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@ -11,11 +11,9 @@ Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/li
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# Licensed under The Apache 2.0 License [see LICENSE for details]
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# Licensed under The Apache 2.0 License [see LICENSE for details]
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# Written by Xinjie Li, Xiangxiang Chu
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# Written by Xinjie Li, Xiangxiang Chu
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# --------------------------------------------------------
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# --------------------------------------------------------
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import logging
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import math
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import math
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from copy import deepcopy
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from copy import deepcopy
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from typing import Optional
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from typing import Optional, Tuple
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import torch
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import torch
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import torch.nn as nn
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import torch.nn as nn
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@ -25,13 +23,9 @@ from functools import partial
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .layers import Mlp, DropPath, to_2tuple, trunc_normal_
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from .layers import Mlp, DropPath, to_2tuple, trunc_normal_
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from .registry import register_model
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from .registry import register_model
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from .vision_transformer import _cfg
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from .vision_transformer import Attention
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from .vision_transformer import Block as TimmBlock
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from .vision_transformer import Attention as TimmAttention
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from .helpers import build_model_with_cfg, overlay_external_default_cfg
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from .helpers import build_model_with_cfg, overlay_external_default_cfg
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from .vision_transformer import checkpoint_filter_fn, _init_vit_weights
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_logger = logging.getLogger(__name__)
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def _cfg(url='', **kwargs):
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def _cfg(url='', **kwargs):
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return {
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return {
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@ -43,6 +37,7 @@ def _cfg(url='', **kwargs):
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**kwargs
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**kwargs
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}
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}
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default_cfgs = {
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default_cfgs = {
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'twins_pcpvt_small': _cfg(
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'twins_pcpvt_small': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_small.pth',
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_small.pth',
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@ -64,78 +59,34 @@ default_cfgs = {
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),
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),
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}
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}
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Size_ = Tuple[int, int]
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class GroupAttention(nn.Module):
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class LocallyGroupedAttn(nn.Module):
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"""
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""" LSA: self attention within a group
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LSA: self attention within a group
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"""
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"""
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., ws=1):
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def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1):
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assert ws != 1
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assert ws != 1
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super(GroupAttention, self).__init__()
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super(LocallyGroupedAttn, self).__init__()
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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self.dim = dim
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self.dim = dim
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self.num_heads = num_heads
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self.num_heads = num_heads
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head_dim = dim // 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.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.qkv = nn.Linear(dim, dim * 3, bias=True)
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self.attn_drop = nn.Dropout(attn_drop)
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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 = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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self.ws = ws
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self.ws = ws
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def forward(self, x, H, W):
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def forward(self, x, size: Size_):
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"""
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# There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
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There are two implementations for this function, zero padding or mask. We don't observe obvious difference for
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# both. You can choose any one, we recommend forward_padding because it's neat. However,
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both. You can choose any one, we recommend forward_padding because it's neat. However,
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# the masking implementation is more reasonable and accurate.
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the masking implementation is more reasonable and accurate.
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Args:
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x:
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H:
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W:
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Returns:
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"""
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return self.forward_padding(x, H, W)
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def forward_mask(self, x, H, W):
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B, N, C = x.shape
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x = x.view(B, H, W, C)
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pad_l = pad_t = 0
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pad_r = (self.ws - W % self.ws) % self.ws
