Merge branch 'master' of https://github.com/abcdvzz/pytorch-image-models into abcdvzz-master

tests/test_models.py

@@ -15,7 +15,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
     torch._C._jit_set_profiling_mode(False)
 
 # transformer models don't support many of the spatial / feature based model functionalities
-NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*']
+NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/__init__.py

@@ -39,6 +39,7 @@ from .vision_transformer_hybrid import *
 from .vovnet import *
 from .xception import *
 from .xception_aligned import *
+from .twins import *
 
 from .factory import create_model, split_model_name, safe_model_name
 from .helpers import load_checkpoint, resume_checkpoint, model_parameters

timm/models/twins.py

@@ -0,0 +1,625 @@
+""" Twins
+A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
+    - https://arxiv.org/pdf/2104.13840.pdf
+
+Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
+
+"""
+# --------------------------------------------------------
+# Twins
+# Copyright (c) 2021 Meituan
+# Licensed under The Apache 2.0 License [see LICENSE for details]
+# Written by Xinjie Li, Xiangxiang Chu
+# --------------------------------------------------------
+
+import logging
+import math
+from copy import deepcopy
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from .layers import Mlp, DropPath, to_2tuple, trunc_normal_
+from .registry import register_model
+from .vision_transformer import _cfg
+from .vision_transformer import Block as TimmBlock
+from .vision_transformer import Attention as TimmAttention
+from .helpers import build_model_with_cfg, overlay_external_default_cfg
+from .vision_transformer import checkpoint_filter_fn, _init_vit_weights
+
+_logger = logging.getLogger(__name__)
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'patch_embed.proj', 'classifier': 'head',
+        **kwargs
+    }
+
+default_cfgs = {
+    'twins_pcpvt_small': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_small.pth',
+        ),
+    'twins_pcpvt_base': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_base.pth',
+        ),
+    'twins_pcpvt_large': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_large.pth',
+        ),
+    'twins_svt_small': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_small.pth',
+        ),
+    'twins_svt_base': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_base.pth',
+        ),
+    'twins_svt_large': _cfg(
+        url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_large.pth',
+        ),
+}
+
+
+
+class GroupAttention(nn.Module):
+    """
+    LSA: self attention within a group
+    """
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., ws=1):
+        assert ws != 1
+        super(GroupAttention, self).__init__()
+        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
+
+        self.dim = dim
+        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)
+        self.ws = ws
+
+    def forward(self, x, H, W):
+        """
+        There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
+        both. You can choose any one, we recommend forward_padding because it's neat. However,
+        the masking implementation is more reasonable and accurate.
+        Args:
+            x:
+            H:
+            W:
+
+        Returns:
+
+        """
+        return self.forward_padding(x, H, W)
+
+    def forward_mask(self, x, H, W):
+        B, N, C = x.shape
+        x = x.view(B, H, W, C)
+        pad_l = pad_t = 0
+        pad_r = (self.ws - W % self.ws) % self.ws
+        pad_b = (self.ws - H % self.ws) % self.ws
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+        _h, _w = Hp // self.ws, Wp // self.ws
+        mask = torch.zeros((1, Hp, Wp), device=x.device)
+        mask[:, -pad_b:, :].fill_(1)
+        mask[:, :, -pad_r:].fill_(1)
+
+        x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)  # B, _h, _w, ws, ws, C
+        mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1,  _h*_w, self.ws*self.ws)
+        attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3)  # 1, _h*_w, ws*ws, ws*ws
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0))
+        qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads,
+                                            C // self.num_heads).permute(3, 0, 1, 4, 2, 5) # n_h, B, _w*_h, nhead, ws*ws, dim
+        q, k, v = qkv[0], qkv[1], qkv[2]  # B, _h*_w, n_head, ws*ws, dim_head
+        attn = (q @ k.transpose(-2, -1)) * self.scale  # B, _h*_w, n_head, ws*ws, ws*ws
+        attn = attn + attn_mask.unsqueeze(2)
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)  # attn @v ->  B, _h*_w, n_head, ws*ws, dim_head
+        attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
+        x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+        x = x.reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def forward_padding(self, x, H, W):
+        B, N, C = x.shape
+        x = x.view(B, H, W, C)
+        pad_l = pad_t = 0
+        pad_r = (self.ws - W % self.ws) % self.ws
