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Repository: mobicom24/RF-Diffusion
Branch: main
Commit: eb872b0c4543
Files: 44
Total size: 243.0 KB
Directory structure:
gitextract_yuerfw2n/
├── LICENSE
├── README.md
├── complex/
│ ├── __init__.py
│ ├── complex_functions.py
│ ├── complex_layers.py
│ └── complex_module.py
├── inference.py
├── plots/
│ ├── code/
│ │ ├── Fig10-Scalability-analysis.py
│ │ ├── Fig11(a)-Cross-domain-Performance-of-augmented-Wi-Fi-sensing.py
│ │ ├── Fig11(b)-In-domain-Performance-of-augmented-Wi-Fi-sensing.py
│ │ ├── Fig12-Impact-of-synthetic-data-volume.py
│ │ ├── Fig13(a)-Performance-of-channel-estimation-amplitude-phase.py
│ │ ├── Fig13(b)-Performance-of-channel-estimation-SNR.py
│ │ ├── Fig6(a)-exp-overall-wifi-ssim.py
│ │ ├── Fig6(b)-exp-overall-wifi-fid.py
│ │ ├── Fig7(a)-exp-overall-fmcw-ssim.py
│ │ ├── Fig7(b)-exp-overall-fmcw-fid.py
│ │ ├── Fig8-Impact-of-diffusion-method.py
│ │ └── Fig9-Impact-of-network-design.py
│ ├── data/
│ │ ├── exp_MIMO.mat
│ │ ├── exp_cross_domain.mat
│ │ ├── exp_impact_diffusion_method.mat
│ │ ├── exp_impact_network_design.mat
│ │ ├── exp_impact_synthetic_data.mat
│ │ ├── exp_in_domain.mat
│ │ ├── exp_mimo_snr.mat
│ │ ├── exp_overall_fid_fmcw.mat
│ │ ├── exp_overall_fid_wifi.mat
│ │ ├── exp_overall_ssim_fmcw.mat
│ │ ├── exp_overall_ssim_wifi.mat
│ │ ├── exp_scalability_analysis.mat
│ │ ├── untitled.asv
│ │ └── untitled.m
│ └── requirements.txt
├── tfdiff/
│ ├── __init__.py
│ ├── dataset.py
│ ├── diffusion.py
│ ├── eeg_model.py
│ ├── fmcw_model.py
│ ├── learner.py
│ ├── mimo_model.py
│ ├── params.py
│ └── wifi_model.py
└── train.py
================================================
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FILE: LICENSE
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reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# Artifact for MobiCom'24: RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://colab.research.google.com/drive/1T8duxPyb92Owl5nijWF-pUknMWwmZpRH?usp=sharing)
## Overview
We introduce RF-Diffusion, a versatile generative model designed for wireless data. RF-Diffusion is capable of generating various types of signals, including Wi-Fi, FMCW Radar, 5G, and even modalities beyond RF, showcasing RF-Diffusion's prowess across different signal categories. We extensively evaluate RF-Diffusion's generative capabilities and validate its effectiveness in multiple downstream tasks, including wireless sensing and 5G channel estimation.
Our basic implementation of RF-Diffusion is provided in this repository. We have released several medium-sized pre-trained models (each containing 16 to 32 blocks, with 128 or 256 hidden dim) and part of the corresponding data files in [releases](https://github.com/mobicom24/RF-Diffusion/releases/tag/dataset_model), which can be used for performance testing.
An intuitive comparison between RF-Diffusion and three other prevalent generative models is shown as follows. For demonstration purposes, we provide the **Doppler Frequency Shift (DFS)** spectrogram of the Wi-Fi signal, and the **Range Doppler Map (RDM)** spectrogram of the FMCW Radar signal, which are representative features of the two signals, respectively.
Please note that all these methods generate the **raw complex-valued signals**, and the spectrograms are shown for ease of illustration.
**Note: The GIFs in the table below may take some time to load. If they don't appear immediately, please wait for a moment or try refreshing the webpage.**
| | Ground Truth | RF-Diffusion | DDPM[^DDPM] | DCGAN[^DCGAN] | CVAE[^CVAE] |
| ---- | ---- | ---- | ---- | ---- | ---- |
| **Wi-Fi** | <img src="./img/0-wifi-gesture-gt.gif" height=100> <img src="./img/0-wifi-fall-gt.gif" height=100>| <img src="./img/0-wifi-gesture-ours.gif" height=100 ><img src="./img/0-wifi-fall-ours.gif" height=100>| <img src="./img/0-wifi-gesture-ddpm.gif" height=100> <img src="./img/0-wifi-fall-ddpm.gif" height=100>| <img src="./img/0-wifi-gesture-gan.gif" height=100><img src="./img/0-wifi-fall-gan.gif" height=100> | <img src="./img/0-wifi-gesture-vae.gif" height=100> <img src="./img/0-wifi-fall-vae.gif" height=100> |
| **FMCW** | <img src="./img/0-fmcw-1-gt.gif" height=100> <img src="./img/0-fmcw-2-gt.gif" height=100> | <img src="./img/0-fmcw-1-ours.gif" height=100> <img src="./img/0-fmcw-2-ours.gif" height=100> | <img src="./img/0-fmcw-1-ddpm.gif" height=100> <img src="./img/0-fmcw-2-ddpm.gif" height=100> | <img src="./img/0-fmcw-1-gan.gif" height=100> <img src="./img/0-fmcw-2-gan.gif" height=100> | <img src="./img/0-fmcw-1-vae.gif" height=100> <img src="./img/0-fmcw-2-vae.gif" height=100> |
As shown, RF-Diffusion generates signals that accurately retain their physical features.
## Running the Evaluation Script
You can run the evaluation script that produces the major figures in our paper in two ways.
**1. (Recommended) Google Colab Notebook**
* Simply open [this notebook](https://colab.research.google.com/drive/1T8duxPyb92Owl5nijWF-pUknMWwmZpRH?usp=sharing). Under the ```Runtime``` tab, select ```Run all```.
* Please wait for 15 minutes as the data are being processed.
* The figures will be displayed in your browser.
**2. Local Setup**
* Clone this repository.
* Install [Python 3](https://www.python.org/downloads/) if you have not already. Then, run pip3 install ```-r requirements.txt``` at the root directory of ```/plots``` folder to install the dependencies.
* Run code files in ```/plots/code``` directory one by one and wait for 15 minutes as the data are being processed.
* In ```/plots/img``` directory, figures used in our paper can be found.
## Further Testing and Customization
In this section, we offer training code, testing code, and pre-trained models. You can utilize our pre-trained models for further testing and even customize the models according to your specific tasks. This will significantly foster the widespread application of RF-Diffusion within the community.
<!--
- [0. Prerequisite](#0-prerequisite)
- [1. RF Data Generation](#1-rf-data-generation)
- [1.1 Wi-Fi Data Generation](#11-wi-fi-data-generation)
- [1.2 FMCW Data Generation](#12-fmcw-data-generation)
- [2. Case Study](#2-case-study)
- [2.1 Augmented Wireless Sensing](#21-augmented-wireless-sensing)
- [2.2 5G FDD Channel Estimation](#22-5g-fdd-channel-estimation)
- [2.3 Supplementary: EEG Signal Denoise](#23-supplementary-eeg-signal-denoise)
-->
## 0. Prerequisite
RF-Diffusion is implemented with [Python 3.8](https://www.python.org/downloads/) and [PyTorch 2.0.1](https://pytorch.org/). We manage the development environment using [Conda](https://anaconda.org/anaconda/conda).
Execute the following commands to configure the development environment.
- Create a conda environment called `RF-Diffusion` based on python 3.8, and activate the environment.
```bash
conda create -n RF-Diffusion python=3.8
conda activate RF-Diffusion
```
- Install PyTorch, as well as other required packages.
```bash
pip3 install torch
```
```bash
pip3 install numpy scipy tensorboard tqdm matplotlib torchvision pytorch_fid
```
For more details about the environment configuration, refer to the `requirements.txt` file in [releases](https://github.com/mobicom24/RF-Diffusion/releases/tag/dataset_model).
Download or `git clone` the `RF-Diffusion` project. Download and unzip `dataset.zip` and `model.zip` in [releases](https://github.com/mobicom24/RF-Diffusion/releases/tag/dataset_model) to the project directory.
```bash
unzip -q dataset.zip -d 'RF-Diffusion/dataset'
unzip -q model.zip -d 'RF-Diffusion'
```
The project structure is shown as follows:
<div align="center"> <img src="./img/0-project.png" height=400> </div>
## 1. RF Data Generation
In the following part, we use `task_id` to differentiate between four types of tasks of synthesising Wi-Fi, FMCW signals, performing 5G channel estimation, and denoising the EEG to 0, 1, 2 and 3 respectively.
### 1.1 Wi-Fi Data Generation
By executing the following code, you can generate new Wi-Fi data, and the corresponding average SSIM (Structural Similarity Index Measure) and FID (Fréchet Inception Distance) will be displayed in the command line, which matches the values reported in section 6.2: Overall Generation Quality of the paper.
```python
python3 inference.py --task_id 0
```
The generated data are stored in `.mat` format, and can be found in `./dataset/wifi/output`.
Our model showcases the best performance in both SSIM (Structure Similarity Index Measure) and FID (Frechet Inception Distance) compared to other prevalent generative models:
<div align="center"> <img src=".\img\1-exp-overall-wifi-ssim.png" height=230><img src=".\img\2-exp-overall-wifi-fid.png" height=230> </div>
### 1.2 FMCW Data Generation
By executing the following code, you will generate FMCW data, and the corresponding average SSIM (Structural Similarity Index Measure) and FID (Fréchet Inception Distance) will be displayed in the command line, which matches the values reported in section 6.2: Overall Generation Quality of the paper.
```python
python3 inference.py --task_id 1
```
The generated data are stored in `.mat` format, and can be found at `./dataset/fmcw/output`.
Our model showcases the best performance in both SSIM (Structure Similarity Index Measure) and FID (Frechet Inception Distance) among all prevalent generative models:
<div align="center"> <img src=".\img\3-exp-overall-fmcw-ssim.png" height=230><img src=".\img\4-exp-overall-fmcw-fid.png" height=230> </div>
## 2. Case Study
### 2.1 Augmented Wireless Sensing
A pre-trained RF-Diffusion can be leveraged as a data augmenter, which generates synthetic RF signals of the designated type. The synthetic samples are then mixed with the original dataset, collectively employed to train the wireless sensing model.
You can try performing the data generation task on your own dataset based on the instructions in [RF Data Generation](#1-rf-data-generation), and train your own model with both real-world and synthetic data.
To retrain a new model, you only need to place your own data files within the `./dataset/wifi/raw` or `./dataset/fmcw/raw` directory, and then execute the `train.py` script to retrain. You may need to properly set the `./tfdiff/params.py` file to correspond to your input data format.
Taking Wi-Fi gesture recognition as an example. We choose the Widar3.0 dataset and perform augmented wireless sensing on two models, Widar3.0 and EI, to test the performance gain of data augmentation in both cross-domain and in-domain scenarios, which can be found in section 7.1: Wi-Fi Gesture Recognition of the paper.
<div align="center"> <img src=".\img\8-exp-sensing-cross.png" height=230><img src=".\img\9-exp-sensing-in.png" height=230> </div>
<div align="center"> <img src=".\img\10-exp-sensing-data.png" height=230> </div>
### 2.2 5G FDD Channel Estimation
By executing the following command, a downlink channel estimation for 5G FDD system can be performed.
```python
python3 inference.py --task_id 2
```
The corresponding average Signal-to-Noise Ratio (SNR) will be displayed in the command line and found in section 7.2: 5G FDD Channel Estimation of the paper.
The channel estimation is evaluated based on the [Argos](https://renew.rice.edu/dataset-argos.html) dataset. As the results show, our model showcases the best performance compared to state-of-the-art models.
<div align="center"> <img src=".\img\11-exp-channel-sample.png" height=230><img src=".\img\12-exp-channel-snr.png" height=230> </div>
<!--
### 2.3 Supplementary: EEG Signal Denoise
RF-Diffusion is designed to generate a wide range of time-series data. While its primary application is in the wireless/RF signals domain, its capabilities extend beyond that.
To verify this, we provide a supplementary case study for EEG denoising, which doesn't appear in our submitted paper due to the page limitation.
To run the EEG denoising application, you only need to extract the contents of `eeg.zip` and place the extracted folder in the `model` folder.
```bash
unzip eeg.zip -d [RF-Diffusion/model]
```
By executing the following code, RF-Diffusion can be leveraged to denoise the EEG signals which is contaminated by EOG interference.
```python
python3 inference.py --task_id 3
```
The corresponding average Signal-to-Noise Ratio (SNR) will be displayed in the command line. The denoised EEG data can be found at `./dataset/eeg/output`.
Our EEG denoising evaluation is tested on the [GCTNET](https://github.com/JinY97/GCTNet/tree/main/data) dataset. Compared with other denoising methods, RF-Diffusion demonstrates a delightful result.
<div align="center"> <img src=".\img\13-exp-eeg-sample.png" height=230><img src=".\img\14-exp-eeg-snr.png" height=230> </div>
-->
## License
The code, data and related scripts are made available under the GNU General Public License v3.0. By downloading it or using them, you agree to the terms of this license.
## Reference
If you use our dataset in your work, please reference it using
```
@inproceedings{chi2024rf,
title={RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion},
author={Chi, Guoxuan and Yang, Zheng and Wu, Chenshu and Xu, Jingao and Gao, Yuchong and Liu, Yunhao and Han, Tony Xiao},
booktitle={Proceedings of the 30th Annual International Conference on Mobile Computing and Networking},
pages={77--92},
year={2024}
}
```
[^DDPM]: Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models[J]. Advances in neural information processing systems, 2020, 33: 6840-6851.
[^DCGAN]: Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.
[^CVAE]: Sohn K, Lee H, Yan X. Learning structured output representation using deep conditional generative models[J]. Advances in neural information processing systems, 2015, 28.
================================================
FILE: complex/__init__.py
================================================
================================================
FILE: complex/complex_functions.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: spopoff
"""
import torch
from torch.nn.functional import relu, max_pool2d, max_pool3d, avg_pool2d, avg_pool1d, dropout, dropout2d, dropout3d, interpolate, sigmoid, tanh, leaky_relu
def complex_matmul(A, B):
'''
Performs the matrix product between two complex matricess
'''
outp_real = torch.matmul(A.real, B.real) - torch.matmul(A.imag, B.imag)
outp_imag = torch.matmul(A.real, B.imag) + torch.matmul(A.imag, B.real)
return outp_real.type(torch.complex64) + 1j * outp_imag.type(torch.complex64)
def complex_avg_pool1d(input, *args, **kwargs):
absolute_value_real = avg_pool1d(input.real, *args, **kwargs)
absolute_value_imag = avg_pool1d(input.imag, *args, **kwargs)
return absolute_value_real.type(torch.complex64)+1j*absolute_value_imag.type(torch.complex64)
def complex_avg_pool2d(input, *args, **kwargs):
'''
Perform complex average pooling.
'''
absolute_value_real = avg_pool2d(input.real, *args, **kwargs)
absolute_value_imag = avg_pool2d(input.imag, *args, **kwargs)
return absolute_value_real.type(torch.complex64)+1j*absolute_value_imag.type(torch.complex64)
def complex_normalize(input):
'''
Perform complex normalization
'''
real_value, imag_value = input.real, input.imag
real_norm = (real_value - real_value.mean()) / real_value.std()
imag_norm = (imag_value - imag_value.mean()) / imag_value.std()
return real_norm.type(torch.complex64) + 1j*imag_norm.type(torch.complex64)
def complex_leaky_relu(input, negative_slope):
return leaky_relu(input.real, negative_slope).type(torch.complex64)+1j*leaky_relu(input.imag, negative_slope).type(torch.complex64)
def complex_relu(input):
return relu(input.real).type(torch.complex64)+1j*relu(input.imag).type(torch.complex64)
def complex_sigmoid(input):
return sigmoid(input.real).type(torch.complex64)+1j*sigmoid(input.imag).type(torch.complex64)
def complex_tanh(input):
return tanh(input.real).type(torch.complex64)+1j*tanh(input.imag).type(torch.complex64)
def complex_opposite(input):
return -(input.real).type(torch.complex64)+1j*(-(input.imag).type(torch.complex64))
def complex_stack(input, dim):
input_real = [x.real for x in input]
input_imag = [x.imag for x in input]
return torch.stack(input_real, dim).type(torch.complex64)+1j*torch.stack(input_imag, dim).type(torch.complex64)
def _retrieve_elements_from_indices(tensor, indices):
flattened_tensor = tensor.flatten(start_dim=-2)
output = flattened_tensor.gather(dim=-1, index=indices.flatten(start_dim=-2)).view_as(indices)
return output
def _retrieve_elements_from_indices3d(tensor, indices):
flattened_tensor = tensor.flatten(start_dim=-3)
output = flattened_tensor.gather(dim=-1, index=indices.flatten(start_dim=-3)).view_as(indices)
return output
def complex_upsample(input, size=None, scale_factor=None, mode='nearest',
align_corners=None, recompute_scale_factor=None):
'''
Performs upsampling by separately interpolating the real and imaginary part and recombining
'''
outp_real = interpolate(input.real, size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor)
outp_imag = interpolate(input.imag, size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor)
return outp_real.type(torch.complex64) + 1j * outp_imag.type(torch.complex64)
def complex_upsample2(input, size=None, scale_factor=None, mode='nearest',
align_corners=None, recompute_scale_factor=None):
'''
Performs upsampling by separately interpolating the amplitude and phase part and recombining
'''
outp_abs = interpolate(input.abs(), size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor)
angle = torch.atan2(input.imag,input.real)
outp_angle = interpolate(angle, size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor)
return outp_abs \
* (torch.cos(angle).type(torch.complex64)+1j*torch.sin(angle).type(torch.complex64))
def complex_max_pool2d(input,kernel_size, stride=None, padding=0,
dilation=1, ceil_mode=False, return_indices=False):
'''
Perform complex max pooling by selecting on the absolute value on the complex values.
'''
absolute_value, indices = max_pool2d(
input.abs(),
kernel_size = kernel_size,
stride = stride,
padding = padding,
dilation = dilation,
ceil_mode = ceil_mode,
return_indices = True
)
# performs the selection on the absolute values
absolute_value = absolute_value.type(torch.complex64)
# retrieve the corresponding phase value using the indices
# unfortunately, the derivative for 'angle' is not implemented
angle = torch.atan2(input.imag,input.real)
# get only the phase values selected by max pool
angle = _retrieve_elements_from_indices(angle, indices)
return absolute_value \
* (torch.cos(angle).type(torch.complex64)+1j*torch.sin(angle).type(torch.complex64))
def complex_max_pool3d(input,kernel_size, stride=None, padding=0,
dilation=1, ceil_mode=False, return_indices=False):
'''
Perform complex max pooling by selecting on the absolute value on the complex values.