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pad_b = (self.ws - H % self.ws) % self.ws
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x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
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_, Hp, Wp, _ = x.shape
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_h, _w = Hp // self.ws, Wp // self.ws
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mask = torch.zeros((1, Hp, Wp), device=x.device)
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mask[:, -pad_b:, :].fill_(1)
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mask[:, :, -pad_r:].fill_(1)
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x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) # B, _h, _w, ws, ws, C
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mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h*_w, self.ws*self.ws)
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attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3) # 1, _h*_w, ws*ws, ws*ws
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0))
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qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads,
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C // self.num_heads).permute(3, 0, 1, 4, 2, 5) # n_h, B, _w*_h, nhead, ws*ws, dim
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q, k, v = qkv[0], qkv[1], qkv[2] # B, _h*_w, n_head, ws*ws, dim_head
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attn = (q @ k.transpose(-2, -1)) * self.scale # B, _h*_w, n_head, ws*ws, ws*ws
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attn = attn + attn_mask.unsqueeze(2)
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn) # attn @v -> B, _h*_w, n_head, ws*ws, dim_head
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attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
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x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
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if pad_r > 0 or pad_b > 0:
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x = x[:, :H, :W, :].contiguous()
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x = x.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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def forward_padding(self, x, H, W):
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B, N, C = x.shape
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B, N, C = x.shape
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H, W = size
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x = x.view(B, H, W, C)
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x = x.view(B, H, W, C)
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pad_l = pad_t = 0
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pad_l = pad_t = 0
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pad_r = (self.ws - W % self.ws) % self.ws
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pad_r = (self.ws - W % self.ws) % self.ws
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@ -144,8 +95,8 @@ class GroupAttention(nn.Module):
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_, Hp, Wp, _ = x.shape
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_, Hp, Wp, _ = x.shape
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_h, _w = Hp // self.ws, Wp // self.ws
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_h, _w = Hp // self.ws, Wp // self.ws
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x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)
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x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3)
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qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads,
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qkv = self.qkv(x).reshape(
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C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
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B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
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q, k, v = qkv[0], qkv[1], qkv[2]
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q, k, v = qkv[0], qkv[1], qkv[2]
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = attn.softmax(dim=-1)
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@ -159,22 +110,56 @@ class GroupAttention(nn.Module):
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x = self.proj_drop(x)
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x = self.proj_drop(x)
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return x
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return x
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# def forward_mask(self, x, size: Size_):
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class Attention(nn.Module):
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# B, N, C = x.shape
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"""
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# H, W = size
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GSA: using a key to summarize the information for a group to be efficient.
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# x = x.view(B, H, W, C)
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# pad_l = pad_t = 0
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# pad_r = (self.ws - W % self.ws) % self.ws
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# pad_b = (self.ws - H % self.ws) % self.ws
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# x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
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# _, Hp, Wp, _ = x.shape
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# _h, _w = Hp // self.ws, Wp // self.ws
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# mask = torch.zeros((1, Hp, Wp), device=x.device)
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# mask[:, -pad_b:, :].fill_(1)
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# mask[:, :, -pad_r:].fill_(1)
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#
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# x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) # B, _h, _w, ws, ws, C
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# mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h * _w, self.ws * self.ws)
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# attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3) # 1, _h*_w, ws*ws, ws*ws
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# attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0))
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# qkv = self.qkv(x).reshape(
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# B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5)
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# # n_h, B, _w*_h, nhead, ws*ws, dim
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# q, k, v = qkv[0], qkv[1], qkv[2] # B, _h*_w, n_head, ws*ws, dim_head
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# attn = (q @ k.transpose(-2, -1)) * self.scale # B, _h*_w, n_head, ws*ws, ws*ws
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# attn = attn + attn_mask.unsqueeze(2)
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# attn = attn.softmax(dim=-1)
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# attn = self.attn_drop(attn) # attn @v -> B, _h*_w, n_head, ws*ws, dim_head
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# attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C)
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# x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C)
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# if pad_r > 0 or pad_b > 0:
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# x = x[:, :H, :W, :].contiguous()
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# x = x.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 GlobalSubSampleAttn(nn.Module):
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""" GSA: using a key to summarize the information for a group to be efficient.
