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| import webdataset as wds | |
| from PIL import Image | |
| import io | |
| import matplotlib.pyplot as plt | |
| import os | |
| import json | |
| from warnings import filterwarnings | |
| # os.environ["CUDA_VISIBLE_DEVICES"] = "0" # choose GPU if you are on a multi GPU server | |
| import numpy as np | |
| import torch | |
| import pytorch_lightning as pl | |
| import torch.nn as nn | |
| from torchvision import datasets, transforms | |
| import tqdm | |
| from os.path import join | |
| from datasets import load_dataset | |
| import pandas as pd | |
| from torch.utils.data import Dataset, DataLoader | |
| import json | |
| import clip | |
| from PIL import Image, ImageFile | |
| ##### This script will predict the aesthetic score for this image file: | |
| img_path = "test.jpg" | |
| # if you changed the MLP architecture during training, change it also here: | |
| class MLP(pl.LightningModule): | |
| def __init__(self, input_size, xcol='emb', ycol='avg_rating'): | |
| super().__init__() | |
| self.input_size = input_size | |
| self.xcol = xcol | |
| self.ycol = ycol | |
| self.layers = nn.Sequential( | |
| nn.Linear(self.input_size, 1024), | |
| #nn.ReLU(), | |
| nn.Dropout(0.2), | |
| nn.Linear(1024, 128), | |
| #nn.ReLU(), | |
| nn.Dropout(0.2), | |
| nn.Linear(128, 64), | |
| #nn.ReLU(), | |
| nn.Dropout(0.1), | |
| nn.Linear(64, 16), | |
| #nn.ReLU(), | |
| nn.Linear(16, 1) | |
| ) | |
| def forward(self, x): | |
| return self.layers(x) | |
| def training_step(self, batch, batch_idx): | |
| x = batch[self.xcol] | |
| y = batch[self.ycol].reshape(-1, 1) | |
| x_hat = self.layers(x) | |
| loss = F.mse_loss(x_hat, y) | |
| return loss | |
| def validation_step(self, batch, batch_idx): | |
| x = batch[self.xcol] | |
| y = batch[self.ycol].reshape(-1, 1) | |
| x_hat = self.layers(x) | |
| loss = F.mse_loss(x_hat, y) | |
| return loss | |
| def configure_optimizers(self): | |
| optimizer = torch.optim.Adam(self.parameters(), lr=1e-3) | |
| return optimizer | |
| def normalized(a, axis=-1, order=2): | |
| import numpy as np # pylint: disable=import-outside-toplevel | |
| l2 = np.atleast_1d(np.linalg.norm(a, order, axis)) | |
| l2[l2 == 0] = 1 | |
| return a / np.expand_dims(l2, axis) | |
| model = MLP(768) # CLIP embedding dim is 768 for CLIP ViT L 14 | |
| s = torch.load("sac+logos+ava1-l14-linearMSE.pth") # load the model you trained previously or the model available in this repo | |
| model.load_state_dict(s) | |
| model.to("cuda") | |
| model.eval() | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model2, preprocess = clip.load("ViT-L/14", device=device) #RN50x64 | |
| pil_image = Image.open(img_path) | |
| image = preprocess(pil_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| image_features = model2.encode_image(image) | |
| im_emb_arr = normalized(image_features.cpu().detach().numpy() ) | |
| prediction = model(torch.from_numpy(im_emb_arr).to(device).type(torch.cuda.FloatTensor)) | |
| print( "Aesthetic score predicted by the model:") | |
| print( prediction ) | |