Merge branch 'feature/crossvit' of https://github.com/chunfuchen/pytorch-image-models into chunfuchen-feature/crossvit
Ross Wightman
3 years ago
commit
a897e0ebcc
@ -0,0 +1,461 @@
|
||||
""" CrossViT Model
|
||||
|
||||
@inproceedings{
|
||||
chen2021crossvit,
|
||||
title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
|
||||
author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
|
||||
booktitle={International Conference on Computer Vision (ICCV)},
|
||||
year={2021}
|
||||
}
|
||||
|
||||
Paper link: https://arxiv.org/abs/2103.14899
|
||||
Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
|
||||
"""
|
||||
|
||||
# Copyright IBM All Rights Reserved.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
|
||||
"""
|
||||
Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
|
||||
|
||||
"""
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.hub
|
||||
from functools import partial
|
||||
from typing import List
|
||||
|
||||
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
||||
from .helpers import build_model_with_cfg
|
||||
from .layers import DropPath, to_2tuple, trunc_normal_
|
||||
from .registry import register_model
|
||||
from .vision_transformer import Mlp, Block
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None,
|
||||
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
|
||||
# 'first_conv': 'patch_embed.proj',
|
||||
'classifier': 'head',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = {
|
||||
'crossvit_15_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'),
|
||||
'crossvit_15_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth'),
|
||||
'crossvit_15_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth'),
|
||||
'crossvit_18_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
|
||||
'crossvit_18_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth'),
|
||||
'crossvit_18_dagger_384': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth'),
|
||||
'crossvit_9_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
|
||||
'crossvit_9_dagger_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth'),
|
||||
'crossvit_base_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'),
|
||||
'crossvit_small_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'),
|
||||
'crossvit_tiny_224': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_tiny_224.pth'),
|
||||
}
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, multi_conv=False):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.num_patches = num_patches
|
||||
if multi_conv:
|
||||
if patch_size[0] == 12:
|
||||
self.proj = nn.Sequential(
|
||||
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=3, padding=0),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=1, padding=1),
|
||||
)
|
||||
elif patch_size[0] == 16:
|
||||
self.proj = nn.Sequential(
|
||||
nn.Conv2d(in_chans, embed_dim // 4, kernel_size=7, stride=4, padding=3),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 4, embed_dim // 2, kernel_size=3, stride=2, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=3, stride=2, padding=1),
|
||||
)
|
||||
else:
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
# FIXME look at relaxing size constraints
|
||||
assert H == self.img_size[0] and W == self.img_size[1], \
|
||||
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
||||
x = self.proj(x).flatten(2).transpose(1, 2)
|
||||
return x
|
||||
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.wq = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.wk = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.wv = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
B, N, C = x.shape
|
||||
q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # B1C -> B1H(C/H) -> BH1(C/H)
|
||||
k = self.wk(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # BNC -> BNH(C/H) -> BHN(C/H)
|
||||
v = self.wv(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) # BNC -> BNH(C/H) -> BHN(C/H)
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, 1, C) # (BH1N @ BHN(C/H)) -> BH1(C/H) -> B1H(C/H) -> B1C
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class CrossAttentionBlock(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):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = CrossAttention(
|
||||
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
x = x[:, 0:1, ...] + self.drop_path(self.attn(self.norm1(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class MultiScaleBlock(nn.Module):
|
||||
|
||||
def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
|
||||
num_branches = len(dim)
|
||||
self.num_branches = num_branches
|
||||
# different branch could have different embedding size, the first one is the base
|
||||
self.blocks = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
tmp = []
|
||||
for i in range(depth[d]):
|
||||
tmp.append(
|
||||
Block(dim=dim[d], num_heads=num_heads[d], mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[i], norm_layer=norm_layer))
|
||||
if len(tmp) != 0:
|
||||
self.blocks.append(nn.Sequential(*tmp))
|
||||
|
||||
if len(self.blocks) == 0:
|
||||
self.blocks = None
|
||||
|
||||
self.projs = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
if dim[d] == dim[(d+1) % num_branches] and False:
|
||||
tmp = [nn.Identity()]
|
||||
else:
|
||||
tmp = [norm_layer(dim[d]), act_layer(), nn.Linear(dim[d], dim[(d+1) % num_branches])]
|
||||
self.projs.append(nn.Sequential(*tmp))
|
||||
|
||||
self.fusion = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
d_ = (d+1) % num_branches
|
||||
nh = num_heads[d_]
|
||||
if depth[-1] == 0: # backward capability:
|
||||
self.fusion.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer))
|
||||
else:
|
||||
tmp = []
|
||||
for _ in range(depth[-1]):
|
||||
tmp.append(CrossAttentionBlock(dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop, attn_drop=attn_drop, drop_path=drop_path[-1], norm_layer=norm_layer))
|
||||
self.fusion.append(nn.Sequential(*tmp))
|
||||
|
||||
self.revert_projs = nn.ModuleList()
|
||||
for d in range(num_branches):
|
||||
if dim[(d+1) % num_branches] == dim[d] and False:
|
||||
tmp = [nn.Identity()]
|
||||
else:
|
||||
tmp = [norm_layer(dim[(d+1) % num_branches]), act_layer(), nn.Linear(dim[(d+1) % num_branches], dim[d])]
|
||||
self.revert_projs.append(nn.Sequential(*tmp))
|
||||
|
||||
def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
|
||||
|
||||
outs_b = []
|
||||
for i, block in enumerate(self.blocks):
|
||||
outs_b.append(block(x[i]))
|
||||
|
||||
# only take the cls token out
|
||||
proj_cls_token = torch.jit.annotate(List[torch.Tensor], [])
|
||||
for i, proj in enumerate(self.projs):
|
||||
proj_cls_token.append(proj(outs_b[i][:, 0:1, ...]))
