李鑫杰
3 years ago
parent
e7f0db8664
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00548b8427
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""" Twins
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A PyTorch impl of : `Twins: Revisiting the Design of Spatial Attention in Vision Transformers`
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- https://arxiv.org/pdf/2104.13840.pdf
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Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/license info below
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"""
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# --------------------------------------------------------
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# Twins
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# Copyright (c) 2021 Meituan
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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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# --------------------------------------------------------
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import logging
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import math
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from copy import deepcopy
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from functools import partial
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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 .registry import register_model
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from .vision_transformer import _cfg
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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 .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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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', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'patch_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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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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),
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'twins_pcpvt_base': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_base.pth',
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),
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'twins_pcpvt_large': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_large.pth',
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),
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'twins_svt_small': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_small.pth',
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),
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'twins_svt_base': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_base.pth',
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),
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'twins_svt_large': _cfg(
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url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/alt_gvt_large.pth',
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),
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}
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class GroupAttention(nn.Module):
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"""
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LSA: self attention within a group
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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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assert ws != 1
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super(GroupAttention, 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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self.dim = dim
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.ws = ws
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def forward(self, x, H, W):
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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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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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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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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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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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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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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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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 Attention(nn.Module):
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"""
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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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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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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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self.dim = dim
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, dim, bias=qkv_bias)
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self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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self.sr_ratio = sr_ratio
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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.norm = nn.LayerNorm(dim)
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def forward(self, x, H, W):
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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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if self.sr_ratio > 1:
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
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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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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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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class Block(nn.Module):
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
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attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(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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x = x + self.drop_path(self.attn(self.norm1(x), H, W))
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x = x + self.drop_path(self.mlp(self.norm2(x)))
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return x
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class SBlock(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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drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
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super(SBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop,
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drop_path, act_layer, norm_layer)
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def forward(self, x, H, W):
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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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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,
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drop_path, act_layer, norm_layer)
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del self.attn
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if ws == 1:
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self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, sr_ratio)
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else:
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self.attn = GroupAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, ws)
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def forward(self, x, H, W):
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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.mlp(self.norm2(x)))
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return x
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class PatchEmbed(nn.Module):
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""" Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
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super().__init__()
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# img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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self.img_size = img_size
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self.patch_size = patch_size
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assert img_size[0] % patch_size[0] == 0 and img_size[1] % patch_size[1] == 0, \
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f"img_size {img_size} should be divided by patch_size {patch_size}."
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self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
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self.num_patches = self.H * self.W
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
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self.norm = nn.LayerNorm(embed_dim)
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def forward(self, x):
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B, C, H, W = x.shape
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x = self.proj(x).flatten(2).transpose(1, 2)
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x = self.norm(x)
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H, W = H // self.patch_size[0], W // self.patch_size[1]
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return x, (H, W)
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# borrow from PVT https://github.com/whai362/PVT.git
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class PyramidVisionTransformer(nn.Module):
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def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
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num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
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attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
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depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=Block):
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super().__init__()
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self.num_classes = num_classes
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self.depths = depths
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# patch_embed
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self.patch_embeds = nn.ModuleList()
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self.pos_embeds = nn.ParameterList()
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self.pos_drops = nn.ModuleList()
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self.blocks = nn.ModuleList()
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for i in range(len(depths)):
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if i == 0:
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self.patch_embeds.append(PatchEmbed(img_size, patch_size, in_chans, embed_dims[i]))
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else:
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self.patch_embeds.append(
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# PatchEmbed(img_size // patch_size // 2 ** (i - 1), 2, embed_dims[i - 1], embed_dims[i])
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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])
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)
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patch_num = self.patch_embeds[-1].num_patches + 1 if i == len(embed_dims) - 1 else self.patch_embeds[
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-1].num_patches
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self.pos_embeds.append(nn.Parameter(torch.zeros(1, patch_num, embed_dims[i])))
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self.pos_drops.append(nn.Dropout(p=drop_rate))
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
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cur = 0
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for k in range(len(depths)):
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_block = nn.ModuleList([block_cls(
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dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
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sr_ratio=sr_ratios[k])
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for i in range(depths[k])])
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self.blocks.append(_block)
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cur += depths[k]
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self.norm = norm_layer(embed_dims[-1])
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# cls_token
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self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[-1]))
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# classification head
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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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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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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)
|
||||
|
||||
@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)
|
||||
Loading…
Reference in new issue