thanks to christophschuhmann ❤

LICENSE

@@ -0,0 +1,201 @@
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README.md

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+# CLIP+MLP Aesthetic Score Predictor
+
+Train, use and visualize an aesthetic score predictor ( how much people like on average an image ) based on a simple neural net that takes CLIP embeddings as inputs.
+
+
+Link to the AVA training data ( already prepared) :
+https://drive.google.com/drive/folders/186XiniJup5Rt9FXsHiAGWhgWz-nmCK_r?usp=sharing
+
+
+Visualizations of all images from LAION 5B (english subset with 2.37B images) in 40 buckets with the model sac+logos+ava1-l14-linearMSE.pth:
+http://captions.christoph-schuhmann.de/aesthetic_viz_laion_sac+logos+ava1-l14-linearMSE-en-2.37B.html
+
+

ava+logos-l14-linearMSE.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:390a3aafaf3b37d57148f9b22f30556de38343064b7d915acfa80d3812b4c9ff
+size 3714759

ava+logos-l14-reluMSE.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0af3254c651d55b7ea851429c20f26ec880bb0169805a4df85b814bd7966f3e4
+size 3714887

prepare-data-for-training.py

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+
+# This script prepares the training images and ratings for the training.
+# It assumes that all images are stored as files that PIL can read.
+# It also assumes that the paths to the images files and the average ratings are in a .parquet files that can be read into a dataframe ( df ).
+
+from datasets import load_dataset
+import pandas as pd
+import statistics
+from torch.utils.data import Dataset, DataLoader
+import clip
+import torch
+from PIL import Image, ImageFile
+import numpy as np
+import time
+
+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)
+
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = clip.load("ViT-L/14", device=device)
+
+ 
+f = "trainingdata.parquet"
+df = pd.read_parquet(f)  #assumes that the df has the columns IMAGEPATH  & AVERAGE_RATING
+
+
+x = []
+y = []
+c= 0
+
+for idx, row in df.iterrows():
+    start = time.time()
+
+    average_rating = float(row.AVERAGE_RATING)
+    print(average_rating)
+    if average_rating <1:
+       continue
+
+    img= row.IMAGEPATH  #assumes that the df has the column IMAGEPATH
+    print(img)
+
+    try:
+       image = preprocess(Image.open(img)).unsqueeze(0).to(device)
+    except:
+   	   continue
+
+    with torch.no_grad():
+       image_features = model.encode_image(image)
+
+    im_emb_arr = image_features.cpu().detach().numpy() 
+    x.append(normalized ( im_emb_arr) )      # all CLIP embeddings are getting normalized. This also has to be done when inputting an embedding later for inference
+    y_ = np.zeros((1, 1))
+    y_[0][0] = average_rating
+    #y_[0][1] = stdev      # I initially considered also predicting the standard deviation, but then didn't do it
+
+    y.append(y_)
+
+
+    print(c)
+    c+=1
+
+
+
+
+x = np.vstack(x)
+y = np.vstack(y)
+print(x.shape)
+print(y.shape)
+np.save('x_OpenAI_CLIP_L14_embeddings.npy', x)
+np.save('y_ratings.npy', y)

sac+logos+ava1-l14-linearMSE.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21dd590f3ccdc646f0d53120778b296013b096a035a2718c9cb0d511bff0f1e0
+size 3714759

simple_inference.py

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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 )
+
+