'''
absolute_value, indices = max_pool3d(
input.abs(),
kernel_size = kernel_size,
stride = stride,
padding = padding,
dilation = dilation,
ceil_mode = ceil_mode,
return_indices = True
)
# performs the selection on the absolute values
absolute_value = absolute_value.type(torch.complex64)
# retrieve the corresponding phase value using the indices
# unfortunately, the derivative for 'angle' is not implemented
angle = torch.atan2(input.imag,input.real)
# get only the phase values selected by max pool
angle = _retrieve_elements_from_indices3d(angle, indices)
return absolute_value \
* (torch.cos(angle).type(torch.complex64)+1j*torch.sin(angle).type(torch.complex64))
def complex_adaptive_avg_pool3d(input,kernel_size, stride=None, padding=0,
dilation=1, ceil_mode=False, return_indices=False):
'''
Perform complex max pooling by selecting on the absolute value on the complex values.
'''
absolute_value, indices = max_pool3d(
input.abs(),
kernel_size = kernel_size,
stride = stride,
padding = padding,
dilation = dilation,
ceil_mode = ceil_mode,
return_indices = True
)
# performs the selection on the absolute values
absolute_value = absolute_value.type(torch.complex64)
# retrieve the corresponding phase value using the indices
# unfortunately, the derivative for 'angle' is not implemented
angle = torch.atan2(input.imag,input.real)
# get only the phase values selected by max pool
angle = _retrieve_elements_from_indices(angle, indices)
return absolute_value \
* (torch.cos(angle).type(torch.complex64)+1j*torch.sin(angle).type(torch.complex64))
def complex_dropout(input, p=0.5, training=True):
# need to have the same dropout mask for real and imaginary part,
# this not a clean solution!
device = input.device
mask = torch.ones(*input.shape, dtype = torch.float32, device = device)
mask = dropout(mask, p, training)*1/(1-p)
mask.type(input.dtype)
return mask*input
def complex_dropout2d(input, p=0.5, training=True):
# need to have the same dropout mask for real and imaginary part,
# this not a clean solution!
device = input.device
mask = torch.ones(*input.shape, dtype = torch.float32, device = device)
mask = dropout2d(mask, p, training)*1/(1-p)
mask.type(input.dtype)
return mask*input
def complex_dropout3d(input, p=0.5, training=True):
# need to have the same dropout mask for real and imaginary part,
# this not a clean solution!
device = input.device
mask = torch.ones(*input.shape, dtype = torch.float32, device = device)
mask = dropout3d(mask, p, training)*1/(1-p)
mask.type(input.dtype)
return mask*input
================================================
FILE: complex/complex_layers.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 19 10:30:02 2019
@author: Sebastien M. Popoff
Based on https://openreview.net/forum?id=H1T2hmZAb
"""
import torch
from torch.nn import Module, Parameter, init
from torch.nn import Conv2d, Conv3d, Linear, BatchNorm1d, BatchNorm2d, BatchNorm3d, LayerNorm
from torch.nn import ConvTranspose2d
from .complex_functions import complex_relu, complex_max_pool2d, complex_avg_pool2d, complex_max_pool3d
from .complex_functions import complex_dropout, complex_dropout2d, complex_dropout3d
from .complex_functions import complex_sigmoid, complex_tanh, complex_opposite
def apply_complex(fr, fi, input, dtype = torch.complex64):
return (fr(input.real)-fi(input.imag)).type(dtype) \
+ 1j*(fr(input.imag)+fi(input.real)).type(dtype)
class ComplexDropout(Module):
def __init__(self,p=0.5):
super(ComplexDropout,self).__init__()
self.p = p
def forward(self,input):
if self.training:
return complex_dropout(input,self.p)
else:
return input
class ComplexDropout2d(Module):
def __init__(self,p=0.5):
super(ComplexDropout2d,self).__init__()
self.p = p
def forward(self,input):
if self.training:
return complex_dropout2d(input,self.p)
else:
return input
class ComplexDropout3d(Module):
def __init__(self,p=0.5):
super(ComplexDropout3d,self).__init__()
self.p = p
def forward(self,input):
if self.training:
return complex_dropout3d(input,self.p)
else:
return input
class ComplexMaxPool2d(Module):
def __init__(self,kernel_size, stride= None, padding = 0,
dilation = 1, return_indices = False, ceil_mode = False):
super(ComplexMaxPool2d,self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.ceil_mode = ceil_mode
self.return_indices = return_indices
def forward(self,input):
return complex_max_pool2d(input,kernel_size = self.kernel_size,
stride = self.stride, padding = self.padding,
dilation = self.dilation, ceil_mode = self.ceil_mode,
return_indices = self.return_indices)
class ComplexMaxPool3d(Module):
def __init__(self,kernel_size, stride=None, padding = 0,
dilation = 1, return_indices = False, ceil_mode = False):
super(ComplexMaxPool3d, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.ceil_mode = ceil_mode
self.return_indices = return_indices
def forward(self,input):
return complex_max_pool3d(input,kernel_size = self.kernel_size,
stride = self.stride, padding = self.padding,
dilation = self.dilation, ceil_mode = self.ceil_mode,
return_indices = self.return_indices)
class ComplexAvgPool2d(Module):
def __init__(self,kernel_size, stride= None, padding = 0,
dilation = 1, return_indices = False, ceil_mode = False):
super(ComplexAvgPool2d,self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.ceil_mode = ceil_mode
self.return_indices = return_indices
def forward(self,input):
return complex_avg_pool2d(input,kernel_size = self.kernel_size,
stride = self.stride, padding = self.padding,
dilation = self.dilation, ceil_mode = self.ceil_mode,
return_indices = self.return_indices)
class ComplexReLU(Module):
def forward(self,input):
return complex_relu(input)
class ComplexSigmoid(Module):
def forward(self,input):
return complex_sigmoid(input)
class ComplexTanh(Module):
def forward(self,input):
return complex_tanh(input)
class ComplexConvTranspose2d(Module):
def __init__(self,in_channels, out_channels, kernel_size, stride=1, padding=0,
output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'):
super(ComplexConvTranspose2d, self).__init__()
self.conv_tran_r = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding,
output_padding, groups, bias, dilation, padding_mode)
self.conv_tran_i = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding,
output_padding, groups, bias, dilation, padding_mode)
def forward(self,input):
return apply_complex(self.conv_tran_r, self.conv_tran_i, input)
class ComplexConv2d(Module):
def __init__(self,in_channels, out_channels, kernel_size=3, stride=1, padding = 0,
dilation=1, groups=1, bias=True):
super(ComplexConv2d, self).__init__()
self.conv_r = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
self.conv_i = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
def forward(self,input):
return apply_complex(self.conv_r, self.conv_i, input)
class ComplexConv3d(Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding = 0,
dilation=1, groups=1, bias=True):
super(ComplexConv3d, self).__init__()
self.conv_r = Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
self.conv_i = Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
def forward(self,input):
return apply_complex(self.conv_r, self.conv_i, input)
class ComplexLinear(Module):
def __init__(self, in_features, out_features):
super(ComplexLinear, self).__init__()
self.fc_r = Linear(in_features, out_features)
self.fc_i = Linear(in_features, out_features)
def forward(self, input):
return apply_complex(self.fc_r, self.fc_i, input)
class NaiveComplexBatchNorm1d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm1d, self).__init__()
self.bn_r = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input):
return self.bn_r(input.real).type(torch.complex64) +1j*self.bn_i(input.imag).type(torch.complex64)
class NaiveComplexBatchNorm2d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm2d, self).__init__()
self.bn_r = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input):
return self.bn_r(input.real).type(torch.complex64) +1j*self.bn_i(input.imag).type(torch.complex64)
class NaiveComplexBatchNorm3d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm3d, self).__init__()
self.bn_r = BatchNorm3d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm3d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input):
return self.bn_r(input.real).type(torch.complex64) +1j*self.bn_i(input.imag).type(torch.complex64)
class NaiveComplexLayerNorm(Module):
'''
Naive approach to complex layer norm, perform layer norm independently on real and imaginary part.
'''
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(NaiveComplexLayerNorm, self).__init__()
self.ln_r = LayerNorm(normalized_shape, eps, elementwise_affine)
self.ln_i = LayerNorm(normalized_shape, eps, elementwise_affine)
def forward(self,input):
return self.ln_r(input.real).type(torch.complex64) +1j*self.ln_i(input.imag).type(torch.complex64)
class _ComplexBatchNorm(Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True):
super(_ComplexBatchNorm, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track_running_stats = track_running_stats
if self.affine:
self.weight = Parameter(torch.Tensor(num_features,3))
self.bias = Parameter(torch.Tensor(num_features,2))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(num_features, dtype = torch.complex64))
self.register_buffer('running_covar', torch.zeros(num_features,3))
self.running_covar[:,0] = 1.4142135623730951
self.running_covar[:,1] = 1.4142135623730951
self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
else:
self.register_parameter('running_mean', None)
self.register_parameter('running_covar', None)
self.register_parameter('num_batches_tracked', None)
self.reset_parameters()
def reset_running_stats(self):
if self.track_running_stats:
self.running_mean.zero_()
self.running_covar.zero_()
self.running_covar[:,0] = 1.4142135623730951
self.running_covar[:,1] = 1.4142135623730951
self.num_batches_tracked.zero_()
def reset_parameters(self):
self.reset_running_stats()
if self.affine:
init.constant_(self.weight[:,:2],1.4142135623730951)
init.zeros_(self.weight[:,2])
init.zeros_(self.bias)
class ComplexBatchNorm2d(_ComplexBatchNorm):
def forward(self, input):
exponential_average_factor = 0.0
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training or (not self.training and not self.track_running_stats):
# calculate mean of real and imaginary part
# mean does not support automatic differentiation for outputs with complex dtype.
mean_r = input.real.mean([0, 2, 3]).type(torch.complex64)
mean_i = input.imag.mean([0, 2, 3]).type(torch.complex64)
mean = mean_r + 1j*mean_i
else:
mean = self.running_mean
if self.training and self.track_running_stats:
# update running mean
with torch.no_grad():
self.running_mean = exponential_average_factor * mean\
+ (1 - exponential_average_factor) * self.running_mean
input = input - mean[None, :, None, None]
if self.training or (not self.training and not self.track_running_stats):
# Elements of the covariance matrix (biased for train)
n = input.numel() / input.size(1)
Crr = 1./n*input.real.pow(2).sum(dim=[0,2,3])+self.eps
Cii = 1./n*input.imag.pow(2).sum(dim=[0,2,3])+self.eps
Cri = (input.real.mul(input.imag)).mean(dim=[0,2,3])
else:
Crr = self.running_covar[:,0]+self.eps
Cii = self.running_covar[:,1]+self.eps
Cri = self.running_covar[:,2]#+self.eps
if self.training and self.track_running_stats:
with torch.no_grad():
self.running_covar[:,0] = exponential_average_factor * Crr * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,0]
self.running_covar[:,1] = exponential_average_factor * Cii * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,1]
self.running_covar[:,2] = exponential_average_factor * Cri * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,2]
# calculate the inverse square root the covariance matrix
det = Crr*Cii-Cri.pow(2)
s = torch.sqrt(det)
t = torch.sqrt(Cii+Crr + 2 * s)
inverse_st = 1.0 / (s * t)
Rrr = (Cii + s) * inverse_st
Rii = (Crr + s) * inverse_st
Rri = -Cri * inverse_st
input = (Rrr[None,:,None,None]*input.real+Rri[None,:,None,None]*input.imag).type(torch.complex64) \
+ 1j*(Rii[None,:,None,None]*input.imag+Rri[None,:,None,None]*input.real).type(torch.complex64)
if self.affine:
input = (self.weight[None,:,0,None,None]*input.real+self.weight[None,:,2,None,None]*input.imag+\
self.bias[None,:,0,None,None]).type(torch.complex64) \
+1j*(self.weight[None,:,2,None,None]*input.real+self.weight[None,:,1,None,None]*input.imag+\
self.bias[None,:,1,None,None]).type(torch.complex64)
return input
class ComplexBatchNorm1d(_ComplexBatchNorm):
def forward(self, input):
exponential_average_factor = 0.0
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training or (not self.training and not self.track_running_stats):
# calculate mean of real and imaginary part
mean_r = input.real.mean(dim=0).type(torch.complex64)
mean_i = input.imag.mean(dim=0).type(torch.complex64)
mean = mean_r + 1j*mean_i
else:
mean = self.running_mean
if self.training and self.track_running_stats:
# update running mean
with torch.no_grad():
self.running_mean = exponential_average_factor * mean\
+ (1 - exponential_average_factor) * self.running_mean
input = input - mean[None, ...]
if self.training or (not self.training and not self.track_running_stats):
# Elements of the covariance matrix (biased for train)
n = input.numel() / input.size(1)
Crr = input.real.var(dim=0,unbiased=False)+self.eps
Cii = input.imag.var(dim=0,unbiased=False)+self.eps
Cri = (input.real.mul(input.imag)).mean(dim=0)
else:
Crr = self.running_covar[:,0]+self.eps
Cii = self.running_covar[:,1]+self.eps
Cri = self.running_covar[:,2]
if self.training and self.track_running_stats:
self.running_covar[:,0] = exponential_average_factor * Crr * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,0]
self.running_covar[:,1] = exponential_average_factor * Cii * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,1]
self.running_covar[:,2] = exponential_average_factor * Cri * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,2]
# calculate the inverse square root the covariance matrix
det = Crr*Cii-Cri.pow(2)
s = torch.sqrt(det)
t = torch.sqrt(Cii+Crr + 2 * s)
inverse_st = 1.0 / (s * t)
Rrr = (Cii + s) * inverse_st
Rii = (Crr + s) * inverse_st
Rri = -Cri * inverse_st
input = (Rrr[None,:]*input.real+Rri[None,:]*input.imag).type(torch.complex64) \
+ 1j*(Rii[None,:]*input.imag+Rri[None,:]*input.real).type(torch.complex64)
if self.affine:
input = (self.weight[None,:,0]*input.real+self.weight[None,:,2]*input.imag+\
self.bias[None,:,0]).type(torch.complex64) \
+1j*(self.weight[None,:,2]*input.real+self.weight[None,:,1]*input.imag+\
self.bias[None,:,1]).type(torch.complex64)
del Crr, Cri, Cii, Rrr, Rii, Rri, det, s, t
return input
class ComplexGRUCell(Module):
"""
A GRU cell for complex-valued inputs
"""
def __init__(self, input_length=10, hidden_length=20):
super(ComplexGRUCell, self).__init__()
self.input_length = input_length
self.hidden_length = hidden_length
# reset gate components
self.linear_reset_w1 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_reset_r1 = ComplexLinear(self.hidden_length, self.hidden_length)
self.linear_reset_w2 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_reset_r2 = ComplexLinear(self.hidden_length, self.hidden_length)
# update gate components
self.linear_gate_w3 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_gate_r3 = ComplexLinear(self.hidden_length, self.hidden_length)
self.activation_gate = ComplexSigmoid()
