From be99eef9c14fe63a2ebf3cdd2784d16140851004 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Thu, 20 May 2021 23:38:35 -0700 Subject: [PATCH] Remove redundant code, cleanup, fix torchscript. --- timm/models/twins.py | 495 +++++++++++++------------------------------ 1 file changed, 149 insertions(+), 346 deletions(-) diff --git a/timm/models/twins.py b/timm/models/twins.py index 27be4cba..ce51c497 100644 --- a/timm/models/twins.py +++ b/timm/models/twins.py @@ -11,11 +11,9 @@ Code/weights from https://github.com/Meituan-AutoML/Twins, original copyright/li # Licensed under The Apache 2.0 License [see LICENSE for details] # Written by Xinjie Li, Xiangxiang Chu # -------------------------------------------------------- - -import logging import math from copy import deepcopy -from typing import Optional +from typing import Optional, Tuple import torch import torch.nn as nn @@ -25,13 +23,9 @@ from functools import partial from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .layers import Mlp, DropPath, to_2tuple, trunc_normal_ from .registry import register_model -from .vision_transformer import _cfg -from .vision_transformer import Block as TimmBlock -from .vision_transformer import Attention as TimmAttention +from .vision_transformer import Attention from .helpers import build_model_with_cfg, overlay_external_default_cfg -from .vision_transformer import checkpoint_filter_fn, _init_vit_weights -_logger = logging.getLogger(__name__) def _cfg(url='', **kwargs): return { @@ -43,6 +37,7 @@ def _cfg(url='', **kwargs): **kwargs } + default_cfgs = { 'twins_pcpvt_small': _cfg( url='https://s3plus.meituan.net/v1/mss_9240d97c6bf34ab1b78859c3c2a2a3e4/automl-model-zoo/models/twins/pcpvt_small.pth', @@ -64,78 +59,34 @@ default_cfgs = { ), } +Size_ = Tuple[int, int] -class GroupAttention(nn.Module): - """ - LSA: self attention within a group +class LocallyGroupedAttn(nn.Module): + """ LSA: self attention within a group """ - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., ws=1): + def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., ws=1): assert ws != 1 - super(GroupAttention, self).__init__() + super(LocallyGroupedAttn, self).__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 + self.scale = head_dim ** -0.5 - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.qkv = nn.Linear(dim, dim * 3, bias=True) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.ws = ws - def forward(self, x, H, W): - """ - There are two implementations for this function, zero padding or mask. We don't observe obvious difference for - both. You can choose any one, we recommend forward_padding because it's neat. However, - the masking implementation is more reasonable and accurate. - Args: - x: - H: - W: - - Returns: - - """ - return self.forward_padding(x, H, W) - - def forward_mask(self, x, H, W): - B, N, C = x.shape - x = x.view(B, H, W, C) - pad_l = pad_t = 0 - pad_r = (self.ws - W % self.ws) % self.ws - pad_b = (self.ws - H % self.ws) % self.ws - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - _h, _w = Hp // self.ws, Wp // self.ws - mask = torch.zeros((1, Hp, Wp), device=x.device) - mask[:, -pad_b:, :].fill_(1) - mask[:, :, -pad_r:].fill_(1) - - x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) # B, _h, _w, ws, ws, C - mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h*_w, self.ws*self.ws) - attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3) # 1, _h*_w, ws*ws, ws*ws - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0)) - qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads, - C // self.num_heads).permute(3, 0, 1, 4, 2, 5) # n_h, B, _w*_h, nhead, ws*ws, dim - q, k, v = qkv[0], qkv[1], qkv[2] # B, _h*_w, n_head, ws*ws, dim_head - attn = (q @ k.transpose(-2, -1)) * self.scale # B, _h*_w, n_head, ws*ws, ws*ws - attn = attn + attn_mask.unsqueeze(2) - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) # attn @v -> B, _h*_w, n_head, ws*ws, dim_head - attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C) - x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C) - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - x = x.reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def forward_padding(self, x, H, W): + def forward(self, x, size: Size_): + # There are two implementations for this function, zero padding or mask. We don't observe obvious difference for + # both. You can choose any one, we recommend forward_padding because it's neat. However, + # the masking implementation is more reasonable and accurate. B, N, C = x.shape + H, W = size x = x.view(B, H, W, C) pad_l = pad_t = 0 pad_r = (self.ws - W % self.ws) % self.ws @@ -144,8 +95,8 @@ class GroupAttention(nn.Module): _, Hp, Wp, _ = x.shape _h, _w = Hp // self.ws, Wp // self.ws x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) - qkv = self.qkv(x).reshape(B, _h * _w, self.ws * self.ws, 3, self.num_heads, - C // self.num_heads).permute(3, 0, 1, 4, 2, 5) + qkv = self.qkv(x).reshape( + B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) q, k, v = qkv[0], qkv[1], qkv[2] attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) @@ -159,22 +110,56 @@ class GroupAttention(nn.Module): x = self.proj_drop(x) return x - -class Attention(nn.Module): - """ - GSA: using a key to summarize the information for a group to be efficient. + # def forward_mask(self, x, size: Size_): + # B, N, C = x.shape + # H, W = size + # x = x.view(B, H, W, C) + # pad_l = pad_t = 0 + # pad_r = (self.ws - W % self.ws) % self.ws + # pad_b = (self.ws - H % self.ws) % self.ws + # x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) + # _, Hp, Wp, _ = x.shape + # _h, _w = Hp // self.ws, Wp // self.ws + # mask = torch.zeros((1, Hp, Wp), device=x.device) + # mask[:, -pad_b:, :].fill_(1) + # mask[:, :, -pad_r:].fill_(1) + # + # x = x.reshape(B, _h, self.ws, _w, self.ws, C).transpose(2, 3) # B, _h, _w, ws, ws, C + # mask = mask.reshape(1, _h, self.ws, _w, self.ws).transpose(2, 3).reshape(1, _h * _w, self.ws * self.ws) + # attn_mask = mask.unsqueeze(2) - mask.unsqueeze(3) # 1, _h*_w, ws*ws, ws*ws + # attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-1000.0)).masked_fill(attn_mask == 0, float(0.0)) + # qkv = self.qkv(x).reshape( + # B, _h * _w, self.ws * self.ws, 3, self.num_heads, C // self.num_heads).permute(3, 0, 1, 4, 2, 5) + # # n_h, B, _w*_h, nhead, ws*ws, dim + # q, k, v = qkv[0], qkv[1], qkv[2] # B, _h*_w, n_head, ws*ws, dim_head + # attn = (q @ k.transpose(-2, -1)) * self.scale # B, _h*_w, n_head, ws*ws, ws*ws + # attn = attn + attn_mask.unsqueeze(2) + # attn = attn.softmax(dim=-1) + # attn = self.attn_drop(attn) # attn @v -> B, _h*_w, n_head, ws*ws, dim_head + # attn = (attn @ v).transpose(2, 3).reshape(B, _h, _w, self.ws, self.ws, C) + # x = attn.transpose(2, 3).reshape(B, _h * self.ws, _w * self.ws, C) + # if pad_r > 0 or pad_b > 0: + # x = x[:, :H, :W, :].contiguous() + # x = x.reshape(B, N, C) + # x = self.proj(x) + # x = self.proj_drop(x) + # return x + + +class GlobalSubSampleAttn(nn.Module): + """ GSA: using a key to summarize the information for a group to be efficient. """ - def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1): + def __init__(self, dim, num_heads=8, attn_drop=0., proj_drop=0., sr_ratio=1): super().__init__() assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 + self.scale = head_dim ** -0.5 - self.q = nn.Linear(dim, dim, bias=qkv_bias) - self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) + self.q = nn.Linear(dim, dim, bias=True) + self.kv = nn.Linear(dim, dim * 2, bias=True) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) @@ -183,18 +168,19 @@ class Attention(nn.Module): if sr_ratio > 1: self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) self.norm = nn.LayerNorm(dim) + else: + self.sr = None + self.norm = None - def forward(self, x, H, W): + def forward(self, x, size: Size_): B, N, C = x.shape q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - if self.sr_ratio > 1: - x_ = x.permute(0, 2, 1).reshape(B, C, H, W) - x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) - x_ = self.norm(x_) - kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - else: - kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + if self.sr is not None: + x = x.permute(0, 2, 1).reshape(B, C, *size) + x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1) + x = self.norm(x) + kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) k, v = kv[0], kv[1] attn = (q @ k.transpose(-2, -1)) * self.scale @@ -210,52 +196,46 @@ class Attention(nn.Module): class Block(nn.Module): - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1): + def __init__(self, dim, num_heads, mlp_ratio=4., drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=None): super().__init__() self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, - attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) + if ws is None: + self.attn = Attention(dim, num_heads, False, None, attn_drop, drop) + elif ws == 1: + self.attn = GlobalSubSampleAttn(dim, num_heads, attn_drop, drop, sr_ratio) + else: + self.attn = LocallyGroupedAttn(dim, num_heads, attn_drop, drop, ws) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - def forward(self, x, H, W): - x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + def forward(self, x, size: Size_): + x = x + self.drop_path(self.attn(self.norm1(x), size)) x = x + self.drop_path(self.mlp(self.norm2(x))) - return x -class SBlock(TimmBlock): - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1): - super(SBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, - drop_path, act_layer, norm_layer) - - def forward(self, x, H, W): - return super(SBlock, self).forward(x) - - -class GroupBlock(TimmBlock): - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1, ws=1): - super(GroupBlock, self).__init__(dim, num_heads, mlp_ratio, qkv_bias, qk_scale, drop, attn_drop, - drop_path, act_layer, norm_layer) - del self.attn - if ws == 1: - self.attn = Attention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, sr_ratio) - else: - self.attn = GroupAttention(dim, num_heads, qkv_bias, qk_scale, attn_drop, drop, ws) +class PosConv(nn.Module): + # PEG from https://arxiv.org/abs/2102.10882 + def __init__(self, in_chans, embed_dim=768, stride=1): + super(PosConv, self).__init__() + self.proj = nn.Sequential(nn.Conv2d(in_chans, embed_dim, 3, stride, 1, bias=True, groups=embed_dim), ) + self.stride = stride - def forward(self, x, H, W): - x = x + self.drop_path(self.attn(self.norm1(x), H, W)) - x = x + self.drop_path(self.mlp(self.norm2(x))) + def forward(self, x, size: Size_): + B, N, C = x.shape + 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 + def no_weight_decay(self): + return ['proj.%d.weight' % i for i in range(4)] + class PatchEmbed(nn.Module): """ 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): super().