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