Spaces:
Runtime error
Runtime error
File size: 3,027 Bytes
88b7229 |
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 |
import os
import numpy as np
import torch
import pytorch_lightning as pl
import torch.nn as nn
import clip
from PIL import Image, ImageFile
import gradio as gr
# 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)
def load_models():
model = MLP(768)
s = torch.load("sac+logos+ava1-l14-linearMSE.pth")
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)
model_dict = {}
model_dict['classifier'] = model
model_dict['clip_model'] = model2
model_dict['clip_preprocess'] = preprocess
model_dict['device'] = device
return model_dict
def predict(image):
image_input = model_dict['clip_preprocess'](image).unsqueeze(0).to(model_dict['device'])
with torch.no_grad():
image_features = model_dict['clip_model'].encode_image(image_input)
im_emb_arr = normalized(image_features.detach().cpu().numpy())
prediction = model_dict['classifier'](torch.from_numpy(im_emb_arr).to(model_dict['device']).type(torch.cuda.FloatTensor))
score = prediction.item()
return {'aesthetic score': score}
if __name__ == '__main__':
print('\tinit models')
global model_dict
model_dict = load_models()
inputs = [gr.inputs.Image(type='pil', label='Image')]
outputs = gr.outputs.JSON()
title = 'image aesthetic predictor'
gr.Interface(predict,
inputs,
outputs,
title=title,
).launch()
|