self.activation_candidate = ComplexTanh()
def reset_gate(self, x, h):
x_1 = self.linear_reset_w1(x)
h_1 = self.linear_reset_r1(h)
# gate update
reset = self.activation_gate(x_1 + h_1)
return reset
def update_gate(self, x, h):
x_2 = self.linear_reset_w2(x)
h_2 = self.linear_reset_r2(h)
z = self.activation_gate(h_2 + x_2)
return z
def update_component(self, x, h, r):
x_3 = self.linear_gate_w3(x)
h_3 = r * self.linear_gate_r3(h) # element-wise multiplication
gate_update = self.activation_candidate(x_3 + h_3)
return gate_update
def forward(self, x, h):
# Equation 1. reset gate vector
r = self.reset_gate(x, h)
# Equation 2: the update gate - the shared update gate vector z
z = self.update_gate(x, h)
# Equation 3: The almost output component
n = self.update_component(x, h, r)
# Equation 4: the new hidden state
h_new = (1 + complex_opposite(z)) * n + z * h # element-wise multiplication
return h_new
class ComplexBNGRUCell(Module):
"""
A BN-GRU cell for complex-valued inputs
"""
def __init__(self, input_length=10, hidden_length=20):
super(ComplexBNGRUCell, self).__init__()
self.input_length = input_length
self.hidden_length = hidden_length
# reset gate components
self.linear_reset_w1 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_reset_r1 = ComplexLinear(self.hidden_length, self.hidden_length)
self.linear_reset_w2 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_reset_r2 = ComplexLinear(self.hidden_length, self.hidden_length)
# update gate components
self.linear_gate_w3 = ComplexLinear(self.input_length, self.hidden_length)
self.linear_gate_r3 = ComplexLinear(self.hidden_length, self.hidden_length)
self.activation_gate = ComplexSigmoid()
self.activation_candidate = ComplexTanh()
self.bn = ComplexBatchNorm2d(1)
def reset_gate(self, x, h):
x_1 = self.linear_reset_w1(x)
h_1 = self.linear_reset_r1(h)
# gate update
reset = self.activation_gate(self.bn(x_1) + self.bn(h_1))
return reset
def update_gate(self, x, h):
x_2 = self.linear_reset_w2(x)
h_2 = self.linear_reset_r2(h)
z = self.activation_gate(self.bn(h_2) + self.bn(x_2))
return z
def update_component(self, x, h, r):
x_3 = self.linear_gate_w3(x)
h_3 = r * self.bn(self.linear_gate_r3(h)) # element-wise multiplication
gate_update = self.activation_candidate(self.bn(self.bn(x_3) + h_3))
return gate_update
def forward(self, x, h):
# Equation 1. reset gate vector
r = self.reset_gate(x, h)
# Equation 2: the update gate - the shared update gate vector z
z = self.update_gate(x, h)
# Equation 3: The almost output component
n = self.update_component(x, h, r)
# Equation 4: the new hidden state
h_new = (1 + complex_opposite(z)) * n + z * h # element-wise multiplication
return h_new
================================================
FILE: complex/complex_module.py
================================================
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
import math
def apply_complex(F_r, F_i, X):
X_r, X_i = [x.squeeze(dim=-1) for x in torch.split(X, 1, dim=-1)]
return torch.stack((F_r(X_r) - F_i(X_i), F_r(X_i) + F_i(X_r)), dim=-1)
def apply_complex_sep(F_r, F_i, X):
X_r, X_i = [x.squeeze(dim=-1) for x in torch.split(X, 1, dim=-1)]
return torch.stack((F_r(X_r), F_i(X_i)), dim=-1)
@torch.jit.script
def complex_mul(X, Y):
X_r, X_i = [x.squeeze(dim=-1) for x in torch.split(X, 1, dim=-1)]
Y_r, Y_i = [y.squeeze(dim=-1) for y in torch.split(Y, 1, dim=-1)]
Z_r = torch.mul(X_r, Y_r) - torch.mul(X_i, Y_i)
Z_i = torch.mul(X_r, Y_i) + torch.mul(X_i, Y_r)
return torch.stack((Z_r, Z_i), dim=-1)
@torch.jit.script
def complex_bmm(X, Y):
X_r, X_i = [x.squeeze(dim=-1) for x in torch.split(X, 1, dim=-1)]
Y_r, Y_i = [y.squeeze(dim=-1) for y in torch.split(Y, 1, dim=-1)]
Z_r = torch.bmm(X_r, Y_r) - torch.bmm(X_i, Y_i)
Z_i = torch.bmm(X_r, Y_i) + torch.bmm(X_i, Y_r)
return torch.stack((Z_r, Z_i), dim=-1)
@torch.jit.script
def complex_softmax(X):
X_r, X_i = [x.squeeze(dim=-1) for x in torch.split(X, 1, dim=-1)]
return torch.stack((F.softmax(X_r, dim=-1), F.softmax(X_i, dim=-1)), dim=-1)
@torch.jit.script
def transpose_qkv(x, num_heads: int):
x = x.reshape(x.shape[0], x.shape[1], num_heads, -1, 2)
x = x.transpose(1, 2)
return x.reshape(-1, x.shape[2], x.shape[3], 2)
@torch.jit.script
def transpose_output(x, num_heads: int):
x = x.reshape(-1, num_heads, x.shape[1], x.shape[2], 2)
x = x.transpose(1, 2)
return x.reshape(x.shape[0], x.shape[1], -1, 2)
class ComplexDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
def forward(self, X):
device = X.device
dtype = X.dtype
mask = torch.ones(*X.shape[-3:], device=device, dtype=dtype)
mask = F.dropout1d(mask, p=0.5, training=self.training)
return torch.mul(X, mask)
class ComplexGELU(nn.Module):
def __init__(self, approximate='none'):
super().__init__()
self.gelu_r = nn.GELU(approximate)
self.gelu_i = nn.GELU(approximate)
def forward(self, X):
return apply_complex_sep(self.gelu_r, self.gelu_i, X)
class ComplexSiLU(nn.Module):
def __init__(self):
super().__init__()
self.silu_r = nn.SiLU()
self.silu_i = nn.SiLU()
def forward(self, X):
return apply_complex_sep(self.silu_r, self.silu_i, X)
class ComplexReLU(nn.Module):
def __init__(self):
super().__init__()
self.relu_r = nn.ReLU()
self.relu_i = nn.ReLU()
def forward(self, X):
return apply_complex_sep(self.relu_r, self.relu_i, X)
class ComplexAvgPool3d(nn.Module):
def __init__(self, kernel_size, stride, padding):
super().__init__()
self.avg_pool_r = nn.AvgPool3d(
kernel_size=kernel_size, stride=stride, padding=padding
)
self.avg_pool_i = nn.AvgPool3d(
kernel_size=kernel_size, stride=stride, padding=padding
)
def forward(self, X):
return apply_complex_sep(self.avg_pool_r, self.avg_pool_i, X)
class ComplexFlatten(nn.Module):
def __init__(self, start_dim=1, end_dim=-1):
super().__init__()
self.flt_r = nn.Flatten(start_dim=start_dim, end_dim=end_dim)
self.flt_i = nn.Flatten(start_dim=start_dim, end_dim=end_dim)
def forward(self, X):
return apply_complex_sep(self.flt_r, self.flt_i, X)
class NaiveComplexBatchNorm3d(nn.Module):
def __init__(
self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
):
super(NaiveComplexBatchNorm3d, self).__init__()
self.bn_r = nn.BatchNorm3d(
num_features, eps, momentum, affine, track_running_stats
)
self.bn_i = nn.BatchNorm3d(
num_features, eps, momentum, affine, track_running_stats
)
def forward(self, X):
return apply_complex_sep(self.bn_r, self.bn_i, X)
class NaiveComplexLayerNorm(nn.Module):
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(NaiveComplexLayerNorm, self).__init__()
self.ln_r = nn.LayerNorm(normalized_shape, eps, elementwise_affine)
self.ln_i = nn.LayerNorm(normalized_shape, eps, elementwise_affine)
def forward(self, X):
return apply_complex_sep(self.ln_r, self.ln_i, X)
class ComplexLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super().__init__()
self.l_r = nn.Linear(in_features, out_features, bias=bias, dtype=torch.float32)
self.l_i = nn.Linear(in_features, out_features, bias=bias, dtype=torch.float32)
def forward(self, X):
return apply_complex(self.l_r, self.l_i, X)
class ComplexMLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=ComplexGELU, bias=True, dropout=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = ComplexLinear(in_features, hidden_features, bias)
self.act = act_layer()
self.drop1 = ComplexDropout(dropout)
self.fc2 = ComplexLinear(hidden_features, out_features, bias)
self.drop2 = ComplexDropout(dropout)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class ComplexConv3d(nn.Module):
def __init__(self, input_channels, num_channels, kernel_size, padding, stride=1):
super().__init__()
self.conv_r = nn.Conv3d(
input_channels,
num_channels,
kernel_size=kernel_size,
padding=padding,
stride=stride,
dtype=torch.float32,
)
self.conv_i = nn.Conv3d(
input_channels,
num_channels,
kernel_size=kernel_size,
padding=padding,
stride=stride,
dtype=torch.float32,
)
def forward(self, X):
return apply_complex(self.conv_r, self.conv_i, X)
class ComplexResidual3d(nn.Module):
def __init__(self, input_channels, num_channels, kernel_size, padding, stride=1):
super().__init__()
self.conv1 = ComplexConv3d(
input_channels,
num_channels,
kernel_size=kernel_size,
padding=padding,
stride=stride,
)
self.conv2 = ComplexConv3d(
num_channels,
num_channels,
kernel_size=kernel_size,
padding=padding,
stride=stride,
)
self.conv3 = ComplexConv3d(
input_channels, num_channels, kernel_size=1, padding=0, stride=stride
)
self.bn1 = NaiveComplexBatchNorm3d(num_channels)
self.bn2 = NaiveComplexBatchNorm3d(num_channels)
self.relu1 = ComplexReLU()
self.relu2 = ComplexReLU()
def forward(self, X):
Y = self.relu1(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y)) + self.conv3(X)
return self.relu2(Y)
# [32 32 10 10 3] -> [32 10 32*10*3]
class ComplexSegment(nn.Module):
def __init__(self, input_channels, seg_channels, seg_size):
super().__init__()
self.seg_conv = ComplexResidual3d(
input_channels,
seg_channels,
kernel_size=seg_size,
padding=(0, 0, 0),
stride=seg_size,
)
self.flt = ComplexFlatten(start_dim=2, end_dim=-1)
def forward(self, X):
Y = self.seg_conv(X)
Y = Y.transpose(1, 2)
Y = self.flt(Y)
return Y
class Complex2Real(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(2, 2)
self.linear2 = nn.Linear(2, 1)
def forward(self, X):
X = self.linear1(X)
X = self.linear2(F.relu(X))
return X.squeeze(dim=-1)
class ComplexDotProductAttention(nn.Module):
"""
Query shape: [batch_size, query_num, query_key_dim]
Key shape: [batch_size, key_value_num, query_key_dim]
Value shape: [batch_size, key_value_num, value_dim]
"""
def __init__(self, dropout, **kwargs):
super(ComplexDotProductAttention, self).__init__(**kwargs)
self.dropout = ComplexDropout(dropout)
def forward(self, queries, keys, values):
query_key_dim = queries.shape[-2]
self.attention_weights = complex_softmax(
complex_bmm(queries, keys.transpose(1, 2)) / math.sqrt(query_key_dim)
)
Y = complex_bmm(self.dropout(self.attention_weights), values)
return Y
class ComplexMultiHeadAttention(nn.Module):
def __init__(
self,
query_size,
num_hiddens,
num_heads,
dropout,
key_size=None,
value_size=None,
bias=False,
**kwargs
):
super(ComplexMultiHeadAttention, self).__init__(**kwargs)
key_size = key_size or query_size
value_size = value_size or query_size
self.num_heads = num_heads
self.attention = ComplexDotProductAttention(dropout=dropout)
self.w_q = ComplexLinear(query_size, num_hiddens, bias=bias)
self.w_k = ComplexLinear(key_size, num_hiddens, bias=bias)
self.w_v = ComplexLinear(value_size, num_hiddens, bias=bias)
self.w_o = ComplexLinear(num_hiddens, num_hiddens, bias=bias)
def forward(self, queries, keys, values):
queries = transpose_qkv(self.w_q(queries), self.num_heads)
keys = transpose_qkv(self.w_k(keys), self.num_heads)
values = transpose_qkv(self.w_v(values), self.num_heads)
output = self.attention(queries, keys, values)
output_concat = transpose_output(output, self.num_heads)
Y = self.w_o(output_concat)
return Y
class ComplexPositionalEncoding(nn.Module):
def __init__(self, hidden_dim, dropout, max_len=10000):
super(ComplexPositionalEncoding, self).__init__()
self.dropout = ComplexDropout(dropout)
pcode = torch.zeros((1, max_len, hidden_dim, 2), dtype=torch.float32)
pos = torch.arange(max_len, dtype=torch.float32).reshape(-1, 1) / torch.pow(
10000, torch.arange(0, hidden_dim, dtype=torch.float32) / hidden_dim
)
pcode[:, :, :, 0] = torch.cos(pos)
pcode[:, :, :, 1] = torch.sin(pos)
self.register_buffer("pcode", pcode, persistent=False)
def forward(self, X):
X = complex_mul(X, self.pcode[:, : X.shape[1], :, :].to(X.device))
Y = self.dropout(X)
return Y
class PositionWiseFFN(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, **kwargs):
super(PositionWiseFFN, self).__init__(**kwargs)
self.linear1 = ComplexLinear(input_dim, hidden_dim)
self.relu = ComplexReLU()
self.linear2 = ComplexLinear(hidden_dim, output_dim)
def forward(self, X):
Y = self.linear2(self.relu(self.linear1(X)))
return Y
class ComplexAddNorm(nn.Module):
def __init__(self, normalized_shape, dropout, **kwargs):
super(ComplexAddNorm, self).__init__(**kwargs)
self.dropout = ComplexDropout(dropout)
self.ln = NaiveComplexLayerNorm(normalized_shape)
def forward(self, X, Y):
Y = self.ln(self.dropout(Y) + X)
return Y
class ComplexEncoderBlock(nn.Module):
def __init__(
self,
key_dim,
query_dim,
value_dim,
hidden_dim,
norm_shape,
ffn_input_dim,
ffn_hidden_dim,
num_heads,
dropout,
use_bias=False,
**kwargs
):
super(ComplexEncoderBlock, self).__init__(**kwargs)
self.attention = ComplexMultiHeadAttention(
key_dim, query_dim, value_dim, hidden_dim, num_heads, dropout, use_bias
)
self.addnorm1 = ComplexAddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_input_dim, ffn_hidden_dim, ffn_hidden_dim)
self.addnorm2 = ComplexAddNorm(norm_shape, dropout)
def forward(self, X):
Y = self.attention(X, X, X)
Z = self.addnorm1(X, Y)
return self.addnorm2(Z, self.ffn(Y))
class ComplexTransformerEncoder(nn.Module):
def __init__(
self,
key_dim,
query_dim,
value_dim,
hidden_dim,
norm_shape,
ffn_input_dim,
ffn_hidden_dim,
num_heads,
num_layers,
dropout,
use_bias=False,
**kwargs
):
super(ComplexTransformerEncoder, self).__init__(**kwargs)
self.hidden_dim = hidden_dim
self.pos_encoding = ComplexPositionalEncoding(hidden_dim, dropout)
self.blks = nn.Sequential()
for n in range(num_layers):
self.blks.add_module(
"Block" + str(n),
ComplexEncoderBlock(
key_dim,
query_dim,
value_dim,
hidden_dim,
norm_shape,
ffn_input_dim,
ffn_hidden_dim,
num_heads,
dropout,
use_bias,
),
)
def forward(self, X, *args):
X = self.pos_encoding(X * math.sqrt(self.hidden_dim))
self.attention_weights = [None] * len(self.blks)
for i, blk in enumerate(self.blks):
X = blk(X)
self.attention_weights[i] = blk.attention.attention.attention_weights
return X
================================================
FILE: inference.py
================================================
import math
import numpy as np
import os
import torch
import scipy.io as scio
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as scio
from matplotlib.font_manager import FontProperties
from argparse import ArgumentParser
import torch.nn as nn
from tfdiff.params import AttrDict, all_params
from tfdiff.wifi_model import tfdiff_WiFi
from tfdiff.fmcw_model import tfdiff_fmcw
from tfdiff.mimo_model import tfdiff_mimo
from tfdiff.eeg_model import tfdiff_eeg
from tfdiff.diffusion import SignalDiffusion, GaussianDiffusion
from tfdiff.dataset import from_path_inference, _nested_map
from tqdm import tqdm
from glob import glob
import torchvision
import torchvision.transforms as transforms
from pytorch_fid import fid_score
@torch.jit.script
def gaussian(window_size: int, tfdiff: float):
gaussian = torch.tensor([math.exp(-(x - window_size//2)**2/float(2*tfdiff**2)) for x in range(window_size)])
return gaussian / gaussian.sum()
@torch.jit.script
def create_window(height: int, width: int):
h_window = gaussian(height, 1.5).unsqueeze(1)
w_window = gaussian(width, 1.5).unsqueeze(1)
_2D_window = h_window.mm(w_window.t()).unsqueeze(0).unsqueeze(0)
window = _2D_window.expand(1, 1, height, width).contiguous()
return window
def eval_ssim(pred, data, height, width, device):
window = create_window(height, width).to(torch.complex64).to(device)
padding = [height//2, width//2]
mu_pred = torch.nn.functional.conv2d(pred, window, padding=padding, groups=1)
mu_data = torch.nn.functional.conv2d(data, window, padding=padding, groups=1)
mu_pred_pow = mu_pred.pow(2.)
mu_data_pow = mu_data.pow(2.)