|
||||
|
||||
# cross attention
|
||||
outs = []
|
||||
for i, (fusion, revert_proj) in enumerate(zip(self.fusion, self.revert_projs)):
|
||||
tmp = torch.cat((proj_cls_token[i], outs_b[(i + 1) % self.num_branches][:, 1:, ...]), dim=1)
|
||||
tmp = fusion(tmp)
|
||||
reverted_proj_cls_token = revert_proj(tmp[:, 0:1, ...])
|
||||
tmp = torch.cat((reverted_proj_cls_token, outs_b[i][:, 1:, ...]), dim=1)
|
||||
outs.append(tmp)
|
||||
return outs
|
||||
|
||||
|
||||
def _compute_num_patches(img_size, patches):
|
||||
return [i // p * i // p for i, p in zip(img_size,patches)]
|
||||
|
||||
|
||||
class CrossViT(nn.Module):
|
||||
""" Vision Transformer with support for patch or hybrid CNN input stage
|
||||
"""
|
||||
def __init__(self, img_size=(224, 224), patch_size=(8, 16), in_chans=3, num_classes=1000, embed_dim=(192, 384), depth=([1, 3, 1], [1, 3, 1], [1, 3, 1]),
|
||||
num_heads=(6, 12), mlp_ratio=(2., 2., 4.), qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
|
||||
drop_path_rate=0., norm_layer=nn.LayerNorm, multi_conv=False):
|
||||
super().__init__()
|
||||
|
||||
self.num_classes = num_classes
|
||||
if not isinstance(img_size, list):
|
||||
img_size = to_2tuple(img_size)
|
||||
self.img_size = img_size
|
||||
|
||||
num_patches = _compute_num_patches(img_size, patch_size)
|
||||
self.num_branches = len(patch_size)
|
||||
self.embed_dim = embed_dim
|
||||
self.num_features = embed_dim[0] # to pass the tests
|
||||
self.patch_embed = nn.ModuleList()
|
||||
|
||||
# hard-coded for torch jit script
|
||||
for i in range(self.num_branches):
|
||||
setattr(self, f'pos_embed_{i}', nn.Parameter(torch.zeros(1, 1 + num_patches[i], embed_dim[i])))
|
||||
setattr(self, f'cls_token_{i}', nn.Parameter(torch.zeros(1, 1, embed_dim[i])))
|
||||
|
||||
for im_s, p, d in zip(img_size, patch_size, embed_dim):
|
||||
self.patch_embed.append(PatchEmbed(img_size=im_s, patch_size=p, in_chans=in_chans, embed_dim=d, multi_conv=multi_conv))
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
total_depth = sum([sum(x[-2:]) for x in depth])
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, total_depth)] # stochastic depth decay rule
|
||||
dpr_ptr = 0
|
||||
self.blocks = nn.ModuleList()
|
||||
for idx, block_cfg in enumerate(depth):
|
||||
curr_depth = max(block_cfg[:-1]) + block_cfg[-1]
|
||||
dpr_ = dpr[dpr_ptr:dpr_ptr + curr_depth]
|
||||
blk = MultiScaleBlock(embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr_,
|
||||
norm_layer=norm_layer)
|
||||
dpr_ptr += curr_depth
|
||||
self.blocks.append(blk)
|
||||
|
||||
self.norm = nn.ModuleList([norm_layer(embed_dim[i]) for i in range(self.num_branches)])
|
||||
self.head = nn.ModuleList([nn.Linear(embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in range(self.num_branches)])
|
||||
|
||||
for i in range(self.num_branches):
|
||||
if hasattr(self, f'pos_embed_{i}'):
|
||||
trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
|
||||
trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
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):
|
||||
out = {'cls_token'}
|
||||
if self.pos_embed[0].requires_grad:
|
||||
out.add('pos_embed')
|
||||
return out
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.ModuleList([nn.Linear(self.embed_dim[i], num_classes) if num_classes > 0 else nn.Identity() for i in range(self.num_branches)])
|
||||
|
||||
def forward_features(self, x):
|
||||
B, C, H, W = x.shape
|
||||
xs = []
|
||||
for i, patch_embed in enumerate(self.patch_embed):
|
||||
x_ = torch.nn.functional.interpolate(x, size=(self.img_size[i], self.img_size[i]), mode='bicubic') if H != self.img_size[i] else x
|
||||
tmp = patch_embed(x_)
|
||||
cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script
