314 lines
13 KiB
Python
314 lines
13 KiB
Python
# Copyright (c) [2012]-[2021] Shanghai Yitu Technology Co., Ltd.
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#
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# This source code is licensed under the Clear BSD License
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# LICENSE file in the root directory of this file
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# All rights reserved.
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"""
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T2T-ViT
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"""
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import torch
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import torch.nn as nn
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from timm.models.helpers import load_pretrained
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from timm.models.registry import register_model
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from timm.models.layers import trunc_normal_
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import numpy as np
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from .token_transformer import Token_transformer
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from .token_performer import Token_performer
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from .transformer_block import Block, get_sinusoid_encoding
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from timm.models import load_checkpoint
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic',
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'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225),
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'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'T2t_vit_t_14': _cfg(),
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'T2t_vit_t_19': _cfg(),
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'T2t_vit_t_24': _cfg(),
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'T2t_vit_14': _cfg(),
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'T2t_vit_19': _cfg(),
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'T2t_vit_24': _cfg(),
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'T2t_vit_7': _cfg(),
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'T2t_vit_10': _cfg(),
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'T2t_vit_12': _cfg(),
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'T2t_vit_14_resnext': _cfg(),
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'T2t_vit_14_wide': _cfg(),
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}
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class T2T_module(nn.Module):
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"""
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Tokens-to-Token encoding module
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"""
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def __init__(self, img_size=224, tokens_type='performer', in_chans=3, embed_dim=768, token_dim=64):
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super().__init__()
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if tokens_type == 'transformer':
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print('adopt transformer encoder for tokens-to-token')
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self.soft_split0 = nn.Unfold(kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
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self.soft_split1 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.soft_split2 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.attention1 = Token_transformer(dim=in_chans * 7 * 7, in_dim=token_dim, num_heads=1, mlp_ratio=1.0)
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self.attention2 = Token_transformer(dim=token_dim * 3 * 3, in_dim=token_dim, num_heads=1, mlp_ratio=1.0)
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self.project = nn.Linear(token_dim * 3 * 3, embed_dim)
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elif tokens_type == 'performer':
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print('adopt performer encoder for tokens-to-token')
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self.soft_split0 = nn.Unfold(kernel_size=(7, 7), stride=(4, 4), padding=(2, 2))
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self.soft_split1 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.soft_split2 = nn.Unfold(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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# self.attention1 = Token_performer(dim=token_dim, in_dim=in_chans*7*7, kernel_ratio=0.5)
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# self.attention2 = Token_performer(dim=token_dim, in_dim=token_dim*3*3, kernel_ratio=0.5)
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self.attention1 = Token_performer(dim=in_chans*7*7, in_dim=token_dim, kernel_ratio=0.5)
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self.attention2 = Token_performer(dim=token_dim*3*3, in_dim=token_dim, kernel_ratio=0.5)
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self.project = nn.Linear(token_dim * 3 * 3, embed_dim)
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elif tokens_type == 'convolution': # just for comparison with conolution, not our model
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# for this tokens type, you need change forward as three convolution operation
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print('adopt convolution layers for tokens-to-token')
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self.soft_split0 = nn.Conv2d(3, token_dim, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2)) # the 1st convolution
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self.soft_split1 = nn.Conv2d(token_dim, token_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) # the 2nd convolution
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self.project = nn.Conv2d(token_dim, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) # the 3rd convolution
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self.num_patches = (img_size // (4 * 2 * 2)) * (img_size // (4 * 2 * 2)) # there are 3 sfot split, stride are 4,2,2 seperately
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def forward(self, x):
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# step0: soft split
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x = self.soft_split0(x).transpose(1, 2)
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# x [B, 56*56, 147=7*7*3]
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# iteration1: restricturization/reconstruction
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x_1_4 = self.attention1(x)
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B, new_HW, C = x_1_4.shape
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x = x_1_4.transpose(1,2).reshape(B, C, int(np.sqrt(new_HW)), int(np.sqrt(new_HW)))
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# iteration1: soft split
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x = self.soft_split1(x).transpose(1, 2)
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# iteration2: restricturization/reconstruction
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x_1_8 = self.attention2(x)
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B, new_HW, C = x_1_8.shape
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x = x_1_8.transpose(1, 2).reshape(B, C, int(np.sqrt(new_HW)), int(np.sqrt(new_HW)))
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# iteration2: soft split
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x = self.soft_split2(x).transpose(1, 2)
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# final tokens
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x = self.project(x)
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return x, x_1_8, x_1_4
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class T2T_ViT(nn.Module):
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def __init__(self, img_size=224, tokens_type='performer', 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):
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super().__init__()
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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.tokens_to_token = T2T_module(
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img_size=img_size, tokens_type=tokens_type, in_chans=in_chans, embed_dim=embed_dim)
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num_patches = self.tokens_to_token.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(data=get_sinusoid_encoding(n_position=num_patches + 1, d_hid=embed_dim), requires_grad=False)
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self.pos_drop = nn.Dropout(p=drop_rate)
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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self.blocks = nn.ModuleList([
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Block(
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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=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
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for i in range(depth)])
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self.norm = norm_layer(embed_dim)
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# Classifier 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.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 {'cls_token'}
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def get_classifier(self):
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return self.head
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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, x_1_8, x_1_4 = self.tokens_to_token(x)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + self.pos_embed
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x = self.pos_drop(x)
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# T2T-ViT backbone
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for blk in 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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return x_1_4, x_1_8, x[:, 1:, :]
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def forward(self, x):
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x, x_1_8, x_1_4 = self.forward_features(x)
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# x = self.head(x)
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return x, x_1_8, x_1_4
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@register_model
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def T2t_vit_t_14(pretrained=True, **kwargs): # adopt transformers for tokens to token
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# if pretrained:
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# kwargs.setdefault('qk_scale', 384 ** -0.5)
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# model = T2T_ViT(tokens_type='transformer', embed_dim=384, depth=14, num_heads=6, mlp_ratio=3., **kwargs)
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model = T2T_ViT(tokens_type='transformer', embed_dim=384, depth=14, num_heads=6, mlp_ratio=3.)
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model.default_cfg = default_cfgs['T2t_vit_t_14']
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if kwargs:
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args = kwargs['args']
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if pretrained:
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load_checkpoint(model, args.pretrained_model, use_ema=True)
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print('Model loaded from {}'.format(args.pretrained_model))
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# load_pretrained(
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# model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_t_19(pretrained=False, **kwargs): # adopt transformers for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 448 ** -0.5)
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model = T2T_ViT(tokens_type='transformer', embed_dim=448, depth=19, num_heads=7, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_t_19']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_t_24(pretrained=False, **kwargs): # adopt transformers for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 512 ** -0.5)
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model = T2T_ViT(tokens_type='transformer', embed_dim=512, depth=24, num_heads=8, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_t_24']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_7(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 256 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=7, num_heads=4, mlp_ratio=2., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_7']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_10(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 256 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=10, num_heads=4, mlp_ratio=2., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_10']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_12(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 256 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=256, depth=12, num_heads=4, mlp_ratio=2., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_12']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_14(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 384 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=384, depth=14, num_heads=6, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_14']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_19(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 448 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=448, depth=19, num_heads=7, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_19']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_24(pretrained=False, **kwargs): # adopt performer for tokens to token
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if pretrained:
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kwargs.setdefault('qk_scale', 512 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=512, depth=24, num_heads=8, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_24']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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# rexnext and wide structure
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@register_model
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def T2t_vit_14_resnext(pretrained=False, **kwargs):
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if pretrained:
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kwargs.setdefault('qk_scale', 384 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=384, depth=14, num_heads=32, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_14_resnext']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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@register_model
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def T2t_vit_14_wide(pretrained=False, **kwargs):
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if pretrained:
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kwargs.setdefault('qk_scale', 512 ** -0.5)
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model = T2T_ViT(tokens_type='performer', embed_dim=768, depth=4, num_heads=12, mlp_ratio=3., **kwargs)
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model.default_cfg = default_cfgs['T2t_vit_14_wide']
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if pretrained:
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load_pretrained(
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model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3))
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return model
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