+        pad_b = (self.ws - H % self.ws) % self.ws
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+        _h, _w = Hp // self.ws, Wp // self.ws
+        x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)
+        qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads,
+                                            C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+        attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
+        x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+        x = x.reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    """
+    GSA: using a  key to summarize the information for a group to be efficient.
+    """
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
+        super().__init__()
+        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
+
+        self.dim = dim
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim, bias=qkv_bias)
+        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1:
+            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
+            self.norm = nn.LayerNorm(dim)
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+
+        if self.sr_ratio > 1:
+            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
+            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
+            x_ = self.norm(x_)
+            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        else:
+            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        k, v = kv[0], kv[1]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        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):
+
+    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, sr_ratio=1):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
+        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(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, H, W):
+        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class SBlock(TimmBlock):
+    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, sr_ratio=1):
+        super(SBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop,
+                                     drop_path, act_layer, norm_layer)
+
+    def forward(self, x, H, W):
+        return super(SBlock, self).forward(x)
+
+
+class GroupBlock(TimmBlock):
+    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, sr_ratio=1, ws=1):
+        super(GroupBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop,
+                                         drop_path, act_layer, norm_layer)
+        del self.attn
+        if ws == 1:
+            self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, sr_ratio)
+        else:
+            self.attn = GroupAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, ws)
+
+    def forward(self, x, H, W):
+        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        # img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+
+        self.img_size = img_size
+        self.patch_size = patch_size
+        assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
+            f"img_size {img_size} should be divided by patch_size {patch_size}."
+        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
+        self.num_patches = self.H * self.W
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        self.norm = nn.LayerNorm(embed_dim)
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        x = self.norm(x)
+        H, W = H // self.patch_size[0], W // self.patch_size[1]
+
+        return x, (H, W)
+
+
+# borrow from PVT https://github.com/whai362/PVT.git
+class PyramidVisionTransformer(nn.Module):
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
+                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
+                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
+                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=Block):
+        super().__init__()
+        self.num_classes = num_classes
+        self.depths = depths
+
+        # patch_embed
+        self.patch_embeds = nn.ModuleList()
+        self.pos_embeds = nn.ParameterList()
+        self.pos_drops = nn.ModuleList()
+        self.blocks = nn.ModuleList()
+
+        for i in range(len(depths)):
+            if i == 0:
+                self.patch_embeds.append(PatchEmbed(img_size, patch_size, in_chans, embed_dims[i]))
+            else:
+                self.patch_embeds.append(
+                    # PatchEmbed(img_size // patch_size // 2 ** (i - 1), 2, embed_dims[i - 1], embed_dims[i])
+                    PatchEmbed((img_size[0] // patch_size // 2**(i-1),img_size[1] // patch_size // 2**(i-1)), 2, embed_dims[i - 1], embed_dims[i])
+                    )
+            patch_num = self.patch_embeds[-1].num_patches + 1 if i == len(embed_dims) - 1 else self.patch_embeds[
+                -1].num_patches
+            self.pos_embeds.append(nn.Parameter(torch.zeros(1, patch_num, embed_dims[i])))
+            self.pos_drops.append(nn.Dropout(p=drop_rate))
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+        cur = 0