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"""
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"""
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def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
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def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1):
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super().__init__()
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super().__init__()
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
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self.dim = dim
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self.dim = dim
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self.num_heads = num_heads
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self.num_heads = num_heads
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head_dim = dim // 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.scale = head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.q = nn.Linear(dim, dim, bias=True)
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self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
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self.kv = nn.Linear(dim, dim * 2, bias=True)
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self.attn_drop = nn.Dropout(attn_drop)
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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 = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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@ -183,18 +168,19 @@ class Attention(nn.Module):
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if sr_ratio > 1:
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if sr_ratio > 1:
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
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self.norm = nn.LayerNorm(dim)
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self.norm = nn.LayerNorm(dim)
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else:
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self.sr = None
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self.norm = None
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def forward(self, x, H, W):
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def forward(self, x, size: Size_):
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B, N, C = x.shape
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B, N, C = x.shape
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q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
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if self.sr_ratio > 1:
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if self.sr is not None:
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
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x = x.permute(0, 2, 1).reshape(B, C, *size)
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x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
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x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
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x_ = self.norm(x_)
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x = self.norm(x)
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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else:
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kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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k, v = kv[0], kv[1]
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k, v = kv[0], kv[1]
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = (q @ k.transpose(-2, -1)) * self.scale
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@ -210,52 +196,46 @@ class Attention(nn.Module):
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class Block(nn.Module):
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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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def __init__(self, dim, num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=None):
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super().__init__()
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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if ws is None:
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dim,
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self.attn = Attention(dim, num_heads, False, None, attn_drop, drop)
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num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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elif ws == 1:
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attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
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self.attn = GlobalSubSampleAttn(dim, num_heads, attn_drop, drop, sr_ratio)
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else:
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self.attn = LocallyGroupedAttn(dim, num_heads, attn_drop, drop, ws)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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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.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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def forward(self, x, H, W):
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def forward(self, x, size: Size_):
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x = x + self.drop_path(self.attn(self.norm1(x), H, W))
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x = x + self.drop_path(self.attn(self.norm1(x), size))
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x = x + self.drop_path(self.mlp(self.norm2(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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return x
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class SBlock(TimmBlock):
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|
class PosConv(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.,
|
|
|
|
# PEG from https://arxiv.org/abs/2102.10882
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|
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
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|
|
def __init__(self, in_chans, embed_dim=768, stride=1):
|
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|
|
super(SBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop,
|
|
|
|
super(PosConv, self).__init__()
|
|
|
|
drop_path, act_layer, norm_layer)
|
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|
|
self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), )
|
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|
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|
|
self.stride = stride
|
|
|
|
def forward(self, x, H, W):
|
|
|
|
|
|
|
|
return super(SBlock, self).forward(x)
|
