File size: 6,492 Bytes
8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 8a8582e f664757 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
from abc import abstractmethod
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
from torch import Tensor
from torch.nn import functional as F
from torchvision.transforms import v2 as T
from .common import ensure_tuple
from .vit import VisionTransformer, vit_base_dreamsim
class DreamsimBackbone(ModelMixin, ConfigMixin):
@abstractmethod
def forward_features(self, x: Tensor) -> Tensor:
raise NotImplementedError("abstract base class was called ;_;")
def forward(self, x: Tensor) -> Tensor:
"""Dreamsim forward pass for similarity computation.
Args:
x (Tensor): Input tensor of shape [2, B, 3, H, W].
Returns:
sim (torch.Tensor): dreamsim similarity score of shape [B].
"""
inputs = x.view(-1, 3, *x.shape[-2:])
x = self.forward_features(inputs).view(*x.shape[:2], -1)
return 1 - F.cosine_similarity(x[0], x[1], dim=1)
def compile(self, *args, **kwargs):
"""Compile the model with Inductor. This is a no-op unless overridden by a subclass."""
return self
class DreamsimModel(DreamsimBackbone):
@register_to_config
def __init__(
self,
image_size: int = 224,
patch_size: int = 16,
layer_norm_eps: float = 1e-6,
pre_norm: bool = False,
act_layer: str = "gelu",
img_mean: tuple[float, float, float] = (0.485, 0.456, 0.406),
img_std: tuple[float, float, float] = (0.229, 0.224, 0.225),
do_resize: bool = False,
) -> None:
super().__init__()
self.image_size = ensure_tuple(image_size, 2)
self.patch_size = ensure_tuple(patch_size, 2)
self.layer_norm_eps = layer_norm_eps
self.pre_norm = pre_norm
self.do_resize = do_resize
self.img_mean = img_mean
self.img_std = img_std
num_classes = 512 if self.pre_norm else 0
self.extractor: VisionTransformer = vit_base_dreamsim(
image_size=image_size,
patch_size=patch_size,
layer_norm_eps=layer_norm_eps,
num_classes=num_classes,
pre_norm=pre_norm,
act_layer=act_layer,
)
self.resize = T.Resize(
self.image_size,
interpolation=T.InterpolationMode.BICUBIC,
antialias=True,
)
self.img_norm = T.Normalize(mean=self.img_mean, std=self.img_std)
def compile(self, *, mode: str = "reduce-overhead", force: bool = False, **kwargs):
if (not self._compiled) or force:
self.extractor = torch.compile(self.extractor, mode=mode, **kwargs)
self._compiled = True
return self
def transforms(self, x: Tensor) -> Tensor:
if self.do_resize:
x = self.resize(x)
return self.img_norm(x)
def forward_features(self, x: Tensor) -> Tensor:
if x.ndim == 3:
x = x.unsqueeze(0)
x = self.transforms(x)
x = self.extractor.forward(x, norm=self.pre_norm)
x = x.div(x.norm(dim=1, keepdim=True))
x = x.sub(x.mean(dim=1, keepdim=True))
return x
class DreamsimEnsemble(DreamsimBackbone):
@register_to_config
def __init__(
self,
image_size: int = 224,
patch_size: int = 16,
layer_norm_eps: float | tuple[float, ...] = (1e-6, 1e-5, 1e-5),
num_classes: int | tuple[int, ...] = (0, 512, 512),
do_resize: bool = False,
) -> None:
super().__init__()
if isinstance(layer_norm_eps, float):
layer_norm_eps = (layer_norm_eps,) * 3
if isinstance(num_classes, int):
num_classes = (num_classes,) * 3
self.image_size = ensure_tuple(image_size, 2)
self.patch_size = ensure_tuple(patch_size, 2)
self.do_resize = do_resize
self.dino: VisionTransformer = vit_base_dreamsim(
image_size=self.image_size,
patch_size=self.patch_size,
layer_norm_eps=layer_norm_eps[0],
num_classes=num_classes[0],
pre_norm=False,
act_layer="gelu",
)
self.clip1: VisionTransformer = vit_base_dreamsim(
image_size=self.image_size,
patch_size=self.patch_size,
layer_norm_eps=layer_norm_eps[1],
num_classes=num_classes[1],
pre_norm=True,
act_layer="quick_gelu",
)
self.clip2: VisionTransformer = vit_base_dreamsim(
image_size=self.image_size,
patch_size=self.patch_size,
layer_norm_eps=layer_norm_eps[2],
num_classes=num_classes[2],
pre_norm=True,
act_layer="gelu",
)
self.resize = T.Resize(
self.image_size,
interpolation=T.InterpolationMode.BICUBIC,
antialias=True,
)
self.dino_norm = T.Normalize(
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
)
self.clip_norm = T.Normalize(
mean=(0.48145466, 0.4578275, 0.40821073),
std=(0.26862954, 0.26130258, 0.27577711),
)
self._compiled = False
def compile(self, *, mode: str = "reduce-overhead", force: bool = False, **kwargs):
if (not self._compiled) or force:
self.dino = torch.compile(self.dino, mode=mode, **kwargs)
self.clip1 = torch.compile(self.clip1, mode=mode, **kwargs)
self.clip2 = torch.compile(self.clip2, mode=mode, **kwargs)
self._compiled = True
return self
def transforms(self, x: Tensor, resize: bool = False) -> tuple[Tensor, Tensor, Tensor]:
if resize:
x = self.resize(x)
x = self.dino_norm(x), self.clip_norm(x), self.clip_norm(x)
return x
def forward_features(self, x: Tensor) -> Tensor:
if x.ndim == 3:
x = x.unsqueeze(0)
x_dino, x_clip1, x_clip2 = self.transforms(x, self.do_resize)
# these expect to always receive a batch, and will return a batch
x_dino = self.dino.forward(x_dino, norm=False)
x_clip1 = self.clip1.forward(x_clip1, norm=True)
x_clip2 = self.clip2.forward(x_clip2, norm=True)
z: Tensor = torch.cat([x_dino, x_clip1, x_clip2], dim=1)
z = z.div(z.norm(dim=1, keepdim=True))
z = z.sub(z.mean(dim=1, keepdim=True))
return z
|