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"""
Adapted from ImageReward (https://github.com/THUDM/ImageReward)
"""
import os
import torch
import torch.nn as nn
from PIL import Image
# from .config import cyclereward_args
from blip.blip_pretrain import blip_pretrain
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from huggingface_hub import PyTorchModelHubMixin
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
cyclereward_args = {
'blip_path': 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large.pth',
'vit': 'large',
'image_size': 224,
'mlp_dim': 768
}
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 CycleReward(nn.Module, PyTorchModelHubMixin):
def __init__(self, device='cpu',
model_type='CycleReward-Combo',
max_length=128,
fix_rate=0.7,
med_config=None,
):
super().__init__()
self.device = device
self.model_type = model_type
self.max_length = max_length
self.blip = blip_pretrain(
pretrained=cyclereward_args['blip_path'],
med_config=med_config,
image_size=cyclereward_args['image_size'],
vit=cyclereward_args['vit']
)
self.preprocess = _transform(cyclereward_args['image_size'])
self.mlp = MLP(cyclereward_args['mlp_dim'])
for name, parms in self.blip.named_parameters():
if '_proj' in name:
parms.requires_grad_(False)
# fix certain ratio of layers (setting from ImageReward)
self.image_layer_num = 24 if cyclereward_args['vit'] == 'large' else 12
if fix_rate > 0:
text_fix_num = "layer.{}".format(int(12 * fix_rate))
image_fix_num = "blocks.{}".format(int(self.image_layer_num * fix_rate))
for name, parms in self.blip.text_encoder.named_parameters():
parms.requires_grad_(False)
if text_fix_num in name:
break
for name, parms in self.blip.visual_encoder.named_parameters():
parms.requires_grad_(False)
if image_fix_num in name:
break
def forward(self, batch):
if 'Combo' in self.model_type:
text_reward = self.text_reward(batch)
image_reward = self.image_reward(batch)
elif 'I2T' in self.model_type:
text_reward = self.text_reward(batch)
image_reward = None
elif 'T2I' in self.model_type:
text_reward = None
image_reward = self.image_reward(batch)
return text_reward, image_reward
def text_reward(self, batch):
images, preferred_ids, preferred_mask, rejected_ids, rejected_mask = batch["images"], batch["preferred_ids"], batch["preferred_mask"], batch["rejected_ids"], batch["rejected_mask"]
images = images.to(self.device)
preferred_ids = preferred_ids.to(self.device)
preferred_mask = preferred_mask.to(self.device)
rejected_ids = rejected_ids.to(self.device)
rejected_mask = rejected_mask.to(self.device)
# encode image
image_embeds = self.blip.visual_encoder(images)
image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(self.device)
# encode preferred
preferred_embeds = self.blip.text_encoder(
preferred_ids,
attention_mask=preferred_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
).last_hidden_state
preferred_embeds = preferred_embeds[:,0,:].float()
# encode rejected
rejected_embeds = self.blip.text_encoder(
rejected_ids,
attention_mask=rejected_mask,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
).last_hidden_state
rejected_embeds = rejected_embeds[:,0,:].float()
preferred_reward = self.mlp(preferred_embeds)
rejected_reward = self.mlp(rejected_embeds)
reward = torch.concat((preferred_reward, rejected_reward), dim=1)
return reward
def image_reward(self, batch):
prompt_ids, prompt_mask, image_preferred, image_rejected = batch["prompt_ids"], batch["prompt_mask"], batch["image_preferred"], batch["image_rejected"]
image_preferred = image_preferred.to(self.device)
image_rejected = image_rejected.to(self.device)
prompt_ids = prompt_ids.view(prompt_ids.shape[0], -1).to(self.device)
prompt_mask = prompt_mask.view(prompt_mask.shape[0], -1).to(self.device)
# encode image
image_embeds_preferred = self.blip.visual_encoder(image_preferred)
image_atts_preferred = torch.ones(image_embeds_preferred.size()[:-1],dtype=torch.long).to(self.device)
image_embeds_rejected = self.blip.visual_encoder(image_rejected)
image_atts_rejected = torch.ones(image_embeds_rejected.size()[:-1],dtype=torch.long).to(self.device)
# encode preferred
preferred_embeds = self.blip.text_encoder(
prompt_ids,
attention_mask=prompt_mask,
encoder_hidden_states=image_embeds_preferred,
encoder_attention_mask=image_atts_preferred,
return_dict=True,
).last_hidden_state
preferred_embeds = preferred_embeds[:,0,:].float()
# encode rejected
rejected_embeds = self.blip.text_encoder(
prompt_ids,
attention_mask=prompt_mask,
encoder_hidden_states=image_embeds_rejected,
encoder_attention_mask=image_atts_rejected,
return_dict=True,
).last_hidden_state
rejected_embeds = rejected_embeds[:,0,:].float()
preferred_reward = self.mlp(preferred_embeds)
rejected_reward = self.mlp(rejected_embeds)
reward = torch.concat((preferred_reward, rejected_reward), dim=1)
return reward
@torch.no_grad()
def score(self, image, prompt):
text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=self.max_length, 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_embeds = 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,
).last_hidden_state
text_embeds = text_embeds[:,0,:].float()
rewards = self.mlp(text_embeds)
return rewards
class MLP(nn.Module):
def __init__(self, input_size):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_size, 1024),
nn.GELU(),
nn.Linear(1024, 128),
nn.GELU(),
nn.Linear(128, 64),
nn.GELU(),
nn.Linear(64, 16),
nn.GELU(),
nn.Linear(16, 1)
)
def init_weights(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
self.layers.apply(init_weights)
def forward(self, input):
return self.layers(input)
device='cuda'
model = CycleReward.from_pretrained("carolineec/CycleReward-Combo")
model.to(device)
model.eval()
preprocess = model.preprocess
image_path = "cat.jpg"
caption = "a photo of a cat"
image = preprocess(Image.open(image_path)).unsqueeze(0).to(device)
print('prepared data')
score = model.score(image, caption)
print('my score:', score.item())
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