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class GroupBlock(TimmBlock):
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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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|
|
|
|
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|
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=1):
|
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|
|
|
|
|
|
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):
|
|
|
|
def forward(self, x, size: Size_):
|
|
|
|
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
|
|
|
B, N, C = x.shape
|
|
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
|
|
cnn_feat_token = x.transpose(1, 2).view(B, C, *size)
|
|
|
|
|
|
|
|
x = self.proj(cnn_feat_token)
|
|
|
|
|
|
|
|
if self.stride == 1:
|
|
|
|
|
|
|
|
x += cnn_feat_token
|
|
|
|
|
|
|
|
x = x.flatten(2).transpose(1, 2)
|
|
|
|
return x
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def no_weight_decay(self):
|
|
|
|
|
|
|
|
return ['proj.%d.weight' % i for i in range(4)]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
|
|
class PatchEmbed(nn.Module):
|
|
|
|
""" Image to Patch Embedding
|
|
|
|
""" Image to Patch Embedding
|
|
|
@ -263,7 +243,7 @@ class PatchEmbed(nn.Module):
|
|
|
|
|
|
|
|
|
|
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
|
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
|
|
|
|
super().__init__()
|
|
|
|
super().__init__()
|
|
|
|
# img_size = to_2tuple(img_size)
|
|
|
|
img_size = to_2tuple(img_size)
|
|
|
|
patch_size = to_2tuple(patch_size)
|
|
|
|
patch_size = to_2tuple(patch_size)
|
|
|
|
|
|
|
|
|
|
|
|
self.img_size = img_size
|
|
|
|
self.img_size = img_size
|
|
|
@ -275,90 +255,62 @@ class PatchEmbed(nn.Module):
|
|
|
|
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
|
|
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
|
|
|
self.norm = nn.LayerNorm(embed_dim)
|
|
|
|
self.norm = nn.LayerNorm(embed_dim)
|
|
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
def forward(self, x) -> Tuple[torch.Tensor, Size_]:
|
|
|
|
B, C, H, W = x.shape
|
|
|
|
B, C, H, W = x.shape
|
|
|
|
|
|
|
|
|
|
|
|
x = self.proj(x).flatten(2).transpose(1, 2)
|
|
|
|
x = self.proj(x).flatten(2).transpose(1, 2)
|
|
|
|
x = self.norm(x)
|
|
|
|
x = self.norm(x)
|
|
|
|
H, W = H // self.patch_size[0], W // self.patch_size[1]
|
|
|
|
out_size = (H // self.patch_size[0], W // self.patch_size[1])
|
|
|
|
|
|
|
|
|
|
|
|
return x, (H, W)
|
|
|
|
return x, out_size
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# borrow from PVT https://github.com/whai362/PVT.git
|
|
|
|
class Twins(nn.Module):
|
|
|
|
class PyramidVisionTransformer(nn.Module):
|
|
|
|
# Adapted from PVT https://github.com/whai362/PVT.git
|
|
|
|
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
|
|
|
|
def __init__(
|
|
|
|
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
|
|
|
|
self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, embed_dims=(64, 128, 256, 512),
|
|
|
|
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
|
|
|
|
num_heads=(1, 2, 4, 8), mlp_ratios=(4, 4, 4, 4), drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
|
|
|
|
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=Block):
|
|
|
|
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=(3, 4, 6, 3), sr_ratios=(8, 4, 2, 1), wss=None,
|
|
|
|
|
|
|
|
block_cls=Block):
|
|
|
|
super().__init__()
|
|
|
|
super().__init__()
|
|
|
|
self.num_classes = num_classes
|
|
|
|
self.num_classes = num_classes
|
|
|
|
self.depths = depths
|
|
|
|
self.depths = depths
|
|
|
|
|
|
|
|
|
|
|
|
# patch_embed
|
|
|
|
img_size = to_2tuple(img_size)
|
|
|
|
|
|
|
|
prev_chs = in_chans
|
|
|
|
self.patch_embeds = nn.ModuleList()
|
|
|
|
self.patch_embeds = nn.ModuleList()
|
|
|
|
self.pos_embeds = nn.ParameterList()
|
|
|
|
|
|
|
|
self.pos_drops = nn.ModuleList()
|
|
|
|
self.pos_drops = nn.ModuleList()
|
|
|
|
self.blocks = nn.ModuleList()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for i in range(len(depths)):
|
|
|
|
for i in range(len(depths)):
|
|
|
|
if i == 0:
|
|
|
|
self.patch_embeds.append(PatchEmbed(img_size, patch_size, prev_chs, embed_dims[i]))
|
|
|
|
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))
|
|
|
|
self.pos_drops.append(nn.Dropout(p=drop_rate))
|
|
|
|
|
|
|
|
prev_chs = embed_dims[i]
|
|
|
|
|
|
|
|
img_size = tuple(t // patch_size for t in img_size)
|
|
|
|
|
|
|
|
patch_size = 2
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
self.blocks = nn.ModuleList()
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
|
|
|
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
|
|
|
cur = 0
|
|
|
|
cur = 0
|
|
|
|
for k in range(len(depths)):
|
|
|
|
for k in range(len(depths)):
|
|
|
|
_block = nn.ModuleList([block_cls(
|
|
|
|
_block = nn.ModuleList([block_cls(
|
|
|
|
dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias,
|
|
|
|
dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], drop=drop_rate,
|
|
|
|
qk_scale=qk_scale,
|
|
|
|
attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[k],
|
|
|
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
|
|
|
ws=1 if wss is None or i % 2 == 1 else wss[k]) for i in range(depths[k])])
|
|
|
|
sr_ratio=sr_ratios[k])
|
|
|
|
|
|
|
|
for i in range(depths[k])])
|
|
|
|
|
|
|
|
self.blocks.append(_block)
|
|
|
|
self.blocks.append(_block)
|
|
|
|
cur += depths[k]
|
|
|
|
cur += depths[k]
|
|
|
|
|
|
|
|
|
|
|
|
self.norm = norm_layer(embed_dims[-1])
|
|
|
|
self.pos_block = nn.ModuleList([PosConv(embed_dim, embed_dim) for embed_dim in embed_dims])
|
|
|
|
|
|
|
|
|
|
|
|
# cls_token
|
|
|
|
self.norm = norm_layer(embed_dims[-1])
|
|
|
|
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[-1]))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# classification head
|
|
|
|
# classification head
|
|
|
|
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
|
|
|
|
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
|
|
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# init weights
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# init weights
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for pos_emb in self.pos_embeds:
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trunc_normal_(pos_emb, std=.02)
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self.apply(self._init_weights)
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self.apply(self._init_weights)
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def reset_drop_path(self, drop_path_rate):
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
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cur = 0
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for k in range(len(self.depths)):
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for i in range(self.depths[k]):
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self.blocks[k][i].drop_path.drop_prob = dpr[cur + i]
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cur += self.depths[k]
