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)