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lpips-tf2.x

  • This is tensorflow 2.x conversion of official repo LPIPS metric (pytorch)
  • Similar to lpips-tensorflow except,
    • In this repo, network architecture is explicitly implemented rather than converting with ONNX.

Limitation

  • Currently only model='net-lin', net='vgg' is implemented

Example usage

  • input image should be [0.0 ~ 255.0], float32, NHWC format
import os
import numpy as np
import tensorflow as tf
from PIL import Image

from models.lpips_tensorflow import learned_perceptual_metric_model


def load_image(fn):
    image = Image.open(fn)
    image = np.asarray(image)
    image = np.expand_dims(image, axis=0)

    image = tf.constant(image, dtype=tf.dtypes.float32)
    return image


image_size = 64
model_dir = './models'
vgg_ckpt_fn = os.path.join(model_dir, 'vgg', 'exported')
lin_ckpt_fn = os.path.join(model_dir, 'lin', 'exported')
lpips = learned_perceptual_metric_model(image_size, vgg_ckpt_fn, lin_ckpt_fn)

# official pytorch model value:
# Distance: ex_ref.png <-> ex_p0.png = 0.569
# Distance: ex_ref.png <-> ex_p1.png = 0.422
image_fn1 = './imgs/ex_ref.png'
image_fn2 = './imgs/ex_p0.png'
# image_fn2 = './imgs/ex_p1.png'

image1 = load_image(image_fn1)
image2 = load_image(image_fn2)
dist01 = lpips([image1, image2])
print('Distance: {:.3f}'.format(dist01))

To reproduce same checkpoint files...

  • Clone official repo LPIPS metric
  • Place ./example_export_script/convert_to_tensorflow.py and ./models/lpips_tensorflow.py on root directory
  • Run convert_to_tensorflow.py