This repo contains TensorFlow 2 implementation of Real-Time High-Resolution Background Matting. For more information and downloading the weights, please visit our official repo.
The TensorFlow implementation is experimental. We find PyTorch to have faster inference speed and suggest you to use the official PyTorch version whenever possible.
We reimplement our model natively in TensorFlow 2 and provide a script to load PyTorch weights directly into the TensorFlow model.
import tensorflow as tf
import torch # For loading PyTorch weights.
from model import MattingRefine, load_torch_weights
# Enable mixed precision, it reduces memory and may make model inference faster.
tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
# Create TensorFlow model
model = MattingRefine(backbone='resnet50',
backbone_scale=0.25,
refine_mode='sampling',
refine_sample_pixels=80000)
# Load PyTorch weights into TensorFlow model.
load_torch_weights(model, torch.load('PATH_TO_PYTORCH_WEIGHTS.pth'))
src = tf.random.normal((1, 1080, 1920, 3))
bgr = tf.random.normal((1, 1080, 1920, 3))
# Faster inference with tf.function
# Note that at the first time the model run with
# tf.function will be slow.
model = tf.function(model, experimental_relax_shapes=True)
pha, fgr = model([src, bgr], training=False)[:2]Please visit the official repo for detail.