liuguoyou / frame-interpolation-pytorch

PyTorch implementation of FILM: Frame Interpolation for Large Motion, In ECCV 2022.

Frame interpolation in PyTorch

This is an unofficial PyTorch inference implementation of FILM: Frame Interpolation for Large Motion, In ECCV 2022.
Original repository link

The project is focused on creating simple and TorchScript compilable inference interface for the original pretrained TF2 model.

Quickstart

Download a compiled model from the release and specify the path to the file in the following snippet:

import torch

device = torch.device('cuda')
precision = torch.float16

model = torch.jit.load(model_path, map_location='cpu')
model.eval().to(device=device, dtype=precision)

img1 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
img3 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
dt = img1.new_full((1, 1), .5)

with torch.no_grad():
    img2 = model(img1, img3, dt)  # Will be of the same shape as inputs (1, 3, 720, 1080)

Exporting model by yourself

You will need to install TensorFlow of the version specified in the original repo and download SavedModel of " Style" network from there

After you have downloaded the SavedModel and can load it via tf.compat.v2.saved_model.load(path):

• Clone the repository

git clone https://github.com/dajes/frame-interpolation-pytorch
cd frame-interpolation-pytorch

• Install dependencies

python -m pip install -r requirements.txt

• Run export.py:

python export.py "model_path" "save_path" [--statedict] [--fp32] [--skiptest] [--gpu]

Argument list:

• model_path Path to the TF SavedModel
• save_path Path to save the PyTorch state dict
• --statedict Export to state dict instead of TorchScript
• --fp32 Save weights at full precision
• --skiptest Skip testing and save model immediately instead
• --gpu Whether to attempt to use GPU for testing

Testing exported model

The following script creates an MP4 video of interpolated frames between 2 input images:

python inference.py "model_path" "img1" "img2" [--save_path SAVE_PATH] [--gpu] [--fp16] [--frames FRAMES] [--fps FPS]

• model_path Path to the exported TorchScript checkpoint
• img1 Path to the first image
• img2 Path to the second image
• --save_path SAVE_PATH Path to save the interpolated frames as a video, if absent it will be saved in the same directory as img1 is located and named output.mp4
• --gpu Whether to attempt to use GPU for predictions
• --fp16 Whether to use fp16 for calculations, speeds inference up on GPUs with tensor cores
• --frames FRAMES Number of frames to interpolate between the input images
• --fps FPS FPS of the output video

Results on the 2 example photos from original repository: