Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Post crossvit merge cleanup, change model names to reflect input size, cleanup img size vs scale handling, fix tests

Browse Source
pull/821/head
Ross Wightman 3 years ago
parent a897e0ebcc
commit f1808e0970
3 changed files with 157 additions and 122 deletions
Show Stats Download Patch File Download Diff File

2
tests/test_models.py
Unescape View File

@ -189,6 +189,8 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
input_tensor = torch.randn((batch_size, *input_size))
# test forward_features (always unpooled)
if 'crossvit' not in model_name:
# FIXME remove crossvit exception
outputs = model.forward_features(input_tensor)
if isinstance(outputs, tuple):
outputs = outputs[0]

4
timm/models/__init__.py
Unescape View File

@ -1,9 +1,9 @@
from .byoanet import *
from .byobnet import *
from .cait import *
from .crossvit import *
from .coat import *
from .convit import *
from .crossvit import *
from .cspnet import *
from .densenet import *
from .dla import *
@ -37,6 +37,7 @@ from .sknet import *
from .swin_transformer import *
from .tnt import *
from .tresnet import *
from .twins import *
from .vgg import *
from .visformer import *
from .vision_transformer import *
@ -45,7 +46,6 @@ from .vovnet import *
from .xception import *
from .xception_aligned import *
from .xcit import *
from .twins import *
from .factory import create_model, split_model_name, safe_model_name
from .helpers import load_checkpoint, resume_checkpoint, model_parameters

255
timm/models/crossvit.py
Unescape View File

@ -10,6 +10,8 @@
Paper link: https://arxiv.org/abs/2103.14899
Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
"""
# Copyright IBM All Rights Reserved.
@ -40,30 +42,49 @@ def _cfg(url='', **kwargs):
'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',
'first_conv': ('patch_embed.0.proj', 'patch_embed.1.proj'),
'classifier': ('head.0', 'head.1'),
**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'),
'crossvit_15_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_224.pth'),
'crossvit_15_dagger_240': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_224.pth',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_15_dagger_408': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_18_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
'crossvit_18_dagger_240': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_224.pth',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_18_dagger_408': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth',
input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_9_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
'crossvit_9_dagger_240': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_dagger_224.pth',
first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
),
'crossvit_base_240': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_base_224.pth'),
'crossvit_small_240': _cfg(
url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_small_224.pth'),
'crossvit_tiny_240': _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)
@ -117,11 +138,13 @@ class CrossAttention(nn.Module):
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)
# B1C -> B1H(C/H) -> BH1(C/H)
q = self.wq(x[:, 0:1, ...]).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# BNC -> BNH(C/H) -> BHN(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)
attn = (q @ k.transpose(-2, -1)) * self.scale # BH1(C/H) @ BH(C/H)N -> BH1N
attn = attn.softmax(dim=-1)
@ -152,7 +175,7 @@ class CrossAttentionBlock(nn.Module):
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.,
def __init__(self, dim, patches, depth, num_heads, mlp_ratio, qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
@ -163,8 +186,8 @@ class MultiScaleBlock(nn.Module):
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,
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))
@ -174,32 +197,36 @@ class MultiScaleBlock(nn.Module):
self.projs = nn.ModuleList()
for d in range(num_branches):
if dim[d] == dim[(d+1) % num_branches] and False:
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])]
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
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,
self.fusion.append(
CrossAttentionBlock(
dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
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,
tmp.append(CrossAttentionBlock(
dim=dim[d_], num_heads=nh, mlp_ratio=mlp_ratio[d], qkv_bias=qkv_bias,
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:
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])]
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]:
@ -225,23 +252,29 @@ class MultiScaleBlock(nn.Module):
def _compute_num_patches(img_size, patches):
return [i // p * i // p for i, p in zip(img_size,patches)]
return [i[0] // p * i[1] // 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):
def __init__(
self, img_size=224, img_scale=(1.0, 1.0), 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=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False
):
super().__init__()
self.num_classes = num_classes
if not isinstance(img_size, list):
if not isinstance(img_size, (tuple, list)):
img_size = to_2tuple(img_size)
self.img_size = img_size
num_patches = _compute_num_patches(img_size, patch_size)
if not isinstance(img_scale, (tuple, list)):
img_scale = to_2tuple(img_scale)
self.img_size_scaled = [tuple([int(sj * si) for sj in img_size]) for si in img_scale]
num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
self.num_branches = len(patch_size)
self.embed_dim = embed_dim
self.num_features = embed_dim[0] # to pass the tests
@ -252,8 +285,9 @@ class CrossViT(nn.Module):
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))
for im_s, p, d in zip(self.img_size_scaled, 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)
@ -264,14 +298,16 @@ class CrossViT(nn.Module):
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)
blk = MultiScaleBlock(
embed_dim, num_patches, block_cfg, num_heads=num_heads, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, 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)])
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}'):
@ -301,13 +337,16 @@ class CrossViT(nn.Module):
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)])
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
ss = self.img_size_scaled[i]
x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic') if H != ss[0] 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)
@ -322,13 +361,15 @@ class CrossViT(nn.Module):
# 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
return tuple([x[:, 0] for x in xs])
def forward(self, x):
xs = self.forward_features(x)
ce_logits = [head(xs[i]) for i, head in enumerate(self.head)]
if isinstance(self.head[0], nn.Identity):
# FIXME to pass current passthrough features tests, could use better approach
ce_logits = tuple(ce_logits)
else:
ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
return ce_logits
@ -354,108 +395,100 @@ def _create_crossvit(variant, pretrained=False, **kwargs):
**kwargs)
@register_model
def crossvit_tiny_224(pretrained=False, **kwargs):
def crossvit_tiny_240(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)
img_scale=(1.0, 224/240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_tiny_240', 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)
def crossvit_small_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_small_240', 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)
def crossvit_base_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_base_240', 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)
def crossvit_9_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_9_240', 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)
def crossvit_15_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_15_240', 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)
def crossvit_18_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224 / 240), 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], **kwargs)
model = _create_crossvit(variant='crossvit_18_240', 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)
def crossvit_9_dagger_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224 / 240), 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], multi_conv=True, **kwargs)
model = _create_crossvit(variant='crossvit_9_dagger_240', 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)
def crossvit_15_dagger_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], multi_conv=True, **kwargs)
model = _create_crossvit(variant='crossvit_15_dagger_240', 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)
def crossvit_15_dagger_408(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 384/408), 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], multi_conv=True, **kwargs)
model = _create_crossvit(variant='crossvit_15_dagger_408', 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)
def crossvit_18_dagger_240(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 224/240), 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], multi_conv=True, **kwargs)
model = _create_crossvit(variant='crossvit_18_dagger_240', 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)
def crossvit_18_dagger_408(pretrained=False, **kwargs):
model_args = dict(
img_scale=(1.0, 384/408), 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], multi_conv=True, **kwargs)
model = _create_crossvit(variant='crossvit_18_dagger_408', pretrained=pretrained, **model_args)
return model

Write Preview
Loading…
Cancel
Save