Spaces:
Running
on
L40S
Running
on
L40S
File size: 8,729 Bytes
4bf9661 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
import os
import torch
from PIL import Image
from typing import List, Union
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from .BLIP.blip_pretrain import BLIP_Pretrain
from torchvision.transforms import InterpolationMode
from safetensors.torch import load_file
from .config import MODEL_PATHS
BICUBIC = InterpolationMode.BICUBIC
def _convert_image_to_rgb(image):
return image.convert("RGB")
def _transform(n_px):
return Compose([
Resize(n_px, interpolation=BICUBIC),
CenterCrop(n_px),
_convert_image_to_rgb,
ToTensor(),
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
class MLP(torch.nn.Module):
def __init__(self, input_size):
super().__init__()
self.input_size = input_size
self.layers = torch.nn.Sequential(
torch.nn.Linear(self.input_size, 1024),
#nn.ReLU(),
torch.nn.Dropout(0.2),
torch.nn.Linear(1024, 128),
#nn.ReLU(),
torch.nn.Dropout(0.2),
torch.nn.Linear(128, 64),
#nn.ReLU(),
torch.nn.Dropout(0.1),
torch.nn.Linear(64, 16),
#nn.ReLU(),
torch.nn.Linear(16, 1)
)
# initial MLP param
for name, param in self.layers.named_parameters():
if 'weight' in name:
torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1))
if 'bias' in name:
torch.nn.init.constant_(param, val=0)
def forward(self, input):
return self.layers(input)
class ImageReward(torch.nn.Module):
def __init__(self, med_config, device='cpu', bert_model_path=""):
super().__init__()
self.device = device
self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path)
self.preprocess = _transform(224)
self.mlp = MLP(768)
self.mean = 0.16717362830052426
self.std = 1.0333394966054072
def score_grad(self, prompt_ids, prompt_attention_mask, image):
"""Calculate the score with gradient for a single image and prompt.
Args:
prompt_ids (torch.Tensor): Tokenized prompt IDs.
prompt_attention_mask (torch.Tensor): Attention mask for the prompt.
image (torch.Tensor): The processed image tensor.
Returns:
torch.Tensor: The reward score.
"""
image_embeds = self.blip.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
text_output = self.blip.text_encoder(
prompt_ids,
attention_mask=prompt_attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
txt_features = text_output.last_hidden_state[:, 0, :]
rewards = self.mlp(txt_features)
rewards = (rewards - self.mean) / self.std
return rewards
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]:
"""Score the images based on the prompt.
Args:
prompt (str): The prompt text.
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
Returns:
List[float]: List of scores for the images.
"""
if isinstance(images, (str, Image.Image)):
# Single image
if isinstance(images, str):
pil_image = Image.open(images)
else:
pil_image = images
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
return [self._calculate_score(prompt, image).item()]
elif isinstance(images, list):
# Multiple images
scores = []
for one_image in images:
if isinstance(one_image, str):
pil_image = Image.open(one_image)
elif isinstance(one_image, Image.Image):
pil_image = one_image
else:
raise TypeError("The type of parameter images is illegal.")
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
scores.append(self._calculate_score(prompt, image).item())
return scores
else:
raise TypeError("The type of parameter images is illegal.")
def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor:
"""Calculate the score for a single image and prompt.
Args:
prompt (str): The prompt text.
image (torch.Tensor): The processed image tensor.
Returns:
torch.Tensor: The reward score.
"""
text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
image_embeds = self.blip.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
text_output = self.blip.text_encoder(
text_input.input_ids,
attention_mask=text_input.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
txt_features = text_output.last_hidden_state[:, 0, :].float()
rewards = self.mlp(txt_features)
rewards = (rewards - self.mean) / self.std
return rewards
def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple:
"""Rank the images based on the prompt.
Args:
prompt (str): The prompt text.
generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images.
Returns:
tuple: (indices, rewards) where indices are the ranks and rewards are the scores.
"""
text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
txt_set = []
for generation in generations_list:
if isinstance(generation, str):
pil_image = Image.open(generation)
elif isinstance(generation, Image.Image):
pil_image = generation
else:
raise TypeError("The type of parameter generations_list is illegal.")
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
image_embeds = self.blip.visual_encoder(image)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
text_output = self.blip.text_encoder(
text_input.input_ids,
attention_mask=text_input.attention_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
txt_set.append(text_output.last_hidden_state[:, 0, :])
txt_features = torch.cat(txt_set, 0).float()
rewards = self.mlp(txt_features)
rewards = (rewards - self.mean) / self.std
rewards = torch.squeeze(rewards)
_, rank = torch.sort(rewards, dim=0, descending=True)
_, indices = torch.sort(rank, dim=0)
indices = indices + 1
return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist()
class ImageRewardScore(torch.nn.Module):
def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
super().__init__()
self.device = device if isinstance(device, torch.device) else torch.device(device)
model_path = path.get("imagereward")
med_config = path.get("med_config")
state_dict = load_file(model_path)
self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device)
self.model.load_state_dict(state_dict, strict=False)
self.model.eval()
@torch.no_grad()
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
"""Score the images based on the prompt.
Args:
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
prompt (str): The prompt text.
Returns:
List[float]: List of scores for the images.
"""
return self.model.score(images, prompt)
|