[feat] add aesthetic score

tools/aesthetic/README.md

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+# Aesthetic Scoring
+
+```bash
+pip install git+https://github.com/openai/CLIP.git
+wget https://github.com/christophschuhmann/improved-aesthetic-predictor/raw/main/sac+logos+ava1-l14-linearMSE.pth -O pretrained_models/aesthetic.pth
+```

tools/aesthetic/inference.py

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+# adapted from https://github.com/christophschuhmann/improved-aesthetic-predictor/blob/main/simple_inference.py
+import clip
+import cv2
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from tqdm import tqdm
+
+
+def get_video_length(cap):
+    return int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+
+def extract_frames(video_path, points=(0.5,)):
+    cap = cv2.VideoCapture(video_path)
+    length = get_video_length(cap)
+    points = [int(length * point) for point in points]
+    frames = []
+    if length < 3:
+        return frames, length
+    for point in points:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, point)
+        ret, frame = cap.read()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = Image.fromarray(frame)
+        frames.append(frame)
+    if len(frames) == 1:
+        frames = frames[0]
+    return frames, length
+
+
+class VideoTextDataset(torch.utils.data.Dataset):
+    def __init__(self, csv_path, transform=None):
+        self.csv_path = csv_path
+        self.samples = pd.read_csv(csv_path, header=None)
+        self.transform = transform
+
+    def getitem(self, index):
+        sample = self.samples.iloc[index]
+        img = extract_frames(sample[0])[0]
+        img = self.transform(img)
+        text = sample[1]
+
+        return dict(index=index, image=img, text=text)
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, index):
+        return self.getitem(index)
+
+
+class MLP(nn.Module):
+    def __init__(self, input_size):
+        super().__init__()
+        self.input_size = input_size
+        self.layers = nn.Sequential(
+            nn.Linear(self.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)
+
+
+class AestheticScorer(nn.Module):
+    def __init__(self, input_size, device):
+        super().__init__()
+        self.mlp = MLP(input_size)
+        self.mlp.load_state_dict(torch.load("pretrained_models/aesthetic.pth"))
+        self.clip, self.preprocess = clip.load("ViT-L/14", device=device)
+
+        self.eval()
+        self.to(device)
+
+    def forward(self, x):
+        image_features = self.clip.encode_image(x)
+        image_features = F.normalize(image_features, p=2, dim=-1).float()
+        return self.mlp(image_features)
+
+
+def main():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = AestheticScorer(768, device)
+
+    dataset = VideoTextDataset(
+        "/mnt/hdd/data/VidProM/VidProM_pika/meta/vidprom_relength_fmin_48_clean_en_unescape_nourl.csv",
+        transform=model.preprocess,
+    )
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1024, shuffle=False, num_workers=16, pin_memory=True)
+    dataset.samples["aesthetic"] = ""
+    model = torch.nn.DataParallel(model)
+    output_file = "vidprom_aes.csv"
+    index = 0
+    for batch in tqdm(dataloader):
+        image = batch["image"].to(device)
+        with torch.no_grad():
+            score = model(image)
+        dataset.samples.loc[index : index + len(score) - 1, "aesthetic"] = score.cpu().numpy().flatten()
+        index += len(score)
+
+    dataset.samples.to_csv(output_file, index=False, header=False)
+    print(f"Saved {index} samples")
+
+
+if __name__ == "__main__":
+    main()