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from logging import getLogger
from typing import Tuple
import torch
from torch import nn
from torch.nn import functional as F
from timm.models import register_model
from timm.models import vision_transformer as tvit
from timm.models import convnext as tconv
from einops import rearrange
from . import extra_timm_models as et
class Fuser(nn.Module):
def __init__(self, src_dim: int, tgt_dim: int, gated: bool = True):
super().__init__()
self.gated = gated
mid_dim = max(src_dim, tgt_dim) * 2
self.fwd = nn.Sequential(
nn.Conv2d(src_dim, mid_dim, kernel_size=3, stride=1, padding=1),
nn.GELU(),
nn.Conv2d(mid_dim, tgt_dim * (2 if gated else 1), kernel_size=3, stride=1, padding=1),
)
def forward(self, src: torch.Tensor, tgt: torch.Tensor) -> torch.Tensor:
if src.ndim == 3:
shape = tgt.shape[-2:]
else:
shape = src.shape[-2:]
nd = shape[0] * shape[1]
if src.ndim == 3:
src = src[:, -nd:].reshape(src.shape[0], src.shape[2], *shape)
if tgt.ndim == 3:
tgt_pre = tgt[:, :-nd]
tgt = tgt[:, -nd:].reshape(tgt.shape[0], tgt.shape[2], *shape)
else:
tgt_pre = None
pred = self.fwd(src)
if self.gated:
g, pred = torch.chunk(pred, 2, dim=1)
g = F.sigmoid(g)
pred = g * pred
tgt = tgt + pred
if tgt_pre is not None:
tgt = rearrange(tgt, 'b c h w -> b (h w) c')
tgt = torch.cat([tgt_pre, tgt], dim=1)
return tgt
class AttnDownsample(nn.Module):
def __init__(self, dim: int, window_size: int, num_heads: int = 16):
super().__init__()
self.q = nn.Parameter(torch.randn(1, num_heads, 1, dim // num_heads) * 0.01)
self.kv = nn.Linear(dim, dim * 2)
self.proj = nn.Linear(dim, dim)
self.window_size = window_size
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
def forward(self, x: torch.Tensor, twod_shape: Tuple[int, int]) -> torch.Tensor:
ntok = twod_shape[0] * twod_shape[1]
x_pre = x[:, :-ntok]
B = x.shape[0]
ds_hw = tuple(s // self.window_size for s in twod_shape)
x_spat = rearrange(
x[:, -ntok:],
'b (h d1 w d2) c -> (b h w) (d1 d2) c',
h=ds_hw[0], w=ds_hw[1],
d1=self.window_size, d2=self.window_size,
)
B, N, C = x_spat.shape
k, v = self.kv(x_spat).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q = (self.q * self.scale).expand(B, -1, -1, -1)
attn = q @ k.transpose(-2, -1)
attn = F.softmax(attn, dim=-1)
x = attn @ v
x = x.transpose(1, 2).reshape(B, C)
x = self.proj(x)
x = rearrange(x, '(b h w) c -> b (h w) c', b=x_pre.shape[0], h=ds_hw[0], w=ds_hw[1])
x = torch.cat([x_pre, x], dim=1)
return x
class HybridModel(nn.Module):
def __init__(self, vit: tvit.VisionTransformer, conv: tconv.ConvNeXt, pretrained: bool = False,
concatenate: bool = False, **kwargs):
super().__init__()
self.conv = conv
self.vit = vit
self.concatenate = concatenate
conv.stages = nn.ModuleList(conv.stages)
vit.blocks = nn.ModuleList(vit.blocks)
self._half_vit_idx = len(vit.blocks) // 2 + 1
self._half_conv_idx = None
x = torch.empty(1, 3, 256, 256)
x = self.conv.stem(x)
for i in range(len(conv.stages)):
x = conv.stages[i](x)
if self._half_conv_idx is None and x.shape[-2:] == (16, 16):
self._half_conv_idx = i + 1
half_conv_dim = x.shape[1]
final_conv_dim = x.shape[1]
self.vit_to_conv_fusion = Fuser(vit.embed_dim, half_conv_dim)
self.conv_to_vit_fusion = Fuser(half_conv_dim, vit.embed_dim)
self.vit_ds = AttnDownsample(vit.embed_dim, window_size=2)
embed_dim = vit.embed_dim + (final_conv_dim if concatenate else 0)
if not concatenate:
self.final_fuse = Fuser(final_conv_dim, vit.embed_dim, gated=False)
self.final_block = tvit.Block(embed_dim, num_heads=16)
self.embed_dim = embed_dim
@property
def patch_size(self):
return 32
@property
def no_fsdp_wrap_types(self):
return {tvit.VisionTransformer, tconv.ConvNeXt}
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.forward_features(x)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
y_vit = self.vit.patch_generator(x)
for i in range(self._half_vit_idx):
y_vit = self.vit.blocks[i](y_vit)
y_conv = self.conv.stem(x)
for i in range(self._half_conv_idx):
y_conv = self.conv.stages[i](y_conv)
y_vit, y_conv = self.conv_to_vit_fusion(y_conv, y_vit), self.vit_to_conv_fusion(y_vit, y_conv)
y_vit = self.vit_ds(y_vit, y_conv.shape[-2:])
for i in range(self._half_vit_idx, len(self.vit.blocks)):
y_vit = self.vit.blocks[i](y_vit)
for i in range(self._half_conv_idx, len(self.conv.stages)):
y_conv = self.conv.stages[i](y_conv)
if self.concatenate:
y_conv = rearrange(y_conv, 'b c h w -> b (h w) c')
# Average pool across the board, and replicate for each cls/register token
conv_summary = y_conv.mean(dim=1, keepdim=True).expand(-1, self.vit.patch_generator.num_cls_patches, -1)
y_conv = torch.cat([conv_summary, y_conv], dim=1)
y = torch.cat([y_vit, y_conv], dim=2)
else:
y = self.final_fuse(y_conv, y_vit)
y = self.final_block(y)
summary = y[:, :self.vit.patch_generator.num_cls_tokens]
features = y[:, self.vit.patch_generator.num_cls_patches:]
return summary, features
@register_model
def hybrid_base(pretrained=False, concatenate: bool = False, weight_init: str = 'skip', **kwargs):
cfg = dict(num_classes=0, **kwargs)
conv = tconv.convnextv2_base(pretrained=pretrained, **cfg)
vit = tvit.vit_base_patch16_224(pretrained=pretrained, weight_init=weight_init, **cfg)
return HybridModel(vit, conv, pretrained, concatenate=concatenate)
@register_model
def hybrid_large(pretrained=False, concatenate: bool = False, weight_init: str = 'skip', **kwargs):
cfg = dict(num_classes=0, **kwargs)
conv = tconv.convnextv2_large(pretrained=pretrained, **cfg)
vit = tvit.vit_large_patch16_224(pretrained=pretrained, weight_init=weight_init, **cfg)
return HybridModel(vit, conv, pretrained, concatenate=concatenate)
@register_model
def hybrid_huge(pretrained=False, concatenate: bool = False, weight_init: str = 'skip', **kwargs):
cfg = dict(num_classes=0, **kwargs)
conv = tconv.convnextv2_huge(pretrained=pretrained, **cfg)
vit = et.vit_huge_patch16_224(pretrained=pretrained, weight_init=weight_init, **cfg)
return HybridModel(vit, conv, pretrained, concatenate=concatenate)
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