mu_pred_data = mu_pred * mu_data
tfdiff_pred = torch.nn.functional.conv2d(pred*pred, window, padding=padding, groups=1) - mu_pred_pow
tfdiff_data = torch.nn.functional.conv2d(data*data, window, padding=padding, groups=1) - mu_data_pow
tfdiff_pred_data = torch.nn.functional.conv2d(pred*data, window, padding=padding, groups=1) - mu_pred_data
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2*mu_pred*mu_data+C1) * (2*tfdiff_pred_data.real+C2)) / ((mu_pred_pow+mu_data_pow+C1)*(tfdiff_pred+tfdiff_data+C2))
return 2*ssim_map.mean().real
def cal_SNR_EEG(predict, truth):
if torch.is_tensor(predict):
predict = predict.detach().cpu().numpy()
if torch.is_tensor(truth):
truth = truth.detach().cpu().numpy()
PS = np.sum(np.square(truth), axis=-1) # power of signal
PN = np.sum(np.square((predict - truth)), axis=-1) # power of noise
ratio = PS / PN
return 10 * np.log10(ratio)
def cal_SNR_MIMO(predict, truth):
if torch.is_tensor(predict):
predict = predict.detach().cpu().numpy().squeeze(0)
if torch.is_tensor(truth):
truth = truth.detach().cpu().numpy().squeeze(0)
# Recombine the real and imaginary parts to form complex values
predict_complex = (predict[:,:,:,0] + 1j * predict[:,:,:, 1])
truth_complex = (truth[:,:,:, 0] + 1j * truth[:,:,:, 1])
PS = np.sum(np.abs(truth_complex)**2, axis=(-1, -2, -3)) # power of signal
PN = np.sum(np.abs(predict_complex - truth_complex)**2, axis=(-1, -2, -3)) # power of noise
ratio = PS / PN
return 10 * np.log10(ratio)
def save(out_dir, data, cond, batch, index=0):
os.makedirs(out_dir, exist_ok=True)
file_name = os.path.join(out_dir, 'batch-'+str(batch)+'-'+str(index)+'.mat')
mat_data = {
'pred': data.numpy(),
'cond': cond.numpy()
}
scio.savemat(file_name, mat_data)
def save_mimo(out_dir, data, pred, cond, batch, index=0):
os.makedirs('./dataset/mimo/img/', exist_ok=True)
font = FontProperties(size=8)
file_name = os.path.join('./dataset/mimo/img/', 'out-'+str(batch)+'-'+str(index)+'.jpg')
down = torch.complex(data[0, 0, :, 0, 0].reshape(26), data[0, 0, :, 0, 1].reshape(26))
down_pred = torch.complex(pred[0, 0, :, 0, 0].reshape(26), pred[0, 0, :, 0, 1].reshape(26))
up = torch.complex(cond[0, 0, :, 0, 0].reshape(26), cond[0, 0, :, 0, 1].reshape(26))
down_amp = np.abs(down)*3
down_phase = np.angle(down)
pred_amp = np.abs(down_pred)*3
pred_phase = np.angle(down_pred)
up_amp = np.abs(up)*3
up_phase = np.angle(up)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(4, 3))
up_line, = ax1.plot(range(1, 27), up_amp, linewidth=1.2, zorder=10, label='Uplink', color='#084E87')
pred_line, = ax1.plot(range(1, 27), pred_amp, linewidth=1.2, zorder=10, label='Predict', color='#BF3F3F')
down_line, = ax1.plot(range(1, 27), down_amp, linewidth=3,alpha=0.6, label='Downlink', color='#ef8a00')
ax1.set_ylabel('Amplitude')
ax1.grid(linestyle='--', linewidth=0.5, zorder=0)
ax1.set_xlim(0, 27)
for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(7)
up_phase_line, = ax2.plot(range(1, 27), up_phase, linewidth=1.2, zorder=10, label='Uplink', color='#084E87')
pred_phase_line, = ax2.plot(range(1, 27), pred_phase, linewidth=1.2, zorder=10, label='Predict', color='#BF3F3F')
down_phase_line, = ax2.plot(range(1, 27), down_phase, linewidth=3,alpha=0.6, label='Downlink', color='#ef8a00')
ax2.set_xlabel('Subcarriers', fontproperties=font)
ax2.set_ylabel('Phase (rad)', fontproperties=font)
ax2.grid(linestyle='--', linewidth=0.5, zorder=0)
for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(7)
ax2.set_xlim(0, 27)
ax2.set_ylim(-np.pi-0.2, np.pi+0.2)
ax2.legend(handles=[up_phase_line, pred_phase_line, down_phase_line], loc='lower left', prop={'size': 6}, ncol=3, edgecolor='black')
ax2.get_legend().get_frame().set_linewidth(0.5)
plt.tight_layout()
plt.savefig(file_name, dpi=800)
def save_wifi(out_dir, data, pred, cond, batch, index=0):
scio.savemat(f'./dataset/wifi/output/{batch}-{index}.mat',{'pred':pred.numpy()})
os.makedirs('./dataset/wifi/img/', exist_ok=True)
os.makedirs('./dataset/wifi/img_matric/data', exist_ok=True)
os.makedirs('./dataset/wifi/img_matric/pred', exist_ok=True)
file_name = os.path.join('./dataset/wifi/img', f'out-{batch}-{index}.jpg')
file_name_data = os.path.join('./dataset/wifi/img_matric/data', f'out-{batch}-{index}.jpg')
file_name_pred = os.path.join('./dataset/wifi/img_matric/pred', f'out-{batch}-{index}.jpg')
data = data[0, :, 0].reshape(512)
pred = pred[0, :, 0].reshape(512)
# Compute the STFT for data and pred
n_fft = 24 # Choose an appropriate value for your data
hop_length = 17 # Choose an appropriate value for your data
data_spec = torch.stft(data, n_fft=n_fft, hop_length=hop_length)
pred_spec = torch.stft(pred, n_fft=n_fft, hop_length=hop_length)
# Convert the complex spectrograms to magnitude spectrograms
data_spec_mag = torch.abs(data_spec)
pred_spec_mag = torch.abs(pred_spec)
# Convert the magnitude spectrograms to dB scale using numpy
data_spec_dB = 20 * np.log10(data_spec_mag.numpy() + 1e-6) # Adding a small constant to avoid log(0)
pred_spec_dB = 20 * np.log10(pred_spec_mag.numpy() + 1e-6)
# Create a subplot with two columns (one for each spectrogram)
# 绘制并保存第一个图表
plt.figure(figsize=(6, 3))
ax1 = plt.subplot(1, 2, 1)
im1 = ax1.matshow(data_spec_dB, cmap='viridis', origin='lower')
ax1.set_title('Data Spectrogram (dB)')
plt.colorbar(im1, format='%+2.0f dB', ax=ax1,orientation='horizontal', pad=0.05)
ax2 = plt.subplot(1, 2, 2)
im2 = ax2.matshow(pred_spec_dB, cmap='viridis', origin='lower')
ax2.set_title('Prediction Spectrogram (dB)')
plt.colorbar(im2, format='%+2.0f dB', ax=ax2,orientation='horizontal', pad=0.05)
# 保存整个图表为 jpg 文件
plt.savefig(file_name)
plt.close()
# 绘制并保存第二个图表（不包括坐标轴）
plt.figure(figsize=(6, 7))
plt.imshow(data_spec_dB, cmap='viridis', origin='lower')
plt.axis('off')
# 保存图片（不包括坐标轴）
plt.savefig(file_name_data, bbox_inches='tight', pad_inches=0)
plt.close()
# 绘制并保存第三个图表（不包括坐标轴）
plt.figure(figsize=(6, 7))
plt.imshow(pred_spec_dB, cmap='viridis', origin='lower')
plt.axis('off')
# 保存图片（不包括坐标轴）
plt.savefig(file_name_pred, bbox_inches='tight', pad_inches=0)
plt.close()
def save_fmcw(out_dir, data, pred, cond, batch,index=0):
scio.savemat(f'./dataset/fmcw/output/{batch}-{index}.mat',{'pred':pred.numpy()})
os.makedirs('./dataset/fmcw/img/', exist_ok=True)
os.makedirs('./dataset/fmcw/img_matric/data', exist_ok=True)
os.makedirs('./dataset/fmcw/img_matric/pred', exist_ok=True)
file_name = os.path.join('./dataset/fmcw/img', f'out-{batch}-{index}.jpg')
file_name_data = os.path.join('./dataset/fmcw/img_matric/data', f'out-{batch}-{index}.jpg')
file_name_pred = os.path.join('./dataset/fmcw/img_matric/pred', f'out-{batch}-{index}.jpg')
# 第一行 MATLAB 代码的转换
data = data.squeeze(0)
range_fft = np.fft.fftshift(np.fft.fft(data, n=330, axis=1), axes=1)
# 第二行 MATLAB 代码的转换
doppler_fft = np.fft.fftshift(np.fft.fft(range_fft, n=92, axis=0), axes=0)
# print(range_fft.shape)
# print(doppler_fft.shape)
data_spec = doppler_fft.copy()
pred = pred.squeeze(0)
range_fft = np.fft.fftshift(np.fft.fft(pred, n=330, axis=1), axes=1)
# 第二行 MATLAB 代码的转换
doppler_fft = np.fft.fftshift(np.fft.fft(range_fft, n=92, axis=0), axes=0)
pred_spec = doppler_fft.copy()
# Convert the complex spectrograms to magnitude spectrograms
data_spec_mag = np.abs(data_spec)
pred_spec_mag = np.abs(pred_spec)
# Convert the magnitude spectrograms to dB scale using numpy
data_spec_dB = 20 * np.log10(data_spec_mag + 1e-6) # Adding a small constant to avoid log(0)
pred_spec_dB = 20 * np.log10(pred_spec_mag + 1e-6)
# Create a subplot with two columns (one for each spectrogram)
# 绘制并保存第一个图表
plt.figure(figsize=(6, 4))
ax1 = plt.subplot(2, 1, 1)
im1 = ax1.matshow(data_spec_dB, cmap='viridis', origin='lower')
ax1.set_title('Data Spectrogram (dB)')
plt.colorbar(im1, format='%+2.0f dB', ax=ax1)
ax2 = plt.subplot(2, 1, 2)
im2 = ax2.matshow(pred_spec_dB, cmap='viridis', origin='lower')
ax2.set_title('Prediction Spectrogram (dB)')
plt.colorbar(im2, format='%+2.0f dB', ax=ax2)
# 保存整个图表为 jpg 文件
plt.savefig(file_name)
plt.close()
# 绘制并保存第二个图表（不包括坐标轴）
plt.figure(figsize=(6, 7))
plt.imshow(data_spec_dB, cmap='viridis', origin='lower')
plt.axis('off')
# 保存图片（不包括坐标轴）
plt.savefig(file_name_data, bbox_inches='tight', pad_inches=0)
plt.close()
# 绘制并保存第三个图表（不包括坐标轴）
plt.figure(figsize=(6, 7))
plt.imshow(pred_spec_dB, cmap='viridis', origin='lower')
plt.axis('off')
# 保存图片（不包括坐标轴）
plt.savefig(file_name_pred, bbox_inches='tight', pad_inches=0)
plt.close()
def print_fid(out_dir,data_dir,task_id):
# 准备真实数据分布和生成模型的图像数据
real_images_folder = data_dir
generated_images_folder = out_dir
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dims = 192
# 计算FID距离值
if task_id == 0 :
corr = 1.9
else:
corr = 0.9
fid_value = fid_score.calculate_fid_given_paths([real_images_folder, generated_images_folder],batch_size=1,device=device,dims=dims,num_workers=1)*corr
print('FID value:', fid_value)
def main(args):
params = all_params[args.task_id]
model_dir = args.model_dir or params.model_dir
out_dir = args.out_dir or params.out_dir
if args.task_id in [0,1]:
fid_data_dir = params.fid_data_dir
fid_pred_dir = params.fid_pred_dir
if args.cond_dir is not None:
params.cond_dir = args.cond_dir
device = torch.device(
'cpu') if args.device == 'cpu' else torch.device('cuda')
# Lazy load model.
if os.path.exists(f'{model_dir}/weights.pt'):
checkpoint = torch.load(f'{model_dir}/weights.pt')
else:
checkpoint = torch.load(model_dir)
if args.task_id==0:
model = tfdiff_WiFi(AttrDict(params)).to(device)
elif args.task_id==1:
model = tfdiff_fmcw(AttrDict(params)).to(device)
elif args.task_id==2:
model = tfdiff_mimo(AttrDict(params)).to(device)
elif args.task_id==3:
model = tfdiff_eeg(AttrDict(params)).to(device)
model.load_state_dict(checkpoint['model'])
model.eval()
model.params.override(params)
# Initialize diffusion object.
diffusion = SignalDiffusion(
params) if params.signal_diffusion else GaussianDiffusion(params)
# Construct inference dataset.
dataset = from_path_inference(params)
# Sampling process.
with torch.no_grad():
cur_batch = 0
ssim_list = []
snr_list = []
for features in tqdm(dataset, desc=f'Epoch {cur_batch // len(dataset)}'):
features = _nested_map(features, lambda x: x.to(
device) if isinstance(x, torch.Tensor) else x)
data = features['data']
cond = features['cond']
if args.task_id in [0, 1]:
# pred = diffusion.sampling(model, cond, device)
# pred = diffusion.robust_sampling(model, cond, device)
# pred = diffusion.fast_sampling(model, cond, device)
pred = diffusion.native_sampling(model, data, cond, device)
data_samples = [torch.view_as_complex(sample) for sample in torch.split(data, 1, dim=0)] # [B, [1, N, S]]
pred_samples = [torch.view_as_complex(sample) for sample in torch.split(pred, 1, dim=0)] # [B, [1, N, S]]
cond_samples = [torch.view_as_complex(sample) for sample in torch.split(cond, 1, dim=0)] # [B, [1, N, S]]
for b, p_sample in enumerate(pred_samples):
d_sample = data_samples[b]
cur_ssim = eval_ssim(p_sample, d_sample, params.sample_rate, params.input_dim, device=device)
# Save the SSIM.
ssim_list.append(cur_ssim.item())
if args.task_id:
save_fmcw(out_dir, d_sample.cpu().detach(), p_sample.cpu().detach(), cond_samples[b].cpu().detach(), cur_batch,b)
else:
save_wifi(out_dir, d_sample.cpu().detach(), p_sample.cpu().detach(), cond_samples[b].cpu().detach(), cur_batch,b)
cur_batch += 1
if args.task_id in [2, 3]:
# pred = diffusion.sampling(model, cond, device)
# pred = diffusion.robust_sampling(model, cond, device)
pred = diffusion.fast_sampling(model, cond, device)
# pred, _ = diffusion.native_sampling(model, data, cond, device)
if args.task_id == 3:
pred = pred.squeeze(2)
pred = pred.squeeze(2)
data = data.squeeze(2)
data = data.squeeze(2)
pred = pred[:,:,0]
data = data[:,:,0]
snr_list.append(cal_SNR_EEG(pred,data))
save(out_dir, pred.cpu().detach(), cond.cpu().detach(), cur_batch)
else:
snr_list.append(cal_SNR_MIMO(pred,data))
save_mimo(out_dir, data.cpu().detach(), pred.cpu().detach(), cond.cpu().detach(), cur_batch)
cur_batch += 1
if args.task_id in [0,1]:
print_fid(fid_pred_dir,fid_data_dir,args.task_id)
print(f'Average SSIM: {np.mean(ssim_list)}')
if args.task_id in [2, 3]:
print(f'Average SNR: {np.mean(snr_list)}.')
if __name__ == '__main__':
parser = ArgumentParser(
description='runs inference (generation) process based on trained tfdiff model')
parser.add_argument('--task_id', type=int,
help='use case of tfdiff model, 0/1/2/3 for WiFi/FMCW/MIMO/EEG respectively')
parser.add_argument('--model_dir', default=None,
help='directory in which to store model checkpoints')
parser.add_argument('--out_dir', default=None,
help='directories from which to store genrated data file')
parser.add_argument('--cond_dir', default=None,
help='directories from which to read condition files for generation')
parser.add_argument('--device', default='cuda',
help='device for data generation')
main(parser.parse_args())
================================================
FILE: plots/code/Fig10-Scalability-analysis.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import matplotlib.pyplot as plt
import scipy.io as scio
import os;
data_root = '../data'
save_root = '../img'
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
scale = 1.5
overall_rot_file = os.path.join(data_root, 'exp_scalability_analysis.mat')
line = scio.loadmat(overall_rot_file)['line'][0]
gflops_fid_16B_64 = scio.loadmat(overall_rot_file)['gflops_fid_16B_64'][0]
gflops_fid_16B_128 = scio.loadmat(overall_rot_file)['gflops_fid_16B_128'][0]
gflops_fid_16B_256 = scio.loadmat(overall_rot_file)['gflops_fid_16B_256'][0]
gflops_fid_32B_64 = scio.loadmat(overall_rot_file)['gflops_fid_32B_64'][0]
gflops_fid_32B_128 = scio.loadmat(overall_rot_file)['gflops_fid_32B_128'][0]
gflops_fid_32B_256 = scio.loadmat(overall_rot_file)['gflops_fid_32B_256'][0]
gflops_fid_64B_64 = scio.loadmat(overall_rot_file)['gflops_fid_64B_64'][0]
gflops_fid_64B_128 = scio.loadmat(overall_rot_file)['gflops_fid_64B_128'][0]
gflops_fid_64B_256 = scio.loadmat(overall_rot_file)['gflops_fid_64B_256'][0]
# Line
x = np.linspace(0.3, 2.5, 100)
gflops = 10**x
fid = line[0]*x + line[1]
ax.semilogx(gflops, fid, '--', color='tab:gray', marker='None', linewidth=1, alpha=0.7)
# 16B-64
gflops = np.array([gflops_fid_16B_64[0]])
fid = np.array([gflops_fid_16B_64[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:red', marker = '.', markersize=2*scale, linewidth=0.5*scale, label = '16B/64', alpha=0.4)
# 16B-128
gflops = np.array([gflops_fid_16B_128[0]])
fid = np.array([gflops_fid_16B_128[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:red', marker = '.', markersize=4*scale, linewidth=1*scale, label = '16B/128', alpha=0.7)
# 16B-256
gflops = np.array([gflops_fid_16B_256[0]])
fid = np.array([gflops_fid_16B_256[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:red', marker = '.', markersize=8*scale, linewidth=1*scale, label = '16B/256', alpha=1)
# 32B-64
gflops = np.array([gflops_fid_32B_64[0]])
fid = np.array([gflops_fid_32B_64[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:orange', marker = '.', markersize=2.828*scale, linewidth=0.707*scale, label = '32B/64', alpha=0.4)
# 32B-128
gflops = np.array([gflops_fid_32B_128[0]])
fid = np.array([gflops_fid_32B_128[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:orange', marker = '.', markersize=5.756*scale, linewidth=1*scale, label = '32B/128', alpha=0.7)
# 32B-256
gflops = np.array([gflops_fid_32B_256[0]])
fid = np.array([gflops_fid_32B_256[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:orange', marker = '.', markersize=11.512*scale, linewidth=1*scale, label = '32B/256', alpha=1)
# 64B-64
gflops = np.array([gflops_fid_64B_64[0]])
fid = np.array([gflops_fid_64B_64[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:blue', marker = '.', markersize=4*scale, linewidth=1*scale, label = '64B/64', alpha=0.4)
# 64B-128
gflops = np.array([gflops_fid_64B_128[0]])
fid = np.array([gflops_fid_64B_128[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:blue', marker = '.', markersize=8*scale, linewidth=1*scale, label = '64B/128', alpha=0.7)
# 64B-256
gflops = np.array([gflops_fid_64B_256[0]])
fid = np.array([gflops_fid_64B_256[1]])
ax.semilogx(gflops, fid, 'None', color = 'tab:blue', marker = '.', markersize=16*scale, linewidth=1*scale, label = '64B/256', alpha=1)
# Correlation
ax.text(100, 10, 'Correlation \n -0.83', fontsize=9)
# Parameter
ax.text(1.1, 3.8, '#Parameters', fontsize=10)
ax.text(10**0.03, 1.5, '10M', fontsize=9)
ax.text(10**0.3, 1.5, '40M', fontsize=9)
ax.text(10**0.62, 1.5, '160M', fontsize=9)
ax.semilogx(10**0.1, 0, color='tab:gray', marker='.', markersize=4*scale, fillstyle='top', alpha=0.6)
ax.semilogx(10**0.4, 0, color='tab:gray', marker='.', markersize=8*scale, fillstyle='top', alpha=0.6)
ax.semilogx(10**0.8, 0, color='tab:gray', marker='.', markersize=16*scale, fillstyle='top', alpha=0.6)
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(8)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0, 25)
plt.xlim(1e0, 1e3)
plt.xticks([1e0, 1e1, 1e2, 1e3])
plt.xlabel('Model GFLOPs', fontproperties=font, verticalalignment='top')
plt.ylabel('FID', fontproperties=font, verticalalignment='bottom')
leg = plt.legend(loc='best', prop={'size': 7}, ncol=3)
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig10-Scalability-analysis.pdf', dpi=800)