+        for k in range(len(depths)):
+            _block = nn.ModuleList([block_cls(
+                dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
+                sr_ratio=sr_ratios[k])
+                for i in range(depths[k])])
+            self.blocks.append(_block)
+            cur += depths[k]
+
+        self.norm = norm_layer(embed_dims[-1])
+
+        # cls_token
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[-1]))
+
+        # classification head
+        self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
+
+        # init weights
+        for pos_emb in self.pos_embeds:
+            trunc_normal_(pos_emb, std=.02)
+        self.apply(self._init_weights)
+
+    def reset_drop_path(self, drop_path_rate):
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
+        cur = 0
+        for k in range(len(self.depths)):
+            for i in range(self.depths[k]):
+                self.blocks[k][i].drop_path.drop_prob = dpr[cur + i]
+            cur += self.depths[k]
+
+    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)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'cls_token'}
+
+    def get_classifier(self):
+        return self.head
+
+    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 forward_features(self, x):
+        B = x.shape[0]
+        for i in range(len(self.depths)):
+            x, (H, W) = self.patch_embeds[i](x)
+            if i == len(self.depths) - 1:
+                cls_tokens = self.cls_token.expand(B, -1, -1)
+                x = torch.cat((cls_tokens, x), dim=1)
+            x = x + self.pos_embeds[i]
+            x = self.pos_drops[i](x)
+            for blk in self.blocks[i]:
+                x = blk(x, H, W)
+            if i < len(self.depths) - 1:
+                x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+
+        x = self.norm(x)
+
+        return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+
+        return x
+
+
+# PEG  from https://arxiv.org/abs/2102.10882
+class PosCNN(nn.Module):
+    def __init__(self, in_chans, embed_dim=768, s=1):
+        super(PosCNN, self).__init__()
+        self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, s, 1, bias=True, groups=embed_dim), )
+        self.s = s
+
+    def forward(self, x, H, W):
+        B, N, C = x.shape
+        feat_token = x
+        cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
+        if self.s == 1:
+            x = self.proj(cnn_feat) + cnn_feat
+        else:
+            x = self.proj(cnn_feat)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+    def no_weight_decay(self):
+        return ['proj.%d.weight' % i for i in range(4)]
+
+
+class CPVTV2(PyramidVisionTransformer):
+    """
+    Use useful results from CPVT. PEG and GAP.
+    Therefore, cls token is no longer required.
+    PEG is used to encode the absolute position on the fly, which greatly affects the performance when input resolution
+    changes during the training (such as segmentation, detection)
+    """
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
+                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
+                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
+                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=Block):
+        super(CPVTV2, self).__init__(img_size, patch_size, in_chans, num_classes, embed_dims, num_heads, mlp_ratios,
+                                     qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate, norm_layer, depths,
+                                     sr_ratios, block_cls)
+        del self.pos_embeds
+        del self.cls_token
+        self.pos_block = nn.ModuleList(
+            [PosCNN(embed_dim, embed_dim) for embed_dim in embed_dims]
+        )
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        import math
+        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)
+        elif isinstance(m, nn.Conv2d):
+            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+            fan_out //= m.groups
+            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
+            if m.bias is not None:
+                m.bias.data.zero_()
+        elif isinstance(m, nn.BatchNorm2d):
+            m.weight.data.fill_(1.0)
+            m.bias.data.zero_()
+
+    def no_weight_decay(self):
+        return set(['cls_token'] + ['pos_block.' + n for n, p in self.pos_block.named_parameters()])
+
+    def forward_features(self, x):
+        B = x.shape[0]
+
+        for i in range(len(self.depths)):
+            x, (H, W) = self.patch_embeds[i](x)
+            x = self.pos_drops[i](x)
+            for j, blk in enumerate(self.blocks[i]):
+                x = blk(x, H, W)
+                if j == 0:
+                    x = self.pos_block[i](x, H, W)  # PEG here
+            if i < len(self.depths) - 1:
+                x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
+
+        x = self.norm(x)
+
+        return x.mean(dim=1)  # GAP here
+
+
+class Twins_PCPVT(CPVTV2):
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256],
