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import random
import torch
from torch import nn
import numpy as np
import re
from einops import rearrange
from dataclasses import dataclass
from torchvision import transforms
from transformers import CLIPTokenizer, CLIPImageProcessor
from transformers import AutoImageProcessor
from transformers import T5EncoderModel, T5Tokenizer, AutoTokenizer
from transformers.utils import ModelOutput
from typing import Iterable, Optional, Union, List
import craftsman
from craftsman.utils.typing import *
from .clip.modeling_clip import CLIPModel
from .clip.modeling_conditional_clip import ConditionalCLIPModel
from .base import BaseEmbedder, ImageType
from .dino_v2.modeling_dinov2 import Dinov2Model
from .dino_v2.modeling_conditional_dinov2 import ConditionalDinov2Model
@dataclass
class CLIPEmbedOutput(ModelOutput):
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
embeds: torch.FloatTensor = None
class DINOEmbedOutput(ModelOutput):
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
@craftsman.register("cond-embedder")
class CondEmbedder(BaseEmbedder):
@dataclass
class Config(BaseEmbedder.Config):
pretrained_model_name_or_path: Optional[str] = None # the pretrained model name or path for condition model
pretrained_clip_name_or_path: Optional[str] = None # the pretrained model name or path for clip
pretrained_dino_name_or_path: Optional[str] = None # the pretrained model name or path for dino
pretrained_linear_proj: Optional[str] = None
freeze_modulation_clip: bool = False
freeze_modulation_dino: bool = False
config_path: str = ''
enable_gradient_checkpointing: bool = False
embeds_fusion_mode: int = 1 # 0: sum | 1: concat
linear_proj_init: str = "constant"
text_max_length: int = 77
image_size_clip: int = 224
image_size_dino: int = 224
cfg: Config
def configure(self) -> None:
super().configure()
# Load the CLIP model and processor
if not self.cfg.encode_camera:
if self.cfg.pretrained_clip_name_or_path is not None:
self.clip_model: CLIPModel = CLIPModel.from_pretrained(self.cfg.pretrained_clip_name_or_path)
else:
self.clip_model: CLIPModel = CLIPModel(config=ConditionalCLIPModel.config_class.from_pretrained(
"openai/clip-vit-large-patch14",
))
if self.cfg.pretrained_dino_name_or_path is not None:
self.dino_model: Dinov2Model = Dinov2Model.from_pretrained(self.cfg.pretrained_dino_name_or_path)
else:
self.dino_model: Dinov2Model = Dinov2Model(config=ConditionalDinov2Model.config_class.from_pretrained(
"facebook/dinov2-base",
))
else:
if self.cfg.pretrained_clip_name_or_path == '':
assert self.cfg.config_path is not None, "The config path should be provided"
conditional_clip_config = ConditionalCLIPModel.config_class.from_json_file(self.cfg.config_path)
conditional_clip_config.vision_config.modulation_dim = self.cfg.camera_embeds_dim
self.clip_model: CLIPModel = ConditionalCLIPModel(conditional_clip_config)
else:
# clip
conditional_clip_config = ConditionalCLIPModel.config_class.from_pretrained(
self.cfg.pretrained_clip_name_or_path,
)
conditional_clip_config.vision_config.modulation_dim = self.cfg.camera_embeds_dim
self.clip_model: CLIPModel = ConditionalCLIPModel.from_pretrained(
self.cfg.pretrained_clip_name_or_path,
vision_config=conditional_clip_config.vision_config
)
# dino
conditional_vit_config = ConditionalDinov2Model.config_class.from_pretrained(
self.cfg.pretrained_dino_name_or_path,
)
conditional_vit_config.modulation_dim = self.cfg.camera_embeds_dim
self.dino_model: ConditionalDinov2Model = ConditionalDinov2Model.from_pretrained(
self.cfg.pretrained_dino_name_or_path,
config=conditional_vit_config
)
self.image_preprocess_clip = CLIPImageProcessor()
self.image_preprocess_dino = AutoImageProcessor.from_pretrained(
self.cfg.pretrained_dino_name_or_path if self.cfg.pretrained_dino_name_or_path is not None else "facebook/dinov2-base",
)
self.transform_clip= transforms.Compose(
[
transforms.Resize(self.cfg.image_size_clip, transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.CenterCrop(self.cfg.image_size_clip), # crop a (224, 224) square
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711],
),
]
)
self.transform_dino = transforms.Compose(
[
transforms.Resize(self.cfg.image_size_dino, transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.CenterCrop(self.cfg.image_size_dino), # crop a (224, 224) square
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
]
)
if self.cfg.enable_gradient_checkpointing:
self.dino_model.encoder.gradient_checkpointing = True
if self.cfg.zero_uncond_embeds:
self.empty_image_embeds_clip = torch.zeros((self.cfg.n_views, 257, 1024)).detach()
self.empty_image_embeds_dino = torch.zeros((self.cfg.n_views, 257, 1024)).detach()
self.empty_image_embeds = torch.cat([self.empty_image_embeds_clip, self.empty_image_embeds_dino], dim=1)
else:
if self.cfg.encode_camera:
self.empty_image_embeds_clip = self.encode_image_clip(torch.zeros(self.cfg.n_views, self.cfg.image_size_clip, self.cfg.image_size_clip, 3), self.cameras[:self.cfg.n_views]).detach()
self.empty_image_embeds_dino = self.encode_image_dino(torch.zeros(self.cfg.n_views, self.cfg.image_size_clip, self.cfg.image_size_clip, 3), self.cameras[:self.cfg.n_views]).detach()
self.empty_image_embeds = torch.cat([self.empty_image_embeds_clip, self.empty_image_embeds_dino], dim=1)
else:
self.empty_image_embeds_clip = self.encode_image_clip(torch.zeros(self.cfg.n_views, self.cfg.image_size_dino, self.cfg.image_size_dino, 3)).detach()
self.empty_image_embeds_dino = self.encode_image_dino(torch.zeros(self.cfg.n_views, self.cfg.image_size_dino, self.cfg.image_size_dino, 3)).detach()
self.empty_image_embeds = torch.cat([self.empty_image_embeds_clip, self.empty_image_embeds_dino], dim=1)
# Freeze the clip model parameters
self.clip_model.eval()
for k, p in self.clip_model.named_parameters():
ks = k.split('.')
if 'mod_norm1' in ks or 'mod_norm2' in ks and not self.cfg.freeze_modulation_clip:
p.requires_grad_(not self.cfg.freeze_modulation_clip)
else:
p.requires_grad_(False)
# freeze the dino model parameters
self.dino_model.eval()
for k, p in self.dino_model.named_parameters():
ks = k.split('.')
if 'mod_norm1' in ks or 'mod_norm2' in ks and not self.cfg.freeze_modulation_dino:
p.requires_grad_(not self.cfg.freeze_modulation_dino)
else:
p.requires_grad_(False)
self.linear_proj = nn.Linear(768, 1024, bias=False)
if self.cfg.linear_proj_init == "constant":
nn.init.constant_(self.linear_proj.weight, 0)
elif self.cfg.linear_proj_init == "xavier":
nn.init.xavier_uniform_(self.linear_proj.weight)
else:
raise ValueError
if self.cfg.pretrained_model_name_or_path is not None:
print(f"Loading ckpt from {self.cfg.pretrained_model_name_or_path}")
ckpt = torch.load(self.cfg.pretrained_model_name_or_path, map_location="cpu")['state_dict']
pretrained_model_ckpt = {}
for k, v in ckpt.items():
if k.startswith('condition.'):
pretrained_model_ckpt[k.replace('condition.', '')] = v
self.load_state_dict(pretrained_model_ckpt, strict=False)
def encode_image_clip(self, images: Iterable[Optional[ImageType]], cameras: Optional[torch.Tensor] = None, force_none_camera_embeds: bool = False, return_dict: bool = False, **kwargs) -> torch.FloatTensor:
camera_embeds = None
if isinstance(images, (np.ndarray, torch.Tensor)): # for training process
assert images.min() >= 0.0 and images.max() <= 1.0, "The pixel values should be in the range of [0, 1]"
do_rescale = False
if self.cfg.encode_camera:
assert cameras is not None, "The cameras should be provided"
