Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,406 Bytes
55ed985 eae4507 55ed985 eae4507 55ed985 cbd4574 55ed985 cbd4574 55ed985 |
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 |
import os
import clip
import numpy as np
import pytorch_lightning as pl
import spaces
import torch
import torch.nn as nn
from huggingface_hub import snapshot_download
from PIL import Image
class AestheticPredictor:
"""Aesthetic Score Predictor.
Args:
clip_model_dir (str): Path to the directory of the CLIP model.
sac_model_path (str): Path to the pre-trained SAC model.
device (str): Device to use for computation ("cuda" or "cpu").
"""
def __init__(self, clip_model_dir=None, sac_model_path=None, device="cpu"):
self.device = device
if clip_model_dir is None:
model_path = snapshot_download(
repo_id="xinjjj/RoboAssetGen", allow_patterns="aesthetic/*"
)
suffix = "aesthetic"
model_path = snapshot_download(
repo_id="xinjjj/RoboAssetGen", allow_patterns=f"{suffix}/*"
)
clip_model_dir = os.path.join(model_path, suffix)
if sac_model_path is None:
model_path = snapshot_download(
repo_id="xinjjj/RoboAssetGen", allow_patterns="aesthetic/*"
)
suffix = "aesthetic"
model_path = snapshot_download(
repo_id="xinjjj/RoboAssetGen", allow_patterns=f"{suffix}/*"
)
sac_model_path = os.path.join(
model_path, suffix, "sac+logos+ava1-l14-linearMSE.pth"
)
self.clip_model, self.preprocess = self._load_clip_model(
clip_model_dir
)
self.sac_model = self._load_sac_model(sac_model_path, input_size=768)
class MLP(pl.LightningModule): # noqa
def __init__(self, input_size):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_size, 1024),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.Linear(16, 1),
)
def forward(self, x):
return self.layers(x)
@staticmethod
def normalized(a, axis=-1, order=2):
"""Normalize the array to unit norm."""
l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
l2[l2 == 0] = 1
return a / np.expand_dims(l2, axis)
def _load_clip_model(self, model_dir: str, model_name: str = "ViT-L/14"):
"""Load the CLIP model."""
model, preprocess = clip.load(
model_name, download_root=model_dir, device=self.device
)
return model, preprocess
def _load_sac_model(self, model_path, input_size):
"""Load the SAC model."""
model = self.MLP(input_size)
ckpt = torch.load(model_path)
model.load_state_dict(ckpt)
model.to(self.device)
model.eval()
return model
def predict(self, image_path):
"""Predict the aesthetic score for a given image.
Args:
image_path (str): Path to the image file.
Returns:
float: Predicted aesthetic score.
"""
pil_image = Image.open(image_path)
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
with torch.no_grad():
# Extract CLIP features
image_features = self.clip_model.encode_image(image)
# Normalize features
normalized_features = self.normalized(
image_features.cpu().detach().numpy()
)
# Predict score
prediction = self.sac_model(
torch.from_numpy(normalized_features)
.type(torch.FloatTensor)
.to(self.device)
)
return prediction.item()
if __name__ == "__main__":
# Configuration
img_path = "/home/users/xinjie.wang/xinjie/asset3d-gen/outputs/imageto3d/demo_objects/bed/sample_0/sample_0_raw.png" # noqa
# clip_model_dir = "/horizon-bucket/robot_lab/users/xinjie.wang/weights/clip" # noqa
# sac_model_path = "/horizon-bucket/robot_lab/users/xinjie.wang/weights/sac/sac+logos+ava1-l14-linearMSE.pth" # noqa
# Initialize the predictor
predictor = AestheticPredictor()
# Predict the aesthetic score
score = predictor.predict(img_path)
print("Aesthetic score predicted by the model:", score)
|