# hzwer/eccv2022-rife architecture

> Architecture diagram of the GitHub repository hzwer/eccv2022-rife, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-25). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/hzwer/eccv2022-rife
- Repository: https://github.com/hzwer/eccv2022-rife

## Overview

RIFE is a PyTorch video-frame interpolation system. Its user-facing workflow takes adjacent video frames or an image pair, runs a pretrained interpolation model to synthesize intermediate frames, and writes interpolated images or video. The repository also supports distributed training on Vimeo data and evaluation across several benchmark datasets. The sampled inference code shows frame processing and model invocation; the README documents broader command options and evaluation capabilities. Some model and dataset implementation details were not sampled, so only documented or source-supported wiring is shown.

Main entry points: [`model/RIFE.py`](https://github.com/hzwer/eccv2022-rife/blob/main/model/RIFE.py) (RIFE model API), [`inference_video.py`](https://github.com/hzwer/eccv2022-rife/blob/main/inference_video.py) (Video inference), [`inference_img.py`](https://github.com/hzwer/eccv2022-rife/blob/main/inference_img.py) (Image inference), [`model/IFNet.py`](https://github.com/hzwer/eccv2022-rife/blob/main/model/IFNet.py) (Flow interpolation)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_interfaces["Inference interfaces"]
  node_video["Video inference<br/>[inference_video.py]"]
  node_image["Image inference<br/>[inference_img.py]"]
end

subgraph group_model["Interpolation model"]
  node_rife_api["RIFE model API<br/>[RIFE.py]"]
  node_ifnet["Flow interpolation<br/>[IFNet.py]"]
  node_refine["Context refinement<br/>[refine.py]"]
  node_warp["Image warping<br/>[warplayer.py]"]
end

subgraph group_learning["Training and evaluation"]
  node_trainer["Training loop<br/>[train.py]"]
  node_dataset["Vimeo dataset<br/>[dataset.py]"]
  node_benchmark["Benchmark runners"]
end

node_user(("CLI user"))
node_video_in["Video or frames"]
node_image_pair["Image pair"]
node_video_out["Interpolated video"]
node_image_out["Interpolated frames"]
node_train_data["Vimeo training data"]
node_benchmark_data["Evaluation datasets"]

node_user -->|"runs video"| node_video
node_video_in -->|"supplies frames"| node_video
node_video -->|"requests interpolation"| node_rife_api
node_video -->|"writes result"| node_video_out
node_user -->|"runs images"| node_image
node_image_pair -->|"supplies pair"| node_image
node_image -->|"requests interpolation"| node_rife_api
node_image -->|"writes frames"| node_image_out
node_rife_api -->|"estimates flow"| node_ifnet
node_ifnet -->|"uses context nets"| node_refine
node_ifnet -->|"warps frames"| node_warp
node_user -->|"starts training"| node_trainer
node_train_data -->|"provides samples"| node_dataset
node_trainer -->|"loads batches"| node_dataset
node_trainer -->|"updates model"| node_rife_api
node_benchmark_data -->|"provides samples"| node_benchmark
node_benchmark -->|"evaluates interpolation"| node_rife_api

click node_video "https://github.com/hzwer/eccv2022-rife/blob/main/inference_video.py"
click node_image "https://github.com/hzwer/eccv2022-rife/blob/main/inference_img.py"
click node_rife_api "https://github.com/hzwer/eccv2022-rife/blob/main/model/RIFE.py"
click node_ifnet "https://github.com/hzwer/eccv2022-rife/blob/main/model/IFNet.py"
click node_refine "https://github.com/hzwer/eccv2022-rife/blob/main/model/refine.py"
click node_warp "https://github.com/hzwer/eccv2022-rife/blob/main/model/warplayer.py"
click node_trainer "https://github.com/hzwer/eccv2022-rife/blob/main/train.py"
click node_dataset "https://github.com/hzwer/eccv2022-rife/blob/main/dataset.py"
click node_benchmark "https://github.com/hzwer/eccv2022-rife/tree/main/benchmark"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_video,node_image,node_user toneBlue
class node_rife_api,node_ifnet,node_refine,node_warp toneAmber
class node_trainer,node_dataset,node_benchmark toneMint
class node_video_in,node_image_pair,node_video_out,node_image_out,node_train_data,node_benchmark_data toneIndigo
```

## Components

### Inference interfaces

- [Video inference](https://github.com/hzwer/eccv2022-rife/blob/main/inference_video.py): `inference_video.py`
- [Image inference](https://github.com/hzwer/eccv2022-rife/blob/main/inference_img.py): `inference_img.py`

### Interpolation model

- [RIFE model API](https://github.com/hzwer/eccv2022-rife/blob/main/model/RIFE.py): `model/RIFE.py`
- [Flow interpolation](https://github.com/hzwer/eccv2022-rife/blob/main/model/IFNet.py): `model/IFNet.py`
- [Context refinement](https://github.com/hzwer/eccv2022-rife/blob/main/model/refine.py): `model/refine.py`
- [Image warping](https://github.com/hzwer/eccv2022-rife/blob/main/model/warplayer.py): `model/warplayer.py`

### Training and evaluation

- [Training loop](https://github.com/hzwer/eccv2022-rife/blob/main/train.py): `train.py`
- [Vimeo dataset](https://github.com/hzwer/eccv2022-rife/blob/main/dataset.py): `dataset.py`
- [Benchmark runners](https://github.com/hzwer/eccv2022-rife/tree/main/benchmark): `benchmark`

### Other components

- **CLI user**
- **Video or frames**
- **Image pair**
- **Interpolated video**
- **Interpolated frames**
- **Vimeo training data**
- **Evaluation datasets**

## Connections

- CLI user → Video inference: runs video
- Video or frames → Video inference: supplies frames
- Video inference → RIFE model API: requests interpolation
- Video inference → Interpolated video: writes result
- CLI user → Image inference: runs images
- Image pair → Image inference: supplies pair
- Image inference → RIFE model API: requests interpolation
- Image inference → Interpolated frames: writes frames
- RIFE model API → Flow interpolation: estimates flow
- Flow interpolation → Context refinement: uses context nets
- Flow interpolation → Image warping: warps frames
- CLI user → Training loop: starts training
- Vimeo training data → Vimeo dataset: provides samples
- Training loop → Vimeo dataset: loads batches
- Training loop → RIFE model API: updates model
- Evaluation datasets → Benchmark runners: provides samples
- Benchmark runners → RIFE model API: evaluates interpolation