__init__() - # img_size = to_2tuple(img_size) + img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_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.norm = nn.LayerNorm(embed_dim) - def forward(self, x): + def forward(self, x) -> Tuple[torch.Tensor, Size_]: B, C, H, W = x.shape x = self.proj(x).flatten(2).transpose(1, 2) x = self.norm(x) - H, W = H // self.patch_size[0], W // self.patch_size[1] + 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 PyramidVisionTransformer(nn.Module): - def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], - num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., - attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, - depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], block_cls=Block): +class Twins(nn.Module): + # Adapted from PVT https://github.com/whai362/PVT.git + 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), drop_rate=0., attn_drop_rate=0., drop_path_rate=0., + 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__() self.num_classes = num_classes self.depths = depths - # patch_embed + img_size = to_2tuple(img_size) + prev_chs = in_chans self.patch_embeds = nn.ModuleList() - self.pos_embeds = nn.ParameterList() self.pos_drops = nn.ModuleList() - self.blocks = nn.ModuleList() - for i in range(len(depths)): - if i == 0: - self.patch_embeds.append(PatchEmbed(img_size, patch_size, in_chans, embed_dims[i])) - else: - self.patch_embeds.append( - # PatchEmbed(img_size // patch_size // 2 ** (i - 1), 2, embed_dims[i - 1], embed_dims[i]) - PatchEmbed((img_size[0] // patch_size // 2**(i-1),img_size[1] // patch_size // 2**(i-1)), 2, embed_dims[i - 1], embed_dims[i]) - ) - patch_num = self.patch_embeds[-1].num_patches + 1 if i == len(embed_dims) - 1 else self.patch_embeds[ - -1].num_patches - self.pos_embeds.append(nn.Parameter(torch.zeros(1, patch_num, embed_dims[i]))) + self.patch_embeds.append(PatchEmbed(img_size, patch_size, prev_chs, embed_dims[i])) 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 cur = 0 for k in range(len(depths)): _block = nn.ModuleList([block_cls( - dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], qkv_bias=qkv_bias, - qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[k]) - for i in range(depths[k])]) + dim=embed_dims[k], num_heads=num_heads[k], mlp_ratio=mlp_ratios[k], 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 wss is None or i % 2 == 1 else wss[k]) for i in range(depths[k])]) self.blocks.append(_block) 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.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[-1])) + self.norm = norm_layer(embed_dims[-1]) # classification head self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity() # init weights - for pos_emb in self.pos_embeds: - trunc_normal_(pos_emb, std=.02) self.apply(self._init_weights) - def reset_drop_path(self, drop_path_rate): - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] - cur = 0 - for k in range(len(self.depths)): - for i in range(self.depths[k]): - self.blocks[k][i].drop_path.drop_prob = dpr[cur + i] - cur += self.depths[k] - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - @torch.jit.ignore def no_weight_decay(self): - return {'cls_token'} + return set(['pos_block.' + n for n, p in self.pos_block.named_parameters()]) def get_classifier(self): return self.head @@ -367,76 +319,7 @@ class PyramidVisionTransformer(nn.Module): 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: @@ -454,98 +337,28 @@ class CPVTV2(PyramidVisionTransformer): 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) + for i, (embed, drop, blocks, pos_blk) in enumerate( + zip(self.patch_embeds, self.pos_drops, self.blocks, self.pos_block)): + x, size = embed(x) + x = drop(x) + for j, blk in enumerate(blocks): + x = blk(x, size) 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: - 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) - 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 -def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs): +def _create_twins(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) @@ -558,11 +371,10 @@ def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = build_model_with_cfg( - CPVTV2, variant, pretrained, + Twins, variant, pretrained, default_cfg=default_cfg, img_size=img_size, num_classes=num_classes, - pretrained_filter_fn=checkpoint_filter_fn, **kwargs) return model @@ -571,55 +383,46 @@ def _create_twins_pcpvt(variant, pretrained=False, default_cfg=None, **kwargs): @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) + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('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) + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('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) + patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], + depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('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) + patch_size=4, embed_dims=[64, 128, 256, 512], num_heads=[2, 4, 8, 16], mlp_ratios=[4, 4, 4, 4], + depths=[2, 2, 10, 4], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('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) + patch_size=4, embed_dims=[96, 192, 384, 768], num_heads=[3, 6, 12, 24], mlp_ratios=[4, 4, 4, 4], + depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('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) + depths=[2, 2, 18, 2], wss=[7, 7, 7, 7], sr_ratios=[8, 4, 2, 1], **kwargs) + return _create_twins('twins_svt_large', pretrained=pretrained, **model_kwargs)