camera_embeds = self.encode_camera(cameras)
pixel_values = self.transform_clip(images.permute(0, 3, 1, 2))
else: # for inference process
do_rescale = True
if self.cfg.encode_camera:
if cameras is None:
bs = len(images) // self.cfg.n_views
cameras = self.cameras[:self.cfg.n_views].repeat(bs, 1, 1).to(self.clip_model.device)
camera_embeds = self.encode_camera(cameras)
pixel_values = self.image_preprocess_clip.preprocess(images, return_tensors='pt', do_rescale=do_rescale).pixel_values
if force_none_camera_embeds:
camera_embeds = None
if pixel_values.ndim == 4:
pixel_values = pixel_values.unsqueeze(1)
if camera_embeds is not None:
camera_embeds = camera_embeds.unsqueeze(1)
if self.cfg.encode_camera and camera_embeds is not None:
vision_outputs = self.clip_model.vision_model(
pixel_values=rearrange(pixel_values.to(self.clip_model.device), "B N C H W -> (B N) C H W"),
condition=rearrange(camera_embeds, "B N C -> (B N) C")
)
else:
vision_outputs = self.clip_model.vision_model(
pixel_values=rearrange(pixel_values.to(self.clip_model.device), "B N C H W -> (B N) C H W"),
)
if return_dict:
# clip
pooler_output = vision_outputs[1] # pooled_output
image_features = self.clip_model.visual_projection(pooler_output)
clip_embeds = vision_outputs.last_hidden_state
clip_embeds_dict = CLIPEmbedOutput(
last_hidden_state=clip_embeds,
pooler_output=pooler_output,
embeds=image_features
)
return clip_embeds_dict
else:
return vision_outputs.last_hidden_state
def encode_image_dino(self, images: Iterable[Optional[ImageType]], cameras: Optional[torch.Tensor] = None, force_none_camera_embeds: bool = False, return_dict: bool = False, **kwargs) -> torch.FloatTensor:
camera_embeds = None
if isinstance(images, (np.ndarray, torch.Tensor)): # for training process
assert images.min() >= 0.0 and images.max() <= 1.0, "The pixel values should be in the range of [0, 1]"
do_rescale = False
if self.cfg.encode_camera:
assert cameras is not None, "The cameras should be provided"
camera_embeds = self.encode_camera(cameras)
pixel_values = self.transform_dino(images.permute(0, 3, 1, 2))
else: # for inference process
do_rescale = True
if self.cfg.encode_camera:
if cameras is None:
bs = len(images) // self.cfg.n_views
cameras = self.cameras[:self.cfg.n_views].repeat(bs, 1, 1).to(self.dino_model.device)
camera_embeds = self.encode_camera(cameras)
pixel_values = self.image_preprocess_dino.preprocess(images, return_tensors='pt', do_rescale=do_rescale).pixel_values
if force_none_camera_embeds:
camera_embeds = None
if pixel_values.ndim == 4:
pixel_values = pixel_values.unsqueeze(1)
if camera_embeds is not None:
camera_embeds = camera_embeds.unsqueeze(1)
if self.cfg.encode_camera and camera_embeds is not None:
vision_outputs = self.dino_model(
rearrange(pixel_values.to(self.dino_model.device), "B N C H W -> (B N) C H W"),
condition=rearrange(camera_embeds, "B N C -> (B N) C"),
)
else:
vision_outputs = self.dino_model(
rearrange(pixel_values.to(self.dino_model.device), "B N C H W -> (B N) C H W"),
)
if return_dict:
# dino
dino_embeds_dict = DINOEmbedOutput(
last_hidden_state=vision_outputs.last_hidden_state,
pooler_output=vision_outputs.pooler_output,
)
return dino_embeds_dict
else:
return vision_outputs.last_hidden_state
def encode_image(self, images: Iterable[Optional[ImageType]], cameras: Optional[torch.Tensor] = None, force_none_camera_embeds: bool = False, return_dict: bool = False, **kwargs) -> torch.FloatTensor:
clip_embeds = self.encode_image_clip(images, cameras)
dino_embeds = self.encode_image_dino(images, cameras)
dino_embeds = self.linear_proj(dino_embeds)
visual_embeds = torch.cat([clip_embeds, dino_embeds], dim=1)
return visual_embeds
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