|
||||
cls_tokens = cls_tokens.expand(B, -1, -1)
|
||||
tmp = torch.cat((cls_tokens, tmp), dim=1)
|
||||
pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1 # hard-coded for torch jit script
|
||||
tmp = tmp + pos_embed
|
||||
tmp = self.pos_drop(tmp)
|
||||
xs.append(tmp)
|
||||
|
||||
for i, blk in enumerate(self.blocks):
|
||||
xs = blk(xs)
|
||||
|
||||
# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
|
||||
xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
|
||||
out = [x[:, 0] for x in xs]
|
||||
|
||||
return out
|
||||
|
||||
def forward(self, x):
|
||||
xs = self.forward_features(x)
|
||||
ce_logits = [head(xs[i]) for i, head in enumerate(self.head)]
|
||||
ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
|
||||
return ce_logits
|
||||
|
||||
|
||||
def _create_crossvit(variant, pretrained=False, **kwargs):
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError('features_only not implemented for Vision Transformer models.')
|
||||
|
||||
def pretrained_filter_fn(state_dict):
|
||||
new_state_dict = {}
|
||||
for key in state_dict.keys():
|
||||
if 'pos_embed' in key or 'cls_token' in key:
|
||||
new_key = key.replace(".", "_")
|
||||
else:
|
||||
new_key = key
|
||||
new_state_dict[new_key] = state_dict[key]
|
||||
return new_state_dict
|
||||
|
||||
return build_model_with_cfg(
|
||||
CrossViT, variant, pretrained,
|
||||
default_cfg=default_cfgs[variant],
|
||||
pretrained_filter_fn=pretrained_filter_fn,
|
||||
**kwargs)
|
||||
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_tiny_224(pretrained=False, **kwargs):
|
||||
model_args = dict(
|
||||
img_size=[240, 224], patch_size=[12, 16], embed_dim=[96, 192], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[3, 3], mlp_ratio=[4, 4, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_tiny_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_small_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[4, 4, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_small_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_base_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[384, 768], depth=[[1, 4, 0], [1, 4, 0], [1, 4, 0]],
|
||||
num_heads=[12, 12], mlp_ratio=[4, 4, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_base_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_15_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_18_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def crossvit_9_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[128, 256], depth=[[1, 3, 0], [1, 3, 0], [1, 3, 0]],
|
||||
num_heads=[4, 4], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_9_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_15_dagger_384(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[408, 384],
|
||||
patch_size=[12, 16], embed_dim=[192, 384], depth=[[1, 5, 0], [1, 5, 0], [1, 5, 0]],
|
||||
num_heads=[6, 6], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_15_dagger_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_224(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[240, 224],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_224', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
|
||||
@register_model
|
||||
def crossvit_18_dagger_384(pretrained=False, **kwargs):
|
||||
model_args = dict(img_size=[408, 384],
|
||||
patch_size=[12, 16], embed_dim=[224, 448], depth=[[1, 6, 0], [1, 6, 0], [1, 6, 0]],
|
||||
num_heads=[7, 7], mlp_ratio=[3, 3, 1], qkv_bias=True,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=True, **kwargs)
|
||||
model = _create_crossvit(variant='crossvit_18_dagger_384', pretrained=pretrained, **model_args)
|
||||
return model
|
||||
Loading…
Reference in new issue