs) | |
| return model | |
| def deit_Giant_48x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs) | |
| return model | |
| def deit_giant_40x2_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Block_paral_LS, **kwargs) | |
| return model | |
| def deit_Giant_48_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1664, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) | |
| return model | |
| def deit_giant_40_patch14_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers = Layer_scale_init_Block, **kwargs) | |
| #model.default_cfg = _cfg() | |
| return model | |
| # Models from Three things everyone should know about Vision Transformers (https://arxiv.org/pdf/2203.09795.pdf) | |
| def deit_small_patch16_36_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) | |
| return model | |
| def deit_small_patch16_36(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=384, depth=36, num_heads=6, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) | |
| return model | |
| def deit_small_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs) | |
| return model | |
| def deit_small_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=384, depth=18, num_heads=6, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs) | |
| return model | |
| def deit_base_patch16_18x2_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block_paralx2, **kwargs) | |
| return model | |
| def deit_base_patch16_18x2(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=768, depth=18, num_heads=12, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Block_paralx2, **kwargs) | |
| return model | |
| def deit_base_patch16_36x1_LS(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6),block_layers=Layer_scale_init_Block, **kwargs) | |
| return model | |
| def deit_base_patch16_36x1(pretrained=False, img_size=224, pretrained_21k = False, **kwargs): | |
| model = vit_models( | |
| img_size = img_size, patch_size=16, embed_dim=768, depth=36, num_heads=12, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) | |
| return model | |