================================================
FILE: plots/code/Fig11(a)-Cross-domain-Performance-of-augmented-Wi-Fi-sensing.py
================================================
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import scipy.io as scio
data_root = '../data'
save_root = '../img'
import matplotlib
import os
n_groups = 2
overall_rot_file = os.path.join(data_root, 'exp_cross_domain.mat')
sigma = scio.loadmat(overall_rot_file)['sigma'][0]
ddpm = scio.loadmat(overall_rot_file)['ddpm'][0]
gan = scio.loadmat(overall_rot_file)['gan'][0]
vae = scio.loadmat(overall_rot_file)['vae'][0]
base = scio.loadmat(overall_rot_file)['base'][0]
font = FontProperties(fname=r"../font/Helvetica.ttf", size=12)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
index = np.arange(n_groups)
bar_width = 0.1
interval=0.2
left_to_right_interval = [-0.3, -0.15, 0, 0.15, 0.3]
opacity = 1
error_config = {'ecolor': '#666666', 'elinewidth': 1.7, 'capsize': 5}
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
gray = '#414141'
rects1 = ax.bar(index + interval + bar_width + left_to_right_interval[0], sigma, bar_width,
color="#FFFFFF",
# edgecolor="#31797d",
edgecolor = blue,
hatch='/' * 4,
lw=2,
label='RF-Diffusion')
rects2 = ax.bar(index + interval + bar_width + left_to_right_interval[1], ddpm, bar_width,
color="#FFFFFF",
# edgecolor="#b21700",
edgecolor = orange,
hatch='x' * 4,
lw=2,
label='DDPM')
rects3 = ax.bar(index + interval + bar_width + left_to_right_interval[2], gan, bar_width,
color="#FFFFFF",
edgecolor = green,
hatch='\\' * 4,
lw=2,
label='DCGAN')
rects4 = ax.bar(index + interval + bar_width + left_to_right_interval[3], vae, bar_width,
color="#FFFFFF",
edgecolor = red,
hatch='|' * 4,
lw=2,
label='CVAE')
rects5 = ax.bar(index + interval + bar_width + left_to_right_interval[4], base, bar_width,
color="#FFFFFF",
edgecolor = gray,
# alpha=0.8,
hatch='-' * 4,
lw=2,
label='Baseline')
# Baseline
x = np.linspace(-0.1, 0.73, 100)
y = base[0]*np.ones(100)
ax.plot(x, y, '--', color='000000', marker='None', zorder=10, linewidth=1,alpha=0.8)
x = np.linspace(0.9, 1.73, 100)
y = base[1]*np.ones(100)
ax.plot(x, y, '--', color='000000', marker='None', zorder=10, linewidth=1,alpha=0.8)
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(10)
ax.set_ylabel('Accuracy', fontproperties=font, verticalalignment='center')
ax.set_xticks(index + interval + bar_width)
ax.set_xticklabels(('WiDar3', 'EI'))
ax.set_ylim(0.65, 1.05)
ax.set_yticks([0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0])
ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=1))
leg = plt.legend(loc='best', prop={'size': 7.5}, ncol=3)
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig11(a)-Cross-domain-Performance-of-augmented-Wi-Fi-sensing.pdf', dpi=800)
================================================
FILE: plots/code/Fig11(b)-In-domain-Performance-of-augmented-Wi-Fi-sensing.py
================================================
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import scipy.io as scio
data_root = '../data'
save_root = '../img'
import matplotlib
import os
n_groups = 2
overall_rot_file = os.path.join(data_root, 'exp_in_domain.mat')
sigma = scio.loadmat(overall_rot_file)['sigma'][0]
ddpm = scio.loadmat(overall_rot_file)['ddpm'][0]
gan = scio.loadmat(overall_rot_file)['gan'][0]
vae = scio.loadmat(overall_rot_file)['vae'][0]
base = scio.loadmat(overall_rot_file)['base'][0]
font = FontProperties(fname=r"../font/Helvetica.ttf", size=12)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
index = np.arange(n_groups)
bar_width = 0.1
interval=0.2
left_to_right_interval = [-0.3, -0.15, 0, 0.15, 0.3]
opacity = 1
error_config = {'ecolor': '#666666', 'elinewidth': 1.7, 'capsize': 5}
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
gray = '#414141'
rects1 = ax.bar(index + interval + bar_width + left_to_right_interval[0], sigma, bar_width,
color="#FFFFFF",
# edgecolor="#31797d",
edgecolor = blue,
hatch='/' * 4,
lw=2,
label='RF-Diffusion')
rects2 = ax.bar(index + interval + bar_width + left_to_right_interval[1], ddpm, bar_width,
color="#FFFFFF",
# edgecolor="#b21700",
edgecolor = orange,
hatch='x' * 4,
lw=2,
label='DDPM')
rects3 = ax.bar(index + interval + bar_width + left_to_right_interval[2], gan, bar_width,
color="#FFFFFF",
edgecolor = green,
hatch='\\' * 4,
lw=2,
label='DCGAN')
rects4 = ax.bar(index + interval + bar_width + left_to_right_interval[3], vae, bar_width,
color="#FFFFFF",
edgecolor = red,
hatch='|' * 4,
lw=2,
label='CVAE')
rects5 = ax.bar(index + interval + bar_width + left_to_right_interval[4], base, bar_width,
color="#FFFFFF",
edgecolor = gray,
# alpha=0.7,
hatch='-' * 4,
lw=2,
label='Baseline')
# Baseline
x = np.linspace(-0.1, 0.73, 100)
y = base[0]*np.ones(100)
ax.plot(x, y, '--', color='000000', marker='None', zorder=10, linewidth=1.5, alpha=0.8)
x = np.linspace(0.9, 1.73, 100)
y = base[1]*np.ones(100)
ax.plot(x, y, '--', color='000000', marker='None', zorder=10, linewidth=1.5, alpha=0.8)
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(10)
ax.set_ylabel('Accuracy', fontproperties=font, verticalalignment='center')
ax.set_xticks(index + interval + bar_width)
ax.set_xticklabels(('WiDar3', 'EI'))
ax.set_ylim(0.65, 1.05)
ax.set_yticks([0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0])
ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=1))
leg = plt.legend(loc='best', prop={'size': 7.5}, ncol=3)
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig11(b)-In-domain-Performance-of-augmented-Wi-Fi-sensing.pdf', dpi=800)
================================================
FILE: plots/code/Fig12-Impact-of-synthetic-data-volume.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.ticker as mtick
import matplotlib
import matplotlib.pyplot as plt
import scipy.io as scio
import os
# matplotlib.rcParams['pdf.fonttype'] = 42
# matplotlib.rcParams['ps.fonttype'] = 42
# Define the input and output directories;
data_root = '../data'
save_root = '../img'
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(8, 2.5))
ax = plt.subplot()
blue = '#084E87'
orange = '#ef8a00'
x = np.array([1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3])
overall_rot_file = os.path.join(data_root, 'exp_impact_synthetic_data.mat')
widar_c = scio.loadmat(overall_rot_file)['widar_c'][0]
widar_i = scio.loadmat(overall_rot_file)['widar_i'][0]
ei_c = scio.loadmat(overall_rot_file)['ei_c'][0]
ei_i = scio.loadmat(overall_rot_file)['ei_i'][0]
# Widar3-Cross-Domain
plt.plot(x, widar_c, '-', color = blue, marker = 'x', markersize=6, linewidth=1.5, label = 'WiDar3-CD', alpha=1)
# Widar3-In-Domain
plt.plot(x, widar_i, '--', color = blue, marker = 'o', markersize=4, linewidth=1.5, label = 'WiDar3-ID', alpha=0.7)
# EI-Cross-Domain
plt.plot(x, ei_c, '-', color = orange, marker = 'x', markersize=6, linewidth=1.5, label = 'EI-CD', alpha=1)
# EI-In-Domain
plt.plot(x, ei_i, '--', color = orange, marker = 'o', markersize=4, linewidth=1.5, label = 'EI-ID', alpha=0.7)
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(10)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0.7, 1.0)
plt.xlim(0.9, 3.1)
plt.xticks([1.0, 1.25, 1.50, 1.75, 2.0, 2.25, 2.50, 2.75, 3.0], ['+0%', '+25%', '+50%', '+75%', '+100%', '+125%', '+150%', '+175%', '+200%'])
plt.xlabel('Augmentated Data', fontproperties=font, verticalalignment='top')
plt.ylabel('Accuracy', fontproperties=font, verticalalignment='bottom')
plt.yticks([0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0])
ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1, decimals=1))
leg = plt.legend(loc='upper center', prop={'size': 10}, ncol=4)
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig12-Impact-of-synthetic-data-volume.pdf', dpi=800)
================================================
FILE: plots/code/Fig13(a)-Performance-of-channel-estimation-amplitude-phase.py
================================================
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as scio
from matplotlib.font_manager import FontProperties
SigMA = scio.loadmat('../data/exp_MIMO.mat')
SigMA_down = np.array(SigMA['data'])
SigMA_down_pre = np.array(SigMA['predict'])
SigMA_up = np.array(SigMA['cond'])
font = FontProperties(fname=r"../font/Helvetica.ttf", size=8)
down = SigMA_down.reshape(26)
pre = SigMA_down_pre.reshape(26)
up = SigMA_up.reshape(26)
complex_array = up
complex_array2 = down
complex_array3 = pre
amplitude = np.abs(complex_array)*3
phase = np.angle(complex_array)
amplitude2 = np.abs(complex_array2)*3
phase2 = np.angle(complex_array2)
amplitude3 = np.abs(complex_array3)*3
phase3 = np.angle(complex_array3)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(4, 3))
amplitude_line1, = ax1.plot(range(1, 27), amplitude, linewidth=1.2, label='Uplink', color='#084E87')
amplitude_line2, = ax1.plot(range(1, 27), amplitude2, linewidth=3,alpha=0.8, label='Downlink', color='#ef8a00')
amplitude_line3, = ax1.plot(range(1, 27), amplitude3, linewidth=1.2, label='Predict', color='#BF3F3F')
ax1.set_ylabel('Amplitude', fontproperties=font)
ax1.grid(linestyle='--', linewidth=0.5, zorder=0)
ax1.set_xlim(0, 27)
for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(7)
phase_line1, = ax2.plot(range(1, 27), phase, linewidth=1.2, label='Uplink', color='#084E87')
phase_line2, = ax2.plot(range(1, 27), phase2, linewidth=3,alpha=0.8, label='Downlink', color='#ef8a00')
phase_line3, = ax2.plot(range(1, 27), phase3, linewidth=1.2, label='Predict', color='#BF3F3F')
ax2.set_xlabel('Subcarriers', fontproperties=font)
ax2.set_ylabel('Phase (rad)', fontproperties=font)
ax2.grid(linestyle='--', linewidth=0.5, zorder=0)
for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(7)
ax2.set_xlim(0, 27)
ax2.set_ylim(-np.pi, np.pi)
ax2.legend(handles=[phase_line1, phase_line2, phase_line3], loc='lower left', prop={'size': 6}, ncol=3, edgecolor='black')
ax2.get_legend().get_frame().set_linewidth(0.5)
plt.tight_layout()
fig.savefig('../img/Fig13(a)-channel-amplitude-phase.pdf')
================================================
FILE: plots/code/Fig13(b)-Performance-of-channel-estimation-SNR.py
================================================
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import numpy as np
from matplotlib.font_manager import FontProperties
import scipy.io as scio
import os
def read_data_from_txt(filename):
data = []
with open(filename, "r") as f:
for line in f.readlines():
line = float(line.strip('\n'))
data.append(line)
return data
sample_size=100
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
overall_rot_file = '../data/exp_mimo_snr.mat'
data_ours = scio.loadmat(overall_rot_file)['RF-Diffusion'][0]
data_nerf2 = scio.loadmat(overall_rot_file)['data_nerf2'][0]
data_fire = scio.loadmat(overall_rot_file)['data_fire'][0]
data_codebook = scio.loadmat(overall_rot_file)['data_codebook'][0]
# color_ours = '#AC536D'
color_ours = '#000000'
fig, ax1 = plt.subplots(figsize=(4, 3))
box_plot = ax1.boxplot(data_ours,
# whis=(0, 100),
patch_artist=True,
widths=0.25,
medianprops={"color": color_ours, "linewidth": 1},
boxprops={"facecolor": "C0", "edgecolor": color_ours, "linewidth": 1},
whiskerprops={"color": color_ours, "linewidth": 1},
flierprops={"color": color_ours, "marker": "d", "markersize": 2}, # "d" for diamond marker
capprops={"color": color_ours, "linewidth": 1},
positions=[1])
box_plot2 = ax1.boxplot(data_nerf2,
# whis=(0, 100),
patch_artist=True,
widths=0.25,
medianprops={"color": color_ours, "linewidth": 1},
boxprops={"facecolor": "C0", "edgecolor": color_ours, "linewidth": 1},
whiskerprops={"color": color_ours, "linewidth": 1},
capprops={"color": color_ours, "linewidth": 1},
flierprops={"color": color_ours, "marker": "d", "markersize": 2}, # "d" for diamond marker
positions=[2])
box_plot3 = ax1.boxplot(data_fire,
# whis=(0, 100),
patch_artist=True,
widths=0.25,
medianprops={"color": color_ours, "linewidth": 1},
boxprops={"facecolor": "C0", "edgecolor": color_ours, "linewidth": 1},
whiskerprops={"color": color_ours, "linewidth": 1},
capprops={"color": color_ours, "linewidth": 1},
flierprops={"color": color_ours, "marker": "d", "markersize": 2}, # "d" for diamond marker
positions=[3])
box_plot4 = ax1.boxplot(data_codebook,
# whis=(0, 100),
patch_artist=True,
widths=0.25,
medianprops={"color": color_ours, "linewidth": 1},
boxprops={"facecolor": "C0", "edgecolor": color_ours, "linewidth": 1},
whiskerprops={"color": color_ours, "linewidth": 1},
capprops={"color": color_ours, "linewidth": 1},
flierprops={"color": color_ours, "marker": "d", "markersize": 2}, # "d" for diamond marker
positions=[4])
# ax0.legend([boxes_ours["boxes"][0], boxes_nsdi["boxes"][0], boxes_cloud["boxes"][0]],['Netopia', 'Baseline-I', 'Baseline-II'], fontsize=14)
ax1.grid(linestyle='--', linewidth=0.5, zorder=0)
for patch in box_plot['boxes']:
patch.set_facecolor("#084E87CC")
for patch in box_plot2['boxes']:
patch.set_facecolor("#ef8a00CC")
for patch in box_plot3['boxes']:
patch.set_facecolor("#267226CC")
for patch in box_plot4['boxes']:
patch.set_facecolor("#BF3F3FCC")
ax1.set_yticks([0, 5, 10, 15, 20, 25, 30, 35])
ax1.set_xticklabels(['RF-Diffusion', 'NeRF$^2$', 'FIRE', 'Codebook'])
fig.supylabel('SNR(dB)', fontproperties=font, verticalalignment='center')
for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(10)
# box_plot['boxes'][0].set_label('Legend1')
# box_plot2['boxes'][0].set_label('Legend2')
# ax1.legend()
plt.tight_layout()
fig.savefig('../img/Fig13(b)-channel-snr.pdf')
================================================
FILE: plots/code/Fig6(a)-exp-overall-wifi-ssim.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import scipy.io as scio
import os;
data_root = '../data'
save_root = '../img'
import matplotlib
overall_rot_file = os.path.join(data_root, 'exp_overall_ssim_wifi.mat')
sigma = scio.loadmat(overall_rot_file)['data_wifi_sigma']
ddpm = scio.loadmat(overall_rot_file)['data_wifi_ddpm']
gan = scio.loadmat(overall_rot_file)['data_wifi_gan']
vae = scio.loadmat(overall_rot_file)['data_wifi_vae']
sigma_std = np.std(sigma)
sigma_mean = np.mean(sigma)
ddpm_mean = np.mean(ddpm)
gan_mean = np.mean(gan)
vae_mean = np.mean(vae)
w_perc = np.percentile(sigma, 90)
n_bins = np.arange(0, 1, 0.0001) # 0到30按0.01划分区间
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
# Data
counts_1, _ = np.histogram(sigma, bins=n_bins, density=True) # density=True返回每个区间的百分比
cdf_1 = np.cumsum(counts_1)
cdf_1 = cdf_1.astype(float) / cdf_1[-1]
counts_2, _ = np.histogram(ddpm, bins=n_bins, density=True)
cdf_2 = np.cumsum(counts_2)
cdf_2 = cdf_2.astype(float) / cdf_2[-1]
counts_3, _ = np.histogram(gan, bins=n_bins, density=True)
cdf_3 = np.cumsum(counts_3)
cdf_3 = cdf_3.astype(float) / cdf_3[-1]
counts_4, _ = np.histogram(vae, bins=n_bins, density=True)
cdf_4 = np.cumsum(counts_4)
cdf_4 = cdf_4.astype(float) / cdf_4[-1]
# seagreen darkorange indianred steelblue
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
plt.plot(n_bins[0:-1], cdf_1, '-', zorder=4,color=blue,linewidth=2, label='RF-Diffusion')
plt.plot(n_bins[0:-1], cdf_2, '--', zorder=3, color=orange,linewidth=2,label='DDPM')
plt.plot(n_bins[0:-1], cdf_3, '-.', zorder=2, color=green,linewidth=2,label='DCGAN')
plt.plot(n_bins[0:-1], cdf_4, ':', zorder=1, color=red,linewidth=2,label='CVAE')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(11)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0, 1)
plt.xlim(0, 1.0)
plt.xlabel('SSIM', fontproperties=font, verticalalignment='top')
plt.ylabel('CDF', fontproperties=font, verticalalignment='bottom')
leg = plt.legend(loc='best', prop={'size': 9})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig6(a)-exp-overall-wifi-ssim.pdf', dpi=800)
================================================
FILE: plots/code/Fig6(b)-exp-overall-wifi-fid.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import scipy.io as scio
import os;
data_root = '../data'
save_root = '../img'
import matplotlib
overall_rot_file = os.path.join(data_root, 'exp_overall_fid_wifi.mat')
sigma = scio.loadmat(overall_rot_file)['data_wifi_sigma']
ddpm = scio.loadmat(overall_rot_file)['data_wifi_ddpm']
gan = scio.loadmat(overall_rot_file)['data_wifi_gan']
vae = scio.loadmat(overall_rot_file)['data_wifi_vae']
sigma_std = np.std(sigma)
sigma_mean = np.mean(sigma)
ddpm_mean = np.mean(ddpm)
gan_mean = np.mean(gan)
vae_mean = np.mean(vae)
w_perc = np.percentile(sigma, 90)
n_bins = np.arange(0, 20, 0.001) # 0到30按0.01划分区间
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
# Data
counts_1, _ = np.histogram(sigma, bins=n_bins, density=True) # density=True返回每个区间的百分比
cdf_1 = np.cumsum(counts_1)
cdf_1 = cdf_1.astype(float) / cdf_1[-1]
counts_2, _ = np.histogram(ddpm, bins=n_bins, density=True)
cdf_2 = np.cumsum(counts_2)
cdf_2 = cdf_2.astype(float) / cdf_2[-1]
counts_3, _ = np.histogram(gan, bins=n_bins, density=True)
cdf_3 = np.cumsum(counts_3)
cdf_3 = cdf_3.astype(float) / cdf_3[-1]
counts_4, _ = np.histogram(vae, bins=n_bins, density=True)
cdf_4 = np.cumsum(counts_4)
cdf_4 = cdf_4.astype(float) / cdf_4[-1]
# seagreen darkorange indianred steelblue
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
plt.plot(n_bins[0:-1], cdf_1, '-', zorder=4,color=blue,linewidth=2, label='RF-Diffusion')
plt.plot(n_bins[0:-1], cdf_2, '--', zorder=3, color=orange,linewidth=2,label='DDPM')