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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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@torch.jit.ignore
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def no_weight_decay(self):
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def no_weight_decay(self):
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return {'cls_token'}
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return set(['pos_block.' + n for n, p in self.pos_block.named_parameters()])
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def get_classifier(self):
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def get_classifier(self):
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return self.head
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return self.head
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@ -367,76 +319,7 @@ class PyramidVisionTransformer(nn.Module):
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self.num_classes = num_classes
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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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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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for i in range(len(self.depths)):
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x, (H, W) = self.patch_embeds[i](x)
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if i == len(self.depths) - 1:
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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_embeds[i]
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x = self.pos_drops[i](x)
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for blk in self.blocks[i]:
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x = blk(x, H, W)
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if i < len(self.depths) - 1:
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x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
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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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x = self.head(x)
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return x
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# PEG from https://arxiv.org/abs/2102.10882
|
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|
class PosCNN(nn.Module):
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def __init__(self, in_chans, embed_dim=768, s=1):
|
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|
super(PosCNN, self).__init__()
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|
self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, s, 1, bias=True, groups=embed_dim), )
|
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|
self.s = s
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|
def forward(self, x, H, W):
|
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|
B, N, C = x.shape
|
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|
|
feat_token = x
|
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|
|
cnn_feat = feat_token.transpose(1, 2).view(B, C, H, W)
|
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|
|
|
if self.s == 1:
|
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|
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|
|
x = self.proj(cnn_feat) + cnn_feat
|
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|
else:
|
|
|
|
|
|
|
|
x = self.proj(cnn_feat)
|
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|
|
x = x.flatten(2).transpose(1, 2)
|
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|
|
return x
|
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|
|
|
|
|
def no_weight_decay(self):
|
|
|
|
|
|
|
|
return ['proj.%d.weight' % i for i in range(4)]
|
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|
|
|
|
|
|
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|
|
|
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|
|
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):
|
|
|
|
def _init_weights(self, m):
|
|
|
|
import math
|
|
|
|
|
|
|
|
if isinstance(m, nn.Linear):
|
|
|
|
if isinstance(m, nn.Linear):
|
|
|
|
trunc_normal_(m.weight, std=.02)
|
|
|
|
trunc_normal_(m.weight, std=.02)
|
|
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
|
|
if isinstance(m, nn.Linear) and m.bias is not None:
|
|
|
@ -454,98 +337,28 @@ class CPVTV2(PyramidVisionTransformer):
|
|
|
|
m.weight.data.fill_(1.0)
|
|
|
|
m.weight.data.fill_(1.0)
|
|
|
|
m.bias.data.zero_()
|
|
|
|
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):
|
|
|
|
def forward_features(self, x):
|
|
|
|
B = x.shape[0]
|
|
|
|
B = x.shape[0]
|
|
|
|
|
|
|
|
for i, (embed, drop, blocks, pos_blk) in enumerate(
|
|
|
|
for i in range(len(self.depths)):
|
|
|
|
zip(self.patch_embeds, self.pos_drops, self.blocks, self.pos_block)):
|
|
|
|
x, (H, W) = self.patch_embeds[i](x)
|
|
|
|
x, size = embed(x)
|
|
|
|
x = self.pos_drops[i](x)
|
|
|
|
x = drop(x)
|
|
|
|
for j, blk in enumerate(self.blocks[i]):
|
|
|
|
for j, blk in enumerate(blocks):
|
|
|
|
x = blk(x, H, W)
|
|
|
|
x = blk(x, size)
|
|
|
|
if j == 0:
|
|
|
|
if j == 0:
|
|
|
|
x = self.pos_block[i](x, H, W) # PEG here
|
|
|
|
x = pos_blk(x, size) # PEG here
|
|
|
|
if i < len(self.depths) - 1:
|
|
|
|
if i < len(self.depths) - 1:
|
|
|
|
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
x = x.reshape(B, *size, -1).permute(0, 3, 1, 2).contiguous()
|
|
|
|
|
|
|
|
|
|
|
|
x = self.norm(x)
|
|
|
|
x = self.norm(x)
|
|
|
|
|
|
|
|
|
|
|
|
return x.mean(dim=1) # GAP here
|
|
|
|
return x.mean(dim=1) # GAP here
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
|
|
|
x = self.forward_features(x)
|
|
|
|
|
|
|
|
x = self.head(x)
|
|
|
|
|
|
|
|
return x
|
|
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
|
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def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs):
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def _create_twins(variant, pretrained=False, default_cfg=None, **kwargs):
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if default_cfg is None:
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if default_cfg is None:
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default_cfg = deepcopy(default_cfgs[variant])
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default_cfg = deepcopy(default_cfgs[variant])
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overlay_external_default_cfg(default_cfg, kwargs)
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overlay_external_default_cfg(default_cfg, kwargs)
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@ -558,11 +371,10 @@ def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs):
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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raise RuntimeError('features_only not implemented for Vision Transformer models.')