train_predictor.py

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+import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = "0"       # in case you are using a multi GPU workstation, choose your GPU here
+import tqdm
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import torch.nn.functional as F
+import pandas as pd
+from datasets import load_dataset
+from torch.utils.data import TensorDataset, DataLoader
+
+import numpy as np
+
+#define your neural net 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
+
+
+
+# load the training data 
+
+x = np.load ("/mnt/spirit/ava_x.npy")
+
+y = np.load ("/mnt/spirit/ava_y.npy")
+
+val_percentage = 0.05 # 5% of the trainingdata will be used for validation
+
+train_border = int(x.shape()[0] * (1 - val_percentage) )
+
+train_tensor_x = torch.Tensor(x[:train_border]) # transform to torch tensor
+train_tensor_y = torch.Tensor(y[:train_border])
+
+train_dataset = TensorDataset(train_tensor_x,train_tensor_y) # create your datset
+train_loader = DataLoader(train_dataset, batch_size=256, shuffle=True,  num_workers=16) # create your dataloader
+
+
+val_tensor_x = torch.Tensor(x[train_border:]) # transform to torch tensor
+val_tensor_y = torch.Tensor(y[train_border:])
+
+'''
+print(train_tensor_x.size())
+print(val_tensor_x.size())
+print( val_tensor_x.dtype)
+print( val_tensor_x[0].dtype)
+'''
+
+val_dataset = TensorDataset(val_tensor_x,val_tensor_y) # create your datset
+val_loader = DataLoader(val_dataset, batch_size=512,  num_workers=16) # create your dataloader
+
+
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = MLP(768).to(device)   # CLIP embedding dim is 768 for CLIP ViT L 14
+
+optimizer = torch.optim.Adam(model.parameters()) 
+
+# choose the loss you want to optimze for
+criterion = nn.MSELoss()
+criterion2 = nn.L1Loss()
+
+epochs = 50
+
+model.train()
+best_loss =999
+save_name = "linear_predictor_L14_MSE.pth"
+
+
+for epoch in range(epochs):
+    losses = []
+    losses2 = []
+    for batch_num, input_data in enumerate(train_loader):
+        optimizer.zero_grad()
+        x, y = input_data
+        x = x.to(device).float()
+        y = y.to(device)
+
+        output = model(x)
+        loss = criterion(output, y)
+        loss.backward()
+        losses.append(loss.item())
+
+
+        optimizer.step()
+
+        if batch_num % 1000 == 0:
+            print('\tEpoch %d | Batch %d | Loss %6.2f' % (epoch, batch_num, loss.item()))
+            #print(y)
+
+    print('Epoch %d | Loss %6.2f' % (epoch, sum(losses)/len(losses)))
+    losses = []
+    losses2 = []
+    
+    for batch_num, input_data in enumerate(val_loader):
+        optimizer.zero_grad()
+        x, y = input_data
+        x = x.to(device).float()
+        y = y.to(device)
+
+        output = model(x)
+        loss = criterion(output, y)
+        lossMAE = criterion2(output, y)
+        #loss.backward()
+        losses.append(loss.item())
+        losses2.append(lossMAE.item())
+        #optimizer.step()
+
+        if batch_num % 1000 == 0:
+            print('\tValidation - Epoch %d | Batch %d | MSE Loss %6.2f' % (epoch, batch_num, loss.item()))
+            print('\tValidation - Epoch %d | Batch %d | MAE Loss %6.2f' % (epoch, batch_num, lossMAE.item()))
+            
+            #print(y)
+
+    print('Validation - Epoch %d | MSE Loss %6.2f' % (epoch, sum(losses)/len(losses)))
+    print('Validation - Epoch %d | MAE Loss %6.2f' % (epoch, sum(losses2)/len(losses2)))
+    if sum(losses)/len(losses) < best_loss:
+        print("Best MAE Val loss so far. Saving model")
+        best_loss = sum(losses)/len(losses)
+        print( best_loss ) 
+
+        torch.save(model.state_dict(), save_name )
+
+
+torch.save(model.state_dict(), save_name)
+
+print( best_loss ) 
+
+print("training done")
+# inferece test with dummy samples from the val set, sanity check
+print( "inferece test with dummy samples from the val set, sanity check")
+model.eval()
+output = model(x[:5].to(device))
+print(output.size())
+print(output)

visulaize_100k_from_LAION400M.py

@@ -0,0 +1,175 @@
+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
+#import open_clip
+
+from PIL import Image, ImageFile
+
+
+# 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("ava+logos-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   
+
+
+
+c=0
+urls= []
+predictions=[]
+
+# this will run inference over 10 webdataset tar files from LAION 400M and sort them into 20 categories
+# you can DL LAION 400M and convert it to wds tar files with img2dataset ( https://github.com/rom1504/img2dataset ) 
+
+
+for j in range(10):
+   if j<10:
+     # change the path to the tar files accordingly
+     dataset = wds.WebDataset("pipe:aws s3 cp s3://s-datasets/laion400m/laion400m-dat-release/0000"+str(j)+".tar -")  #"pipe:aws s3 cp s3://s-datasets/laion400m/laion400m-dat-release/00625.tar -")
+   else:
+     dataset = wds.WebDataset("pipe:aws s3 cp s3://s-datasets/laion400m/laion400m-dat-release/000"+str(j)+".tar -")  #"pipe:aws s3 cp s3://s-datasets/laion400m/laion400m-dat-release/00625.tar -")
+
+
+   for i, d in enumerate(dataset):
+      print(c)
+
+      metadata= json.loads(d['json'])       
+
+      pil_image = Image.open(io.BytesIO(d['jpg']))
+      c=c+1
+      try:
+         image = preprocess(pil_image).unsqueeze(0).to(device)
+
+      except:
+         continue
+
+      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))
+      urls.append(metadata["url"])
+      predictions.append(prediction)
+
+
+df = pd.DataFrame(list(zip(urls, predictions)),
+               columns =['filepath', 'prediction'])
+
+
+buckets = [(i, i+1) for i in range(20)]
+
+
+html= "<h1>Aesthetic subsets in LAION 100k samples</h1>"
+
+i =0
+for [a,b] in buckets:
+    a = a/2
+    b = b/2
+    total_part = df[(  (df["prediction"] ) *1>= a) & (  (df["prediction"] ) *1 <= b)]
+    print(a,b)
+    print(len(total_part) )
+    count_part = len(total_part) / len(df) * 100
+    estimated =int ( len(total_part) )
+    part = total_part[:50]
+
+    html+=f"<h2>In bucket {a} - {b} there is {count_part:.2f}% samples:{estimated:.2f} </h2> <div>"
+    for filepath in part["filepath"]:
+        html+='<img src="'+filepath +'" height="200" />'
+
+
+    html+="</div>"
+    i+=1
+    print(i)
+with open("./aesthetic_viz_laion_ava+logos_L14_100k-linearMSE.html", "w") as f:
+    f.write(html)
+    
+