+                 num_heads=[1, 2, 4], mlp_ratios=[4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
+                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
+                 depths=[4, 4, 4], sr_ratios=[4, 2, 1], block_cls=SBlock):
+        super(Twins_PCPVT, self).__init__(img_size, patch_size, in_chans, num_classes, embed_dims, num_heads,
+                                    mlp_ratios, qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate,
+                                    norm_layer, depths, sr_ratios, block_cls)
+
+
+class Twins_SVT(Twins_PCPVT):
+    """
+    alias Twins-SVT
+    """
+    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256],
+                 num_heads=[1, 2, 4], mlp_ratios=[4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
+                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
+                 depths=[4, 4, 4], sr_ratios=[4, 2, 1], block_cls=GroupBlock, wss=[7, 7, 7]):
+        super(Twins_SVT, self).__init__(img_size, patch_size, in_chans, num_classes, embed_dims, num_heads,
+                                     mlp_ratios, qkv_bias, qk_scale, drop_rate, attn_drop_rate, drop_path_rate,
+                                     norm_layer, depths, sr_ratios, block_cls)
+        del self.blocks
+        self.wss = wss
+        # transformer encoder
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
+        cur = 0
+        self.blocks = nn.ModuleList()
+        for k in range(len(depths)):
+            _block = nn.ModuleList([block_cls(
+                dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
+                sr_ratio=sr_ratios[k], ws=1 if i % 2 == 1 else wss[k]) for i in range(depths[k])])
+            self.blocks.append(_block)
+            cur += depths[k]
+        self.apply(self._init_weights)
+
+
+def _conv_filter(state_dict, patch_size=16):
+    """ convert patch embedding weight from manual patchify + linear proj to conv"""
+    out_dict = {}
+    for k, v in state_dict.items():
+        if 'patch_embed.proj.weight' in k:
+            v = v.reshape((v.shape[0], 3, patch_size, patch_size))
+        out_dict[k] = v
+
+    return out_dict
+
+def _create_twins_svt(variant, pretrained=False, default_cfg=None, **kwargs):
+    if default_cfg is None:
+        default_cfg = deepcopy(default_cfgs[variant])
+    overlay_external_default_cfg(default_cfg, kwargs)
+    default_num_classes = default_cfg['num_classes']
+    default_img_size = default_cfg['input_size'][-2:]
+
+    num_classes = kwargs.pop('num_classes', default_num_classes)
+    img_size = kwargs.pop('img_size', default_img_size)
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Vision Transformer models.')
+
+    model = build_model_with_cfg(
+        Twins_SVT, variant, pretrained,
+        default_cfg=default_cfg,
+        img_size=img_size,
+        num_classes=num_classes,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        **kwargs)
+
+    return model
+
+def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs):
+    if default_cfg is None:
+        default_cfg = deepcopy(default_cfgs[variant])
+    overlay_external_default_cfg(default_cfg, kwargs)
+    default_num_classes = default_cfg['num_classes']
+    default_img_size = default_cfg['input_size'][-2:]
+
+    num_classes = kwargs.pop('num_classes', default_num_classes)
+    img_size = kwargs.pop('img_size', default_img_size)
+    if kwargs.get('features_only', None):
+        raise RuntimeError('features_only not implemented for Vision Transformer models.')
+
+    model = build_model_with_cfg(
+        CPVTV2, variant, pretrained,
+        default_cfg=default_cfg,
+        img_size=img_size,
+        num_classes=num_classes,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        **kwargs)
+
+    return model
+
+
+@register_model
+def twins_pcpvt_small(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+    return _create_twins_pcpvt('twins_pcpvt_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def twins_pcpvt_base(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+    return _create_twins_pcpvt('twins_pcpvt_base', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def twins_pcpvt_large(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+    return _create_twins_pcpvt('twins_pcpvt_large', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def twins_svt_small(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+    return _create_twins_svt('twins_svt_small', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def twins_svt_base(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+
+    return _create_twins_svt('twins_svt_base', pretrained=pretrained, **model_kwargs)
+
+
+@register_model
+def twins_svt_large(pretrained=False, **kwargs):
+    model_kwargs = dict(
+        patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32], mlp_ratios=[4, 4, 4, 4],
+        qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
+        **kwargs)
+
+    return _create_twins_svt('twins_svt_large', pretrained=pretrained, **model_kwargs)