plt.plot(n_bins[0:-1], cdf_3, '-.', zorder=2, color=green,linewidth=2,label='DCGAN')
plt.plot(n_bins[0:-1], cdf_4, ':', zorder=1, color=red,linewidth=2,label='CVAE')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(11)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0, 1)
plt.xlim(0, 20.0)
plt.xlabel('FID', fontproperties=font, verticalalignment='top')
plt.ylabel('CDF', fontproperties=font, verticalalignment='bottom')
leg = plt.legend(loc='best', prop={'size': 9})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig6(b)-exp-overall-wifi-fid.pdf', dpi=800)
================================================
FILE: plots/code/Fig7(a)-exp-overall-fmcw-ssim.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import scipy.io as scio
import os;
data_root = '../data'
save_root = '../img'
import matplotlib
overall_rot_file = os.path.join(data_root, 'exp_overall_ssim_fmcw.mat')
sigma = scio.loadmat(overall_rot_file)['data_fmcw_sigma']
ddpm = scio.loadmat(overall_rot_file)['data_fmcw_ddpm']
gan = scio.loadmat(overall_rot_file)['data_fmcw_gan']
vae = scio.loadmat(overall_rot_file)['data_fmcw_vae']
sigma_std = np.std(sigma)
sigma_mean = np.mean(sigma)
ddpm_mean = np.mean(ddpm)
gan_mean = np.mean(gan)
vae_mean = np.mean(vae)
w_perc = np.percentile(sigma, 90)
n_bins = np.arange(0, 1, 0.0001) # 0到30按0.01划分区间
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
# Data
counts_1, _ = np.histogram(sigma, bins=n_bins, density=True) # density=True返回每个区间的百分比
cdf_1 = np.cumsum(counts_1)
cdf_1 = cdf_1.astype(float) / cdf_1[-1]
counts_2, _ = np.histogram(ddpm, bins=n_bins, density=True)
cdf_2 = np.cumsum(counts_2)
cdf_2 = cdf_2.astype(float) / cdf_2[-1]
counts_3, _ = np.histogram(gan, bins=n_bins, density=True)
cdf_3 = np.cumsum(counts_3)
cdf_3 = cdf_3.astype(float) / cdf_3[-1]
counts_4, _ = np.histogram(vae, bins=n_bins, density=True)
cdf_4 = np.cumsum(counts_4)
cdf_4 = cdf_4.astype(float) / cdf_4[-1]
# seagreen darkorange indianred steelblue
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
plt.plot(n_bins[0:-1], cdf_1, '-', zorder=4,color=blue,linewidth=2, label='RF-Diffusion')
plt.plot(n_bins[0:-1], cdf_2, '--', zorder=3, color=orange,linewidth=2,label='DDPM')
plt.plot(n_bins[0:-1], cdf_3, '-.', zorder=2, color=green,linewidth=2,label='DCGAN')
plt.plot(n_bins[0:-1], cdf_4, ':', zorder=1, color=red,linewidth=2,label='CVAE')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(11)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0, 1)
plt.xlim(0, 1.0)
plt.xlabel('SSIM', fontproperties=font, verticalalignment='top')
plt.ylabel('CDF', fontproperties=font, verticalalignment='bottom')
leg = plt.legend(loc='best', prop={'size': 9})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig7(a)-exp-overall-fmcw-ssim.pdf', dpi=800)
================================================
FILE: plots/code/Fig7(b)-exp-overall-fmcw-fid.py
================================================
import numpy as np
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import scipy.io as scio
import os;
# matplotlib.rcParams['pdf.fonttype'] = 42
# matplotlib.rcParams['ps.fonttype'] = 42
# Define the input and output directories;
data_root = '../data'
save_root = '../img'
import matplotlib
# matplotlib.rcParams['pdf.fonttype'] = 42
# matplotlib.rcParams['ps.fonttype'] = 42
overall_rot_file = os.path.join(data_root, 'exp_overall_fid_fmcw.mat')
sigma = scio.loadmat(overall_rot_file)['data_fmcw_sigma']
ddpm = scio.loadmat(overall_rot_file)['data_fmcw_ddpm']
gan = scio.loadmat(overall_rot_file)['data_fmcw_gan']
vae = scio.loadmat(overall_rot_file)['data_fmcw_vae']
sigma_std = np.std(sigma)
sigma_mean = np.mean(sigma)
ddpm_mean = np.mean(ddpm)
gan_mean = np.mean(gan)
vae_mean = np.mean(vae)
w_perc = np.percentile(sigma, 90)
n_bins = np.arange(0, 20, 0.001) # 0到30按0.01划分区间
font = FontProperties(fname=r"../font/Helvetica.ttf", size=11)
plt.figure(figsize=(4, 2.5))
ax = plt.subplot()
# Data
counts_1, _ = np.histogram(sigma, bins=n_bins, density=True) # density=True返回每个区间的百分比
cdf_1 = np.cumsum(counts_1)
cdf_1 = cdf_1.astype(float) / cdf_1[-1]
counts_2, _ = np.histogram(ddpm, bins=n_bins, density=True)
cdf_2 = np.cumsum(counts_2)
cdf_2 = cdf_2.astype(float) / cdf_2[-1]
counts_3, _ = np.histogram(gan, bins=n_bins, density=True)
cdf_3 = np.cumsum(counts_3)
cdf_3 = cdf_3.astype(float) / cdf_3[-1]
counts_4, _ = np.histogram(vae, bins=n_bins, density=True)
cdf_4 = np.cumsum(counts_4)
cdf_4 = cdf_4.astype(float) / cdf_4[-1]
# seagreen darkorange indianred steelblue
blue = '#084E87'
orange = '#ef8a00'
green = '#267226'
red = '#BF3F3F'
plt.plot(n_bins[0:-1], cdf_1, '-', zorder=4,color=blue,linewidth=2, label='RF-Diffusion')
plt.plot(n_bins[0:-1], cdf_2, '--', zorder=3, color=orange,linewidth=2,label='DDPM')
plt.plot(n_bins[0:-1], cdf_3, '-.', zorder=2, color=green,linewidth=2,label='DCGAN')
plt.plot(n_bins[0:-1], cdf_4, ':', zorder=1, color=red,linewidth=2,label='CVAE')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(11)
plt.grid(linestyle='--', linewidth=0.5, zorder=0)
plt.ylim(0, 1)
plt.xlim(0, 25.0)
plt.xlabel('FID', fontproperties=font, verticalalignment='top')
plt.ylabel('CDF', fontproperties=font, verticalalignment='bottom')
leg = plt.legend(loc='lower right', prop={'size': 9})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
plt.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig7(b)-exp-overall-fmcw-fid.pdf', dpi=800)
================================================
FILE: plots/code/Fig8-Impact-of-diffusion-method.py
================================================
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import scipy.io as scio
data_root = '../data'
save_root = '../img'
import matplotlib
import os
n_groups = 3
overall_rot_file = os.path.join(data_root, 'exp_impact_diffusion_method.mat')
ssim_mean = scio.loadmat(overall_rot_file)['ssim_mean'][0]
ssim_std = scio.loadmat(overall_rot_file)['ssim_std'][0]
fid_mean = scio.loadmat(overall_rot_file)['fid_mean'][0]
fid_std = scio.loadmat(overall_rot_file)['fid_std'][0]
font = FontProperties(fname=r"../font/Helvetica.ttf", size=12)
fig, ax = plt.subplots(figsize=(4, 2.5))
ax2 = ax.twinx()
index = np.arange(n_groups)
bar_width = 0.2
interval = 0.1
left_interval = -0.15
right_interval = 0.15
opacity = 1
error_config = {'ecolor': '#666666', 'elinewidth': 1.7, 'capsize': 5}
blue = '#084E87'
orange = '#ef8a00'
rects1 = ax.bar(index + interval + bar_width + left_interval, ssim_mean, bar_width,
color="#FFFFFF",
# edgecolor="#31797d",
edgecolor = blue,
yerr=ssim_std, error_kw=error_config,
hatch='/' * 4,
lw=2,
label='SSIM')
rects2 = ax2.bar(index + interval + bar_width + right_interval, fid_mean, bar_width,
color="#FFFFFF",
# edgecolor="#b21700",
edgecolor = orange,
yerr=fid_std, error_kw=error_config,
hatch='x' * 4,
lw=2,
label='FID')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(8)
ax.set_ylabel('SSIM', fontproperties=font, verticalalignment='center')
ax2.set_ylabel('FID', fontproperties=font, verticalalignment='center')
ax.set_xticks(index + bar_width + interval)
ax.set_xticklabels(('Time-Frequency', 'Gaussian', 'Blur'))
ax.set_ylim(0, 1.2)
ax2.set_ylim(0, 18)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
ax2.set_yticks([0, 3, 6, 9, 12, 15])
leg = fig.legend(loc='upper left', bbox_to_anchor=(0.145, 0.955), prop={'size': 8})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
fig.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig8-Impact-of-diffusion-method.pdf', dpi=800)
================================================
FILE: plots/code/Fig9-Impact-of-network-design.py
================================================
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib.ticker import MaxNLocator
from collections import namedtuple
from matplotlib.font_manager import FontProperties
from matplotlib.backends.backend_pdf import PdfPages
import scipy.io as scio
data_root = '../data'
save_root = '../img'
import matplotlib
import os
n_groups = 3
overall_rot_file = os.path.join(data_root, 'exp_impact_network_design.mat')
ssim_mean = scio.loadmat(overall_rot_file)['ssim_mean'][0]
ssim_std = scio.loadmat(overall_rot_file)['ssim_std'][0]
fid_mean = scio.loadmat(overall_rot_file)['fid_mean'][0]
fid_std = scio.loadmat(overall_rot_file)['fid_std'][0]
font = FontProperties(fname=r"../font/Helvetica.ttf", size=12)
fig, ax = plt.subplots(figsize=(4, 2.5))
ax2 = ax.twinx()
index = np.arange(n_groups)
bar_width = 0.2
interval = 0.1
left_interval = - 0.15
right_interval = 0.15
opacity = 1
error_config = {'ecolor': '#666666', 'elinewidth': 1.7, 'capsize': 5}
blue = '#084E87'
orange = '#ef8a00'
rects1 = ax.bar(index + interval + bar_width + left_interval, ssim_mean, bar_width,
color="#FFFFFF",
# edgecolor="#31797d",
edgecolor = blue,
yerr=ssim_std, error_kw=error_config,
hatch='/' * 4,
lw=2,
label='SSIM')
rects2 = ax2.bar(index + interval + bar_width + right_interval, fid_mean, bar_width,
color="#FFFFFF",
# edgecolor="#b21700",
edgecolor = orange,
yerr=fid_std, error_kw=error_config,
hatch='x' * 4,
lw=2,
label='FID')
# Set ticks grids and labels
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontproperties(font)
label.set_fontsize(8)
ax.set_ylabel('SSIM', fontproperties=font, verticalalignment='center')
ax2.set_ylabel('FID', fontproperties=font, verticalalignment='center')
ax.set_xticks(index + bar_width + interval)
ax.set_xticklabels(('HDT', 'DT', 'U-Net'))
ax.set_ylim(0, 1.2)
ax2.set_ylim(0, 12)
ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
ax2.set_yticks([0, 2, 4, 6, 8, 10])
leg = fig.legend(loc='upper left', bbox_to_anchor=(0.145, 0.955), prop={'size': 8})
leg.get_frame().set_edgecolor('#000000')
leg.get_frame().set_linewidth(0.5)
fig.tight_layout()
# plt.show()
plt.savefig(save_root + '/Fig9-Impact-of-network-design.pdf', dpi=800)
================================================
FILE: plots/data/untitled.asv
================================================
ssim_mean = [0.8064, 0.7135, 0.4391];
ssim_std = [0.1128, 0.1547, 0.1958];
fsd_mean = [4.417, 7.534, 14.72];
fsd_std = [1.051, 1.135, 2.944];
save('exp_impact_diffusion_method','ssim_mean',ssim_mean,)
================================================
FILE: plots/data/untitled.m
================================================
mimo = 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save('exp_scalability_analysis.mat','line','gflops_fid_16B_64','gflops_fid_16B_128','gflops_fid_16B_256','gflops_fid_32B_64','gflops_fid_32B_128','gflops_fid_32B_256','gflops_fid_64B_64','gflops_fid_64B_128','gflops_fid_64B_256');
================================================
FILE: plots/requirements.txt
================================================
matplotlib
scipy
numpy
================================================
FILE: tfdiff/__init__.py
================================================
================================================
FILE: tfdiff/dataset.py
================================================
import numpy as np
import os
import random
import torch
import torch.nn.functional as F
import scipy.io as scio
from tfdiff.params import AttrDict
from glob import glob
from torch.utils.data.distributed import DistributedSampler
# data_key='csi_data',
# gesture_key='gesture',
# location_key='location',
# orient_key='orient',
# room_key='room',
# rx_key='rx',
# user_key='user',
def _nested_map(struct, map_fn):
if isinstance(struct, tuple):
return tuple(_nested_map(x, map_fn) for x in struct)
if isinstance(struct, list):
return [_nested_map(x, map_fn) for x in struct]
if isinstance(struct, dict):
return {k: _nested_map(v, map_fn) for k, v in struct.items()}
return map_fn(struct)
class WiFiDataset(torch.utils.data.Dataset):
def __init__(self, paths):
super().__init__()
self.filenames = []
for path in paths:
self.filenames += glob(f'{path}/**/user*.mat', recursive=True)
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
cur_filename = self.filenames[idx]
cur_sample = scio.loadmat(cur_filename,verify_compressed_data_integrity=False)
# cur_data = torch.from_numpy(cur_sample['csi_data']).to(torch.complex64)
cur_data = torch.from_numpy(cur_sample['feature']).to(torch.complex64)
cur_cond = torch.from_numpy(cur_sample['cond']).to(torch.complex64)
return {
'data': cur_data,
'cond': cur_cond.squeeze(0)
}
class FMCWDataset(torch.utils.data.Dataset):
def __init__(self, paths):
super().__init__()
self.filenames = []
for path in paths:
self.filenames += glob(f'{path}/**/*.mat', recursive=True)
def __len__(self):
return len(self.filenames)
def __getitem__(self, idx):
cur_filename = self.filenames[idx]
cur_sample = scio.loadmat(cur_filename)
cur_data = torch.from_numpy(cur_sample['feature']).to(torch.complex64)
cur_cond = torch.from_numpy(cur_sample['cond'].astype(np.int16)).to(torch.complex64)
return {
'data': cur_data,
'cond': cur_cond.squeeze(0)
}
class MIMODataset(torch.utils.data.Dataset):
def __init__(self, paths):
super().__init__()
self.filenames = []
for path in paths:
self.filenames += glob(f'{path}/**/*.mat', recursive=True)
def __len__(self):
return len(self.filenames)
def __getitem__(self,idx):
dataset = scio.loadmat(self.filenames[idx])
data = torch.from_numpy(dataset['down_link']).to(torch.complex64)
cond = torch.from_numpy(dataset['up_link']).to(torch.complex64)
return {
'data': torch.view_as_real(data),
'cond': torch.view_as_real(cond)
}
class EEGDataset(torch.utils.data.Dataset):
def __init__(self, paths):
super().__init__()
paths = paths[0]
self.filenames = []
self.filenames += glob(f'{paths}/*.mat', recursive=True)
def __len__(self):
return len(self.filenames)
def __getitem__(self,idx):
dataset = scio.loadmat(self.filenames[idx])
data = torch.from_numpy(dataset['clean']).to(torch.complex64)
cond = torch.from_numpy(dataset['disturb']).to(torch.complex64)
return {
'data': data,
'cond': cond
}
class Collator:
def __init__(self, params):
self.params = params
def collate(self, minibatch):
sample_rate = self.params.sample_rate
task_id = self.params.task_id
## WiFi Case
if task_id == 0:
for record in minibatch:
# Filter out records that aren't long enough.
if len(record['data']) < sample_rate:
del record['data']
del record['cond']
continue
data = torch.view_as_real(record['data']).permute(1, 2, 0)
down_sample = F.interpolate(data, sample_rate, mode='nearest-exact')
norm_data = (down_sample - down_sample.mean()) / down_sample.std()
record['data'] = norm_data.permute(2, 0, 1)
data = torch.stack([record['data'] for record in minibatch if 'data' in record])
cond = torch.stack([record['cond'] for record in minibatch if 'cond' in record])
return {
'data': data,
'cond': torch.view_as_real(cond),
}
## FMCW Case
elif task_id == 1:
for record in minibatch:
# Filter out records that aren't long enough.
if len(record['data']) < sample_rate:
del record['data']
del record['cond']
continue
data = torch.view_as_real(record['data']).permute(1, 2, 0)
down_sample = F.interpolate(data, sample_rate, mode='nearest-exact')
norm_data = (down_sample - down_sample.mean()) / down_sample.std()
record['data'] = norm_data.permute(2, 0, 1)
data = torch.stack([record['data'] for record in minibatch if 'data' in record])
cond = torch.stack([record['cond'] for record in minibatch if 'cond' in record])
return {
'data': data,
'cond': torch.view_as_real(cond),
}
## MIMO Case
elif task_id == 2:
for record in minibatch:
data = record['data']
cond = record['cond']
# print(f'data.shape:{data.shape}')
norm_data = (data) / cond.std()
norm_cond = (cond) / cond.std()
record['data'] = norm_data.reshape(14, 96, 26, 2).transpose(1,2)
record['cond'] = norm_cond.reshape(14, 96, 26, 2).transpose(1,2)
data = torch.stack([record['data'] for record in minibatch if 'data' in record])
cond = torch.stack([record['cond'] for record in minibatch if 'cond' in record])
return {
'data': data,
'cond': cond,
}
## EEG Case
if task_id == 3:
for record in minibatch:
data = record['data']
cond = record['cond']
norm_data = data / cond.std()
norm_cond = cond / cond.std()
record['data'] = norm_data.reshape(512, 1, 1)
record['cond'] = norm_cond.reshape(512)
data = torch.stack([record['data'] for record in minibatch if 'data' in record])
cond = torch.stack([record['cond'] for record in minibatch if 'cond' in record])
return {
'data': torch.view_as_real(data),
'cond': torch.view_as_real(cond),
}
else:
raise ValueError("Unexpected task_id.")
def from_path(params, is_distributed=False):
data_dir = params.data_dir
task_id = params.task_id
if task_id == 0:
dataset = WiFiDataset(data_dir)
elif task_id == 1:
dataset = FMCWDataset(data_dir)
elif task_id == 2:
dataset = MIMODataset(data_dir)
elif task_id == 3:
dataset = EEGDataset(data_dir)
else:
raise ValueError("Unexpected task_id.")
return torch.utils.data.DataLoader(
dataset,
batch_size=params.batch_size,
collate_fn=Collator(params).collate,
shuffle=not is_distributed,
num_workers=8,
sampler=DistributedSampler(dataset) if is_distributed else None,
pin_memory=True,
drop_last=True,
persistent_workers=True)
def from_path_inference(params):
cond_dir = params.cond_dir
task_id = params.task_id
if task_id == 0:
dataset = WiFiDataset(cond_dir)
elif task_id == 1:
dataset = FMCWDataset(cond_dir)
elif task_id == 2:
dataset = MIMODataset(cond_dir)
elif task_id == 3:
dataset = EEGDataset(cond_dir)
else:
raise ValueError("Unexpected task_id.")
