150 lines
4.7 KiB
Python
150 lines
4.7 KiB
Python
# Project EmbodiedGen
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#
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# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied. See the License for the specific language governing
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# permissions and limitations under the License.
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import os
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import clip
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torch.nn as nn
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from huggingface_hub import snapshot_download
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from PIL import Image
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class AestheticPredictor:
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"""Aesthetic Score Predictor.
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Checkpoints from https://github.com/christophschuhmann/improved-aesthetic-predictor/tree/main
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Args:
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clip_model_dir (str): Path to the directory of the CLIP model.
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sac_model_path (str): Path to the pre-trained SAC model.
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device (str): Device to use for computation ("cuda" or "cpu").
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"""
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def __init__(self, clip_model_dir=None, sac_model_path=None, device="cpu"):
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self.device = device
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if clip_model_dir is None:
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model_path = snapshot_download(
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repo_id="xinjjj/RoboAssetGen", allow_patterns="aesthetic/*"
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)
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suffix = "aesthetic"
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model_path = snapshot_download(
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repo_id="xinjjj/RoboAssetGen", allow_patterns=f"{suffix}/*"
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)
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clip_model_dir = os.path.join(model_path, suffix)
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if sac_model_path is None:
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model_path = snapshot_download(
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repo_id="xinjjj/RoboAssetGen", allow_patterns="aesthetic/*"
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)
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suffix = "aesthetic"
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model_path = snapshot_download(
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repo_id="xinjjj/RoboAssetGen", allow_patterns=f"{suffix}/*"
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)
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sac_model_path = os.path.join(
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model_path, suffix, "sac+logos+ava1-l14-linearMSE.pth"
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)
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self.clip_model, self.preprocess = self._load_clip_model(
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clip_model_dir
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)
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self.sac_model = self._load_sac_model(sac_model_path, input_size=768)
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class MLP(pl.LightningModule): # noqa
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def __init__(self, input_size):
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super().__init__()
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self.layers = nn.Sequential(
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nn.Linear(input_size, 1024),
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nn.Dropout(0.2),
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nn.Linear(1024, 128),
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nn.Dropout(0.2),
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nn.Linear(128, 64),
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nn.Dropout(0.1),
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nn.Linear(64, 16),
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nn.Linear(16, 1),
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)
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def forward(self, x):
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return self.layers(x)
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@staticmethod
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def normalized(a, axis=-1, order=2):
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"""Normalize the array to unit norm."""
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l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
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l2[l2 == 0] = 1
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return a / np.expand_dims(l2, axis)
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def _load_clip_model(self, model_dir: str, model_name: str = "ViT-L/14"):
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"""Load the CLIP model."""
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model, preprocess = clip.load(
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model_name, download_root=model_dir, device=self.device
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)
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return model, preprocess
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def _load_sac_model(self, model_path, input_size):
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"""Load the SAC model."""
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model = self.MLP(input_size)
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ckpt = torch.load(model_path)
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model.load_state_dict(ckpt)
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model.to(self.device)
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model.eval()
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return model
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def predict(self, image_path):
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"""Predict the aesthetic score for a given image.
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Args:
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image_path (str): Path to the image file.
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Returns:
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float: Predicted aesthetic score.
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"""
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pil_image = Image.open(image_path)
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image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
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with torch.no_grad():
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# Extract CLIP features
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image_features = self.clip_model.encode_image(image)
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# Normalize features
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normalized_features = self.normalized(
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image_features.cpu().detach().numpy()
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)
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# Predict score
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prediction = self.sac_model(
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torch.from_numpy(normalized_features)
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.type(torch.FloatTensor)
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.to(self.device)
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)
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return prediction.item()
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if __name__ == "__main__":
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# Configuration
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img_path = "apps/assets/example_image/sample_00.jpg"
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# Initialize the predictor
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predictor = AestheticPredictor()
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# Predict the aesthetic score
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score = predictor.predict(img_path)
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print("Aesthetic score predicted by the model:", score)
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