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model = build_model_with_cfg(
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model = build_model_with_cfg(
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CPVTV2, variant, pretrained,
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Twins, variant, pretrained,
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default_cfg=default_cfg,
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default_cfg=default_cfg,
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img_size=img_size,
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img_size=img_size,
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num_classes=num_classes,
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num_classes=num_classes,
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pretrained_filter_fn=checkpoint_filter_fn,
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**kwargs)
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**kwargs)
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return model
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return model
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@ -571,55 +383,46 @@ def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs):
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@register_model
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@register_model
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def twins_pcpvt_small(pretrained=False, **kwargs):
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def twins_pcpvt_small(pretrained=False, **kwargs):
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model_kwargs = dict(
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model_kwargs = dict(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
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depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
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**kwargs)
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return _create_twins('twins_pcpvt_small', pretrained=pretrained, **model_kwargs)
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return _create_twins_pcpvt('twins_pcpvt_small', pretrained=pretrained, **model_kwargs)
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@register_model
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@register_model
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def twins_pcpvt_base(pretrained=False, **kwargs):
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def twins_pcpvt_base(pretrained=False, **kwargs):
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model_kwargs = dict(
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model_kwargs = dict(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
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depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
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**kwargs)
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return _create_twins('twins_pcpvt_base', pretrained=pretrained, **model_kwargs)
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return _create_twins_pcpvt('twins_pcpvt_base', pretrained=pretrained, **model_kwargs)
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@register_model
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@register_model
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def twins_pcpvt_large(pretrained=False, **kwargs):
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def twins_pcpvt_large(pretrained=False, **kwargs):
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model_kwargs = dict(
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model_kwargs = dict(
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], qkv_bias=True,
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patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
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norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
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depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], **kwargs)
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**kwargs)
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return _create_twins('twins_pcpvt_large', pretrained=pretrained, **model_kwargs)
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return _create_twins_pcpvt('twins_pcpvt_large', pretrained=pretrained, **model_kwargs)
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@register_model
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@register_model
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def twins_svt_small(pretrained=False, **kwargs):
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def twins_svt_small(pretrained=False, **kwargs):
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model_kwargs = dict(
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model_kwargs = dict(
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patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
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patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4],
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norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
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depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
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**kwargs)
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return _create_twins('twins_svt_small', pretrained=pretrained, **model_kwargs)
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return _create_twins_svt('twins_svt_small', pretrained=pretrained, **model_kwargs)
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@register_model
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@register_model
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def twins_svt_base(pretrained=False, **kwargs):
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def twins_svt_base(pretrained=False, **kwargs):
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model_kwargs = dict(
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model_kwargs = dict(
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patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4], qkv_bias=True,
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patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4],
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norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
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depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
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**kwargs)
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return _create_twins('twins_svt_base', pretrained=pretrained, **model_kwargs)
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return _create_twins_svt('twins_svt_base', pretrained=pretrained, **model_kwargs)
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@register_model
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@register_model
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|
def twins_svt_large(pretrained=False, **kwargs):
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|
def twins_svt_large(pretrained=False, **kwargs):
|
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|
|
model_kwargs = dict(
|
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|
model_kwargs = dict(
|
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|
patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32], mlp_ratios=[4, 4, 4, 4],
|
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patch_size=4, embed_dims=[128, 256, 512, 1024], num_heads=[4, 8, 16, 32], mlp_ratios=[4, 4, 4, 4],
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qkv_bias=True,
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|
depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs)
|
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|
norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1],
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|
return _create_twins('twins_svt_large', pretrained=pretrained, **model_kwargs)
|
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|
**kwargs)
|
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|
return _create_twins_svt('twins_svt_large', pretrained=pretrained, **model_kwargs)
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