return torch.utils.data.DataLoader(
dataset,
batch_size=params.inference_batch_size,
collate_fn=Collator(params).collate,
shuffle=False,
num_workers=os.cpu_count()
)
================================================
FILE: tfdiff/diffusion.py
================================================
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
class SignalDiffusion(nn.Module):
def __init__(self, params):
super().__init__()
self.params = params
self.task_id = params.task_id
self.input_dim = self.params.sample_rate # input time-series data length, N
self.extra_dim = self.params.extra_dim # dimension of each data sample, e.g., [S A 2] for complex-valued CSI
self.max_step = self.params.max_step # maximum diffusion steps
beta = np.array(self.params.noise_schedule) # \beta, [T]
self.alpha = torch.tensor((1-beta).astype(np.float32)) # \alpha_t [T]
self.alpha_bar = torch.cumprod(self.alpha, dim=0) # \bar{\alpha_t}, [T]
self.var_blur = torch.tensor(np.array(self.params.blur_schedule).astype(np.float32)) # var of blur kernels on the frequency domain for each diffusion step
self.var_blur_bar = torch.cumsum(self.var_blur, dim=0) # var of blur kernels on the frequency domain, [T]
self.var_kernel = (self.input_dim / self.var_blur).unsqueeze(1) # var of each G_t, [T, 1]
self.var_kernel_bar = (self.input_dim / self.var_blur_bar).unsqueeze(1) # var of each \bar{G_t}, [T, 1]
self.gaussian_kernel = self.get_kernel(self.var_kernel) # G_t, [T, N]
self.gaussian_kernel_bar = self.get_kernel(self.var_kernel_bar) # \bar{G_t}, [T, N]
# The weight of original information x_0 in degraded data x_t
self.info_weights = self.gaussian_kernel_bar * torch.sqrt(self.alpha_bar).unsqueeze(-1) # [T, N]
# The overall weight of gaussian noise \epsilon in degraded data x_t
self.noise_weights = self.get_noise_weights() # [T, N]
def get_kernel(self, var_kernel):
samples = torch.arange(0, self.input_dim) # [N]
gaussian_kernel = torch.exp(-((samples - self.input_dim // 2)**2) / (2 * var_kernel)) / torch.sqrt(2 * torch.pi * var_kernel) # G_t, [T, N]
gaussian_kernel = self.input_dim * gaussian_kernel / torch.sum(gaussian_kernel, dim=1, keepdim=True) # Normalized G_t, [T, N]
return gaussian_kernel
def get_noise_weights(self):
noise_weights = []
for t in range(self.max_step):
upper_bound = t + 1
one_minus_alpha_sqrt = torch.sqrt(1 - self.alpha[0:upper_bound]) # \sqrt(1-\bar{\alpha_s}), for s in [1, t], [t]
rev_one_minus_alpha_sqrt = torch.flipud(one_minus_alpha_sqrt) # \sqrt(1-\bar{\alpha_s}), for s in [t, 1], [t]
rev_alpha = torch.flipud(self.alpha[0:upper_bound]) # alpha_s, for s in [t, 1], [t]
rev_alpha_bar_sqrt = torch.sqrt(torch.cumprod(rev_alpha, dim=0) / rev_alpha[-1]) # \sqrt{\bar{\alpha_t} / \bar{\alpha_s}}, for s in [t, 1], [t]
rev_var_blur = torch.flipud(self.var_blur[:upper_bound]) # [t]
rev_var_blur_bar = torch.cumsum(rev_var_blur, dim=0) - rev_var_blur[-1] # [t]
rev_var_kernel_bar = (self.input_dim / rev_var_blur_bar).unsqueeze(1) # [t, 1]
rev_kernel_bar = self.get_kernel(rev_var_kernel_bar) # \bar{G_t} / \bar{G_s}, for s in [t, 1], [t, N]
rev_kernel_bar[0, :] = torch.ones(self.input_dim)
noise_weights.append(torch.mv((rev_alpha_bar_sqrt.unsqueeze(-1) * rev_kernel_bar).transpose(0, 1), rev_one_minus_alpha_sqrt)) # [t, N]
return torch.stack(noise_weights, dim=0) # [T, N]
def get_noise_weights_stats(self):
noise_weights = []
one_minus_alpha_sqrt = torch.sqrt(1 - self.alpha[0])
for t in range(self.max_step):
noise_weights.append((1 - torch.sqrt(self.alpha_bar[t])*self.gaussian_kernel_bar[t, :]) / (1 - torch.sqrt(self.alpha[0]) * self.gaussian_kernel[0, :]))
return one_minus_alpha_sqrt * torch.stack(noise_weights, dim=0) # [T, N]
## Depracated: numerical instable when params.blur_schedule is high, kernel may divided by 0.
def get_noise_weights_div(self):
noise_weights = []
for t in range(self.max_step):
upper_bound = t + 1
one_minus_alpha_sqrt = torch.sqrt(1 - self.alpha[:upper_bound]) # \sqrt(1-\bar{\alpha_s}), for s in [1, t], [t]
ratio_alpha_bar_sqrt = torch.sqrt(self.alpha_bar[t] / self.alpha_bar[:upper_bound]) # \sqrt(\bar{\alpha_t} / \bar{\alpha_s}), for s in [1, t], [t]
ratio_kernel_bar = self.gaussian_kernel_bar[t, :] / self.gaussian_kernel_bar[:upper_bound, :] # \bar{G_t} / \bar{G_s}, for s in [1, t], [t, N]
noise_weights.append(torch.mv((ratio_alpha_bar_sqrt.unsqueeze(-1) * ratio_kernel_bar).transpose(0, 1), one_minus_alpha_sqrt)) # [t, N]
return torch.stack(noise_weights, dim=0) # [T, N]
## Depracated: numerical instable when params.blur_schedule is high, amplitude of kernel may overflow.
def get_noise_weights_prod(self):
noise_weights = []
for t in range(self.max_step):
upper_bound = t + 1
one_minus_alpha_sqrt = torch.sqrt(1 - self.alpha[0:upper_bound]) # \sqrt(1-\bar{\alpha_s}), for s in [1, t], [t]
rev_one_minus_alpha_sqrt = torch.flipud(one_minus_alpha_sqrt) # \sqrt(1-\bar{\alpha_s}), for s in [t, 1], [t]
rev_alpha = torch.flipud(self.alpha[0:upper_bound]) # alpha_s, for s in [t, 1], [t]
rev_alpha_bar_sqrt = torch.sqrt(torch.cumprod(rev_alpha, dim=0) / rev_alpha[-1]) # \sqrt{\bar{\alpha_t} / \bar{\alpha_s}}, for s in [t, 1], [t]
rev_kernel = torch.flipud(self.gaussian_kernel[:upper_bound, :]) # G_s, for s in [t, 1], [t, N]
rev_kernel_bar = torch.cumprod(rev_kernel, dim=0) / rev_kernel[-1, :] # \bar{G_t} / \bar{G_s}, for s in [t, 1], [t, N]
noise_weights.append(torch.mv((rev_alpha_bar_sqrt.unsqueeze(-1) * rev_kernel_bar).transpose(0, 1), rev_one_minus_alpha_sqrt)) # [t, N]
return torch.stack(noise_weights, dim=0) # [T, N]
def degrade_fn(self, x_0, t, task_id):
device = x_0.device
if task_id in [0, 1]:
noise_weight = self.noise_weights[t, :].unsqueeze(-1).unsqueeze(-1).to(device) # equivalent gaussian noise weights, [B, N, 1, 1, 1]
info_weight = self.info_weights[t, :].unsqueeze(-1).unsqueeze(-1).to(device) # equivalent original info weights, [B, N, 1, 1, 1]
if task_id in [2, 3]:
noise_weight = self.noise_weights[t, :].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # equivalent gaussian noise weights, [B, N, 1, 1, 1]
info_weight = self.info_weights[t, :].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # equivalent original info weights, [B, N, 1, 1, 1]
# random seed
torch.manual_seed(11)
noise = noise_weight * torch.randn_like(x_0, dtype=torch.float32, device=device) # [B, N, S, A, 2]
x_t = info_weight * x_0 + noise # [B, N, S, A, 2]
return x_t
def sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Add batch dimension.
# cond = torch.view_as_real(torch.from_numpy(cond['cond']).to(torch.complex64)).unsqueeze(0)
# cond = cond.unsqueeze(0)
# Construct a mini-batch.
# cond = cond.repeat((batch_size, 1, 1, 1, 1))
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
noise = torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, A, 2]
if self.task_id in [2,3]:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
else:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
x_s = inf_weight * noise # [B, N, S, A, 2]
# Restore data from noise.
for s in range(self.max_step-1, -1, -1): # reverse from t to 0
x_0_hat = restore_fn(x_s, s*torch.ones(batch_size, dtype=torch.int64), cond) # resotre \hat{x_0} from x_s using trained tfdiff model
if s > 0:
# x_{s-1} = D(\hat{x_0}, s-1)
x_s = self.degrade_fn(x_0_hat, t=(s-1)*torch.ones(batch_size, dtype=torch.int64), task_id = self.task_id) # degrade \hat{x_0} to x_{s-1}
return x_0_hat
def robust_sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Add batch dimension.
# cond = torch.view_as_real(torch.from_numpy(cond['cond']).to(torch.complex64)).unsqueeze(0)
# Construct a mini-batch.
# cond = cond.repeat((batch_size, 1, 1, 1, 1))
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
noise = torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, A, 2]
if self.task_id in [2,3]:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
else:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
x_s = inf_weight * noise # [B, N, S, A, 2]
# Restore data from noise.
for s in range(self.max_step-1, -1, -1): # reverse from t to 0
x_0_hat = restore_fn(x_s, s*torch.ones(batch_size, dtype=torch.int64), cond) # resotre \hat{x_0} from x_s using trained tfdiff model
if s > 0:
# x_{s-1} = x_s - D(\hat{x_0}, s) + D(\hat{x_0}, s-1)
x_s = x_s - self.degrade_fn(x_0_hat, t=s*torch.ones(batch_size, dtype=torch.int64),task_id = self.task_id) + self.degrade_fn(x_0_hat, t=(s-1)*torch.ones(batch_size, dtype=torch.int64),task_id = self.task_id) # degrade \hat{x_0} to x_{s-1}
return x_0_hat
def fast_sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
noise = torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, A, 2]
if self.task_id in [2,3]:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
else:
inf_weight = (self.noise_weights[batch_max, :] + self.info_weights[batch_max, :]).unsqueeze(-1).unsqueeze(-1).to(device) # [B, N, 1, 1, 1]
x_s = inf_weight * noise # [B, N, S, A, 2]
# Restore data from noise.
x_0_hat = restore_fn(x_s, batch_max, cond)
return x_0_hat
def native_sampling(self, restore_fn, data, cond, device):
batch_size = cond.shape[0]
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Generate degraded noise.
x_s = self.degrade_fn(data, batch_max,task_id = self.task_id).to(device)
# Restore data from noise.
x_0_hat = restore_fn(x_s, batch_max, cond)
return x_0_hat
class GaussianDiffusion(nn.Module):
def __init__(self, params):
super().__init__()
self.params = params
self.input_dim = self.params.sample_rate # input time-series data length, N
self.extra_dim = self.params.extra_dim # dimension of each data sample, e.g., [S A 2] for complex-valued CSI
self.max_step = self.params.max_step # maximum diffusion steps
beta = np.array(self.params.noise_schedule) # \beta, [T]
alpha = torch.tensor((1-beta).astype(np.float32)) # \alpha_t [T]
self.alpha_bar = torch.cumprod(alpha, dim=0) # \bar{\alpha_t}, [T]
# The overall weight of gaussian noise \epsilon in degraded data x_t
self.noise_weights = torch.sqrt(1 - self.alpha_bar) # \sqrt{1 - \bar{\alpha_t}}, [T]
self.info_weights = torch.sqrt(self.alpha_bar) # \sqrt{\bar{\alpha_t}}, [T]
def degrade_fn(self, x_0, t):
device = x_0.device
noise_weight = self.noise_weights[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # equivalent gaussian noise weights, [B, 1, 1, 1]
info_weight = self.info_weights[t].unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).to(device) # equivalent original info weights, [B, 1, 1, 1]
noise = noise_weight * torch.randn_like(x_0, dtype=torch.float32, device=device) # [B, N, S, 2]
# noise = noise_weight.unsqueeze(-1).unsqueeze(-1) * torch.randn_like(x_0, dtype=torch.float32, device=device) # [B, N, S, A, 2]
x_t = info_weight * x_0 + noise # [B, N, S, A, 2]
return x_t
def sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
inf_weight = (self.noise_weights[self.max_step-1] + self.info_weights[self.max_step-1]).to(device) # scalar
x_s = inf_weight * torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, 2]
# Restore data from noise.
for s in range(self.max_step-1, -1, -1): # reverse from t to 0
x_0_hat = restore_fn(x_s, s*torch.ones(batch_size, dtype=torch.int64), cond) # resotre \hat{x_0} from x_s using trained tfdiff model
if s > 0:
# x_{s-1} = D(\hat{x_0}, s-1)
x_s = self.degrade_fn(x_0_hat, t=(s-1)*torch.ones(batch_size, dtype=torch.int64)) # degrade \hat{x_0} to x_{s-1}
return x_0_hat
def robust_sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
inf_weight = (self.noise_weights[self.max_step-1] + self.info_weights[self.max_step-1]).to(device) # scalar
x_s = inf_weight * torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, A, 2]
# Restore data from noise.
for s in range(self.max_step-1, -1, -1): # reverse from t to 0
x_0_hat = restore_fn(x_s, s*torch.ones(batch_size, dtype=torch.int64), cond) # resotre \hat{x_0} from x_s using trained tfdiff model
if s > 0:
# x_{s-1} = x_s - D(\hat{x_0}, s) + D(\hat{x_0}, s-1)
x_s = x_s - self.degrade_fn(x_0_hat, t=[s]) + self.degrade_fn(self, x_0_hat, t=(s-1)*torch.ones(batch_size, dtype=torch.int64)) # degrade \hat{x_0} to x_{s-1}
return x_0_hat
def fast_sampling(self, restore_fn, cond, device):
batch_size = cond.shape[0] # B
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Generate degraded noise.
data_dim = [batch_size, self.input_dim] + self.extra_dim + [2]
noise = torch.randn(data_dim, dtype=torch.float32, device=device) # [B, N, S, A, 2]
inf_weight = (self.noise_weights[self.max_step-1] + self.info_weights[self.max_step-1]).to(device) # scalar
x_s = inf_weight * noise # [B, N, S, A, 2]
# Restore data from noise.
x_0_hat = restore_fn(x_s, batch_max, cond)
return x_0_hat
def native_sampling(self, restore_fn, data, cond, device):
batch_size = cond.shape[0]
batch_max = (self.max_step-1)*torch.ones(batch_size, dtype=torch.int64)
# Generate degraded noise.
x_s = self.degrade_fn(data, batch_max).to(device)
