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# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torchvision
import torch.nn as nn
from einops import rearrange
from models.sigclip import SigLIPViTBackbone
from models.dino import DinoViTBackbone
class ShallowDeepSiglipDinoEncoder(nn.Module):
def __init__(self, siglip_config={}, dino_config={}):
super().__init__()
self.to_pil = torchvision.transforms.ToPILImage()
self.image_encoder_siglip = SigLIPViTBackbone(**siglip_config)
self.image_encoder_dino = DinoViTBackbone(**dino_config)
def forward(self, image_tensor, device="cpu"):
bs = image_tensor.size(0)
# tensor 转 PIL
pixel_values = []
for image_tensor_i in image_tensor:
pixel_values.append(self.to_pil(image_tensor_i))
embeddings = []
embeddings_siglip_list = self.image_encoder_siglip(pixel_values, device)
embeddings_dino_list = self.image_encoder_dino(pixel_values, device)
for embeddings_siglip_i, embeddings_dino_i in zip(embeddings_siglip_list, embeddings_dino_list):
embeddings_i = torch.cat([embeddings_siglip_i, embeddings_dino_i], dim=-1) # channel concat
embeddings.append(embeddings_i)
return embeddings
# The default is to use double the image size, i.e., 768x768.
class ShallowDeepPatchfySiglipDinoEncoder(nn.Module):
def __init__(self, siglip_config={}, dino_config={}, patchfy_scale=2, default_image_size=384):
super().__init__()
self.to_pil = torchvision.transforms.ToPILImage()
self.image_encoder_siglip = SigLIPViTBackbone(**siglip_config)
self.image_encoder_dino = DinoViTBackbone(**dino_config)
self.patchfy = (patchfy_scale > 1)
self.patchfy_scale = patchfy_scale
self.default_image_size = default_image_size
def forward(self, image_tensor, device="cpu", **kwargs): # input image size = 768
image_tensor = image_tensor["image_ref"] # this is a dict
bs = image_tensor.size(0)
if self.patchfy:
image_local = rearrange(image_tensor, "b c (h hp) (w wp) -> (b hp wp) c h w", hp=self.patchfy_scale, wp=self.patchfy_scale)
image_global = torch.nn.functional.interpolate(image_tensor, size=(self.default_image_size, self.default_image_size), mode='bilinear', align_corners=True)
# tensor 转 PIL
pixel_values_local, pixel_values_global = [], []
for image_tensor_i in image_local:
pixel_values_local.append(self.to_pil(image_tensor_i.to(torch.float)))
for image_tensor_i in image_global:
pixel_values_global.append(self.to_pil(image_tensor_i.to(torch.float)))
embeddings = []
embeddings_siglip_list = self.image_encoder_siglip(pixel_values_global, device)
embeddings_dino_list = self.image_encoder_dino(pixel_values_global, device)
for embeddings_siglip_i, embeddings_dino_i in zip(embeddings_siglip_list, embeddings_dino_list):
embeddings_i = torch.cat([embeddings_siglip_i, embeddings_dino_i], dim=-1) # channel concat
embeddings.append(embeddings_i)
embeddings_local_siglip_deep = self.image_encoder_siglip(pixel_values_local, device)[-1]
embeddings_local_dino_deep = self.image_encoder_dino(pixel_values_local, device)[-1]
embeddings_local_deep = torch.cat([embeddings_local_siglip_deep, embeddings_local_dino_deep], dim=-1)
embeddings_local_deep = rearrange(embeddings_local_deep, "(b hp wp) l c -> b (l hp wp) c", hp=self.patchfy_scale, wp=self.patchfy_scale)
embeddings.append(embeddings_local_deep)
else:
# tensor 转 PIL
pixel_values = []
for image_tensor_i in image_tensor:
pixel_values.append(self.to_pil(image_tensor_i))
embeddings = []
embeddings_siglip_list = self.image_encoder_siglip(pixel_values, device)
embeddings_dino_list = self.image_encoder_dino(pixel_values, device)
for embeddings_siglip_i, embeddings_dino_i in zip(embeddings_siglip_list, embeddings_dino_list):
# 逐层concat的方式
embeddings_i = torch.cat([embeddings_siglip_i, embeddings_dino_i], dim=-1) # channel concat
embeddings.append(embeddings_i)
if len(embeddings) == 1:
embeddings = embeddings[0]
return embeddings
class ShallowDeepPatchfySiglipDinoEncoder_v2(nn.Module):
def __init__(self, siglip_config={}, dino_config={}, patchfy_scale=2, default_image_size=384):
super().__init__()
self.to_pil = torchvision.transforms.ToPILImage()
self.image_encoder_siglip = SigLIPViTBackbone(**siglip_config)
self.image_encoder_dino = DinoViTBackbone(**dino_config)
self.patchfy = (patchfy_scale > 1)
self.patchfy_scale = patchfy_scale
self.default_image_size = default_image_size
def forward(self, image_tensor_dict, device="cpu", **kwargs): # input image size = 768
image_tensor = image_tensor_dict["image_ref"]
bs = image_tensor.size(0)
if self.patchfy:
image_local = rearrange(image_tensor, "b c (h hp) (w wp) -> (b hp wp) c h w", hp=self.patchfy_scale, wp=self.patchfy_scale)
image_global = torch.nn.functional.interpolate(image_tensor, size=(self.default_image_size, self.default_image_size), mode='bilinear', align_corners=True)
pixel_values_local, pixel_values_global = [], []
for image_tensor_i in image_local:
pixel_values_local.append(self.to_pil(image_tensor_i.to(torch.float32)))
for image_tensor_i in image_global:
pixel_values_global.append(self.to_pil(image_tensor_i.to(torch.float32)))
embeddings = []
embeddings_siglip_list = self.image_encoder_siglip(pixel_values_global, device)
embeddings_dino_list = self.image_encoder_dino(pixel_values_global, device)
for embeddings_siglip_i, embeddings_dino_i in zip(embeddings_siglip_list, embeddings_dino_list):
embeddings_i = torch.cat([embeddings_siglip_i, embeddings_dino_i], dim=-1) # channel concat
embeddings.append(embeddings_i)
embeddings_local_siglip_deep = self.image_encoder_siglip(pixel_values_local, device)[-1]
embeddings_local_dino_deep = self.image_encoder_dino(pixel_values_local, device)[-1]
embeddings_local_deep = torch.cat([embeddings_local_siglip_deep, embeddings_local_dino_deep], dim=-1)
embeddings_local_deep = rearrange(embeddings_local_deep, "(b hp wp) l c -> b (l hp wp) c", hp=self.patchfy_scale, wp=self.patchfy_scale)
embeddings.append(embeddings_local_deep)
else:
# tensor 转 PIL
pixel_values = []
for image_tensor_i in image_tensor:
pixel_values.append(self.to_pil(image_tensor_i))
embeddings = []
embeddings_siglip_list = self.image_encoder_siglip(pixel_values, device)
embeddings_dino_list = self.image_encoder_dino(pixel_values, device)
for embeddings_siglip_i, embeddings_dino_i in zip(embeddings_siglip_list, embeddings_dino_list):
embeddings_i = torch.cat([embeddings_siglip_i, embeddings_dino_i], dim=-1) # channel concat
embeddings.append(embeddings_i)
if len(embeddings) == 1:
embeddings = embeddings[0]
return embeddings
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