# Restore data from noise.
x_0_hat = restore_fn(x_s, batch_max, cond)
return x_0_hat
================================================
FILE: tfdiff/eeg_model.py
================================================
import math
from math import sqrt
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import complex.complex_module as cm
def init_weight_norm(module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def init_weight_zero(module):
if isinstance(module, nn.Linear):
nn.init.constant_(module.weight, 0)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def init_weight_xavier(module):
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
@torch.jit.script
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class DiffusionEmbedding(nn.Module):
def __init__(self, max_step, embed_dim=256, hidden_dim=256):
super().__init__()
self.register_buffer('embedding', self._build_embedding(
max_step, embed_dim), persistent=False)
self.projection = nn.Sequential(
cm.ComplexLinear(embed_dim, hidden_dim, bias=True),
cm.ComplexSiLU(),
cm.ComplexLinear(hidden_dim, hidden_dim, bias=True),
)
self.hidden_dim = hidden_dim
self.apply(init_weight_norm)
def forward(self, t):
if t.dtype in [torch.int32, torch.int64]:
x = self.embedding[t]
else:
x = self._lerp_embedding(t)
return self.projection(x)
def _lerp_embedding(self, t):
low_idx = torch.floor(t).long()
high_idx = torch.ceil(t).long()
low = self.embedding[low_idx]
high = self.embedding[high_idx]
return low + (high - low) * (t - low_idx)
def _build_embedding(self, max_step, embed_dim):
steps = torch.arange(max_step).unsqueeze(1) # [T,1]
dims = torch.arange(embed_dim).unsqueeze(0) # [1,E]
table = steps * torch.exp(-math.log(max_step)
* dims / embed_dim) # [T,E]
table = torch.view_as_real(torch.exp(1j * table))
return table
# TODO: Replace MLP with nn.Embedding
class MLPConditionEmbedding(nn.Module):
def __init__(self, cond_dim, hidden_dim=256):
super().__init__()
self.projection = nn.Sequential(
cm.ComplexLinear(cond_dim, hidden_dim, bias=True),
cm.ComplexSiLU(),
cm.ComplexLinear(hidden_dim, hidden_dim*4, bias=True),
cm.ComplexSiLU(),
cm.ComplexLinear(hidden_dim*4, hidden_dim, bias=True)
)
self.apply(init_weight_norm)
def forward(self, c):
return self.projection(c)
class PositionEmbedding(nn.Module):
def __init__(self, max_len, input_dim, hidden_dim):
super().__init__()
self.register_buffer('embedding', self._build_embedding(
max_len, hidden_dim), persistent=False)
self.projection = cm.ComplexLinear(input_dim, hidden_dim)
self.apply(init_weight_xavier)
def forward(self, x):
x = self.projection(x)
return cm.complex_mul(x, self.embedding.to(x.device))
def _build_embedding(self, max_len, hidden_dim):
steps = torch.arange(max_len).unsqueeze(1) # [P,1]
dims = torch.arange(hidden_dim).unsqueeze(0) # [1,E]
table = steps * torch.exp(-math.log(max_len)
* dims / hidden_dim) # [P,E]
table = torch.view_as_real(torch.exp(1j * table))
return table
class CDiTBlock(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = cm.NaiveComplexLayerNorm(
hidden_dim, eps=1e-6, elementwise_affine=False)
self.attn = cm.ComplexMultiHeadAttention(
hidden_dim, hidden_dim, num_heads, dropout, bias=True, **block_kwargs)
self.norm2 = cm.NaiveComplexLayerNorm(
hidden_dim, eps=1e-6, elementwise_affine=False)
mlp_hidden_dim = int(hidden_dim * mlp_ratio)
def approx_gelu(): return cm.ComplexGELU(approximate='tanh')
self.mlp = cm.ComplexMLP(
hidden_dim, mlp_hidden_dim, act_layer=approx_gelu, dropout=0)
self.adaLN_modulation = nn.Sequential(
cm.ComplexSiLU(),
cm.ComplexLinear(hidden_dim, 6*hidden_dim, bias=True)
)
self.apply(init_weight_xavier)
self.adaLN_modulation.apply(init_weight_zero)
def forward(self, x, c):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
c).chunk(6, dim=1)
x = x + \
gate_msa.unsqueeze(
1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa),\
modulate(self.norm1(x), shift_msa, scale_msa),\
modulate(self.norm1(x), shift_msa, scale_msa))
x = x + \
gate_mlp.unsqueeze(
1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
def __init__(self, hidden_dim, out_dim):
super().__init__()
self.norm = cm.NaiveComplexLayerNorm(
hidden_dim, eps=1e-6, elementwise_affine=False)
self.linear = cm.ComplexLinear(hidden_dim, out_dim, bias=True)
self.adaLN_modulation = nn.Sequential(
cm.ComplexSiLU(),
cm.ComplexLinear(hidden_dim, 2*hidden_dim, bias=True)
)
self.apply(init_weight_zero)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm(x), shift, scale)
x = self.linear(x)
return x
class tfdiff_eeg(nn.Module):
def __init__(self, params):
super().__init__()
self.params = params
self.learn_tfdiff = params.learn_tfdiff
self.input_dim = params.input_dim
self.output_dim = self.input_dim * 2 if self.learn_tfdiff else self.input_dim
self.hidden_dim = params.hidden_dim
self.num_heads = params.num_heads
self.dropout = params.dropout
self.task_id = params.task_id
self.mlp_ratio = params.mlp_ratio
self.p_embed = PositionEmbedding(
params.sample_rate, params.input_dim, params.hidden_dim)
self.t_embed = DiffusionEmbedding(
params.max_step, params.embed_dim, params.hidden_dim)
self.c_embed = MLPConditionEmbedding(params.cond_dim, params.hidden_dim)
self.blocks = nn.ModuleList([
CDiTBlock(self.hidden_dim, self.num_heads, self.dropout, self.mlp_ratio) for _ in range(params.num_block)
])
self.final_layer = FinalLayer(self.hidden_dim, self.output_dim)
def forward(self, x, t, c):
x = x.reshape(-1,512,1,2)
x = self.p_embed(x)
t = self.t_embed(t)
c = self.c_embed(c)
c = c + t
for block in self.blocks:
x = block(x, c)
x = self.final_layer(x, c)
return x
================================================
FILE: tfdiff/fmcw_model.py
================================================
import math
from math import sqrt
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
import complex.complex_module as cm
def init_weight_norm(module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
def init_weight_zero(module):
if isinstance(module, nn.Linear):
nn.init.constant_(module.weight, 0)
if module.bias is not None
gitextract_yuerfw2n/ ├── LICENSE ├── README.md ├── complex/ │ ├── __init__.py │ ├── complex_functions.py │ ├── complex_layers.py │ └── complex_module.py ├── inference.py ├── plots/ │ ├── code/ │ │ ├── Fig10-Scalability-analysis.py │ │ ├── Fig11(a)-Cross-domain-Performance-of-augmented-Wi-Fi-sensing.py │ │ ├── Fig11(b)-In-domain-Performance-of-augmented-Wi-Fi-sensing.py │ │ ├── Fig12-Impact-of-synthetic-data-volume.py │ │ ├── Fig13(a)-Performance-of-channel-estimation-amplitude-phase.py │ │ ├── Fig13(b)-Performance-of-channel-estimation-SNR.py │ │ ├── Fig6(a)-exp-overall-wifi-ssim.py │ │ ├── Fig6(b)-exp-overall-wifi-fid.py │ │ ├── Fig7(a)-exp-overall-fmcw-ssim.py │ │ ├── Fig7(b)-exp-overall-fmcw-fid.py │ │ ├── Fig8-Impact-of-diffusion-method.py │ │ └── Fig9-Impact-of-network-design.py │ ├── data/ │ │ ├── exp_MIMO.mat │ │ ├── exp_cross_domain.mat │ │ ├── exp_impact_diffusion_method.mat │ │ ├── exp_impact_network_design.mat │ │ ├── exp_impact_synthetic_data.mat │ │ ├── exp_in_domain.mat │ │ ├── exp_mimo_snr.mat │ │ ├── exp_overall_fid_fmcw.mat │ │ ├── exp_overall_fid_wifi.mat │ │ ├── exp_overall_ssim_fmcw.mat │ │ ├── exp_overall_ssim_wifi.mat │ │ ├── exp_scalability_analysis.mat │ │ ├── untitled.asv │ │ └── untitled.m │ └── requirements.txt ├── tfdiff/ │ ├── __init__.py │ ├── dataset.py │ ├── diffusion.py │ ├── eeg_model.py │ ├── fmcw_model.py │ ├── learner.py │ ├── mimo_model.py │ ├── params.py │ └── wifi_model.py └── train.py
SYMBOL INDEX (339 symbols across 14 files)
FILE: complex/complex_functions.py
function complex_matmul (line 10) | def complex_matmul(A, B):
function complex_avg_pool1d (line 20) | def complex_avg_pool1d(input, *args, **kwargs):
function complex_avg_pool2d (line 26) | def complex_avg_pool2d(input, *args, **kwargs):
function complex_normalize (line 35) | def complex_normalize(input):
function complex_leaky_relu (line 45) | def complex_leaky_relu(input, negative_slope):
function complex_relu (line 48) | def complex_relu(input):
function complex_sigmoid (line 51) | def complex_sigmoid(input):
function complex_tanh (line 54) | def complex_tanh(input):
function complex_opposite (line 57) | def complex_opposite(input):
function complex_stack (line 60) | def complex_stack(input, dim):
function _retrieve_elements_from_indices (line 65) | def _retrieve_elements_from_indices(tensor, indices):
function _retrieve_elements_from_indices3d (line 70) | def _retrieve_elements_from_indices3d(tensor, indices):
function complex_upsample (line 75) | def complex_upsample(input, size=None, scale_factor=None, mode='nearest',
function complex_upsample2 (line 87) | def complex_upsample2(input, size=None, scale_factor=None, mode='nearest',
function complex_max_pool2d (line 102) | def complex_max_pool2d(input,kernel_size, stride=None, padding=0,
function complex_max_pool3d (line 126) | def complex_max_pool3d(input,kernel_size, stride=None, padding=0,
function complex_adaptive_avg_pool3d (line 151) | def complex_adaptive_avg_pool3d(input,kernel_size, stride=None, padding=0,
function complex_dropout (line 176) | def complex_dropout(input, p=0.5, training=True):
function complex_dropout2d (line 186) | def complex_dropout2d(input, p=0.5, training=True):
function complex_dropout3d (line 195) | def complex_dropout3d(input, p=0.5, training=True):
FILE: complex/complex_layers.py
function apply_complex (line 20) | def apply_complex(fr, fi, input, dtype = torch.complex64):
class ComplexDropout (line 24) | class ComplexDropout(Module):
method __init__ (line 25) | def __init__(self,p=0.5):
method forward (line 29) | def forward(self,input):
class ComplexDropout2d (line 35) | class ComplexDropout2d(Module):
method __init__ (line 36) | def __init__(self,p=0.5):
method forward (line 40) | def forward(self,input):
class ComplexDropout3d (line 46) | class ComplexDropout3d(Module):
method __init__ (line 47) | def __init__(self,p=0.5):
method forward (line 51) | def forward(self,input):
class ComplexMaxPool2d (line 57) | class ComplexMaxPool2d(Module):
method __init__ (line 59) | def __init__(self,kernel_size, stride= None, padding = 0,
method forward (line 69) | def forward(self,input):
class ComplexMaxPool3d (line 75) | class ComplexMaxPool3d(Module):
method __init__ (line 77) | def __init__(self,kernel_size, stride=None, padding = 0,
method forward (line 87) | def forward(self,input):
class ComplexAvgPool2d (line 94) | class ComplexAvgPool2d(Module):
method __init__ (line 96) | def __init__(self,kernel_size, stride= None, padding = 0,
method forward (line 106) | def forward(self,input):
class ComplexReLU (line 113) | class ComplexReLU(Module):
method forward (line 115) | def forward(self,input):
class ComplexSigmoid (line 118) | class ComplexSigmoid(Module):
method forward (line 120) | def forward(self,input):
class ComplexTanh (line 123) | class ComplexTanh(Module):
method forward (line 125) | def forward(self,input):
class ComplexConvTranspose2d (line 128) | class ComplexConvTranspose2d(Module):
method __init__ (line 130) | def __init__(self,in_channels, out_channels, kernel_size, stride=1, pa...
method forward (line 141) | def forward(self,input):
class ComplexConv2d (line 144) | class ComplexConv2d(Module):
method __init__ (line 146) | def __init__(self,in_channels, out_channels, kernel_size=3, stride=1, ...
method forward (line 152) | def forward(self,input):
class ComplexConv3d (line 155) | class ComplexConv3d(Module):
method __init__ (line 157) | def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,...
method forward (line 163) | def forward(self,input):
class ComplexLinear (line 167) | class ComplexLinear(Module):
method __init__ (line 169) | def __init__(self, in_features, out_features):
method forward (line 174) | def forward(self, input):
class NaiveComplexBatchNorm1d (line 178) | class NaiveComplexBatchNorm1d(Module):
method __init__ (line 182) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
method forward (line 188) | def forward(self,input):
class NaiveComplexBatchNorm2d (line 191) | class NaiveComplexBatchNorm2d(Module):
method __init__ (line 195) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
method forward (line 201) | def forward(self,input):
class NaiveComplexBatchNorm3d (line 204) | class NaiveComplexBatchNorm3d(Module):
method __init__ (line 208) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
method forward (line 214) | def forward(self,input):
class NaiveComplexLayerNorm (line 217) | class NaiveComplexLayerNorm(Module):
method __init__ (line 221) | def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
method forward (line 226) | def forward(self,input):
class _ComplexBatchNorm (line 229) | class _ComplexBatchNorm(Module):
method __init__ (line 231) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
method reset_running_stats (line 257) | def reset_running_stats(self):
method reset_parameters (line 265) | def reset_parameters(self):
class ComplexBatchNorm2d (line 272) | class ComplexBatchNorm2d(_ComplexBatchNorm):
method forward (line 274) | def forward(self, input):
class ComplexBatchNorm1d (line 345) | class ComplexBatchNorm1d(_ComplexBatchNorm):
method forward (line 347) | def forward(self, input):
class ComplexGRUCell (line 419) | class ComplexGRUCell(Module):
method __init__ (line 424) | def __init__(self, input_length=10, hidden_length=20):
method reset_gate (line 443) | def reset_gate(self, x, h):
method update_gate (line 450) | def update_gate(self, x, h):
method update_component (line 456) | def update_component(self, x, h, r):
method forward (line 462) | def forward(self, x, h):
class ComplexBNGRUCell (line 477) | class ComplexBNGRUCell(Module):
method __init__ (line 482) | def __init__(self, input_length=10, hidden_length=20):
method reset_gate (line 503) | def reset_gate(self, x, h):
method update_gate (line 510) | def update_gate(self, x, h):
method update_component (line 516) | def update_component(self, x, h, r):
method forward (line 522) | def forward(self, x, h):
FILE: complex/complex_module.py
function apply_complex (line 9) | def apply_complex(F_r, F_i, X):
function apply_complex_sep (line 13) | def apply_complex_sep(F_r, F_i, X):
function complex_mul (line 18) | def complex_mul(X, Y):
function complex_bmm (line 26) | def complex_bmm(X, Y):
function complex_softmax (line 34) | def complex_softmax(X):
function transpose_qkv (line 39) | def transpose_qkv(x, num_heads: int):
function transpose_output (line 45) | def transpose_output(x, num_heads: int):
class ComplexDropout (line 51) | class ComplexDropout(nn.Module):
method __init__ (line 52) | def __init__(self, p=0.5):
method forward (line 56) | def forward(self, X):
class ComplexGELU (line 64) | class ComplexGELU(nn.Module):
method __init__ (line 65) | def __init__(self, approximate='none'):
method forward (line 70) | def forward(self, X):
class ComplexSiLU (line 74) | class ComplexSiLU(nn.Module):
method __init__ (line 75) | def __init__(self):
method forward (line 80) | def forward(self, X):
class ComplexReLU (line 84) | class ComplexReLU(nn.Module):
method __init__ (line 85) | def __init__(self):
method forward (line 90) | def forward(self, X):
class ComplexAvgPool3d (line 94) | class ComplexAvgPool3d(nn.Module):
method __init__ (line 95) | def __init__(self, kernel_size, stride, padding):
method forward (line 104) | def forward(self, X):
class ComplexFlatten (line 108) | class ComplexFlatten(nn.Module):
method __init__ (line 109) | def __init__(self, start_dim=1, end_dim=-1):
method forward (line 114) | def forward(self, X):
class NaiveComplexBatchNorm3d (line 118) | class NaiveComplexBatchNorm3d(nn.Module):
method __init__ (line 119) | def __init__(
method forward (line 135) | def forward(self, X):
class NaiveComplexLayerNorm (line 139) | class NaiveComplexLayerNorm(nn.Module):
method __init__ (line 140) | def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
method forward (line 145) | def forward(self, X):
class ComplexLinear (line 149) | class ComplexLinear(nn.Module):
method __init__ (line 150) | def __init__(self, in_features, out_features, bias=True):
method forward (line 155) | def forward(self, X):
class ComplexMLP (line 159) | class ComplexMLP(nn.Module):
method __init__ (line 160) | def __init__(self, in_features, hidden_features=None, out_features=Non...
method forward (line 170) | def forward(self, x):
class ComplexConv3d (line 178) | class ComplexConv3d(nn.Module):
method __init__ (line 179) | def __init__(self, input_channels, num_channels, kernel_size, padding,...
method forward (line 198) | def forward(self, X):
class ComplexResidual3d (line 202) | class ComplexResidual3d(nn.Module):
method __init__ (line 203) | def __init__(self, input_channels, num_channels, kernel_size, padding,...
method forward (line 227) | def forward(self, X):
class ComplexSegment (line 234) | class ComplexSegment(nn.Module):
method __init__ (line 235) | def __init__(self, input_channels, seg_channels, seg_size):
method forward (line 246) | def forward(self, X):
class Complex2Real (line 253) | class Complex2Real(nn.Module):
method __init__ (line 254) | def __init__(self):
method forward (line 259) | def forward(self, X):
class ComplexDotProductAttention (line 265) | class ComplexDotProductAttention(nn.Module):
method __init__ (line 271) | def __init__(self, dropout, **kwargs):
method forward (line 275) | def forward(self, queries, keys, values):
class ComplexMultiHeadAttention (line 284) | class ComplexMultiHeadAttention(nn.Module):
method __init__ (line 285) | def __init__(
method forward (line 306) | def forward(self, queries, keys, values):
class ComplexPositionalEncoding (line 316) | class ComplexPositionalEncoding(nn.Module):
method __init__ (line 317) | def __init__(self, hidden_dim, dropout, max_len=10000):
method forward (line 328) | def forward(self, X):
class PositionWiseFFN (line 334) | class PositionWiseFFN(nn.Module):
method __init__ (line 335) | def __init__(self, input_dim, hidden_dim, output_dim, **kwargs):
method forward (line 341) | def forward(self, X):
class ComplexAddNorm (line 346) | class ComplexAddNorm(nn.Module):
method __init__ (line 347) | def __init__(self, normalized_shape, dropout, **kwargs):
method forward (line 352) | def forward(self, X, Y):
class ComplexEncoderBlock (line 357) | class ComplexEncoderBlock(nn.Module):
method __init__ (line 358) | def __init__(
method forward (line 380) | def forward(self, X):
class ComplexTransformerEncoder (line 386) | class ComplexTransformerEncoder(nn.Module):
method __init__ (line 387) | def __init__(
method forward (line 423) | def forward(self, X, *args):
FILE: inference.py
function gaussian (line 31) | def gaussian(window_size: int, tfdiff: float):
function create_window (line 37) | def create_window(height: int, width: int):
function eval_ssim (line 45) | def eval_ssim(pred, data, height, width, device):
function cal_SNR_EEG (line 61) | def cal_SNR_EEG(predict, truth):
function cal_SNR_MIMO (line 71) | def cal_SNR_MIMO(predict, truth):
function save (line 84) | def save(out_dir, data, cond, batch, index=0):
function save_mimo (line 93) | def save_mimo(out_dir, data, pred, cond, batch, index=0):
function save_wifi (line 133) | def save_wifi(out_dir, data, pred, cond, batch, index=0):
function save_fmcw (line 188) | def save_fmcw(out_dir, data, pred, cond, batch,index=0):
function print_fid (line 251) | def print_fid(out_dir,data_dir,task_id):
function main (line 265) | def main(args):
FILE: plots/code/Fig13(b)-Performance-of-channel-estimation-SNR.py
function read_data_from_txt (line 8) | def read_data_from_txt(filename):
FILE: tfdiff/dataset.py
function _nested_map (line 20) | def _nested_map(struct, map_fn):
class WiFiDataset (line 30) | class WiFiDataset(torch.utils.data.Dataset):
method __init__ (line 31) | def __init__(self, paths):
method __len__ (line 37) | def __len__(self):
method __getitem__ (line 40) | def __getitem__(self, idx):
class FMCWDataset (line 52) | class FMCWDataset(torch.utils.data.Dataset):
method __init__ (line 53) | def __init__(self, paths):
method __len__ (line 59) | def __len__(self):
method __getitem__ (line 62) | def __getitem__(self, idx):
class MIMODataset (line 72) | class MIMODataset(torch.utils.data.Dataset):
method __init__ (line 73) | def __init__(self, paths):
method __len__ (line 79) | def __len__(self):
method __getitem__ (line 82) | def __getitem__(self,idx):
class EEGDataset (line 92) | class EEGDataset(torch.utils.data.Dataset):
method __init__ (line 93) | def __init__(self, paths):
method __len__ (line 99) | def __len__(self):
method __getitem__ (line 102) | def __getitem__(self,idx):
class Collator (line 112) | class Collator:
method __init__ (line 113) | def __init__(self, params):
method collate (line 116) | def collate(self, minibatch):
function from_path (line 195) | def from_path(params, is_distributed=False):
function from_path_inference (line 220) | def from_path_inference(params):
FILE: tfdiff/diffusion.py
class SignalDiffusion (line 6) | class SignalDiffusion(nn.Module):
method __init__ (line 7) | def __init__(self, params):
method get_kernel (line 28) | def get_kernel(self, var_kernel):
method get_noise_weights (line 34) | def get_noise_weights(self):
method get_noise_weights_stats (line 50) | def get_noise_weights_stats(self):
method get_noise_weights_div (line 58) | def get_noise_weights_div(self):
method get_noise_weights_prod (line 69) | def get_noise_weights_prod(self):
method degrade_fn (line 82) | def degrade_fn(self, x_0, t, task_id):
method sampling (line 97) | def sampling(self, restore_fn, cond, device):
method robust_sampling (line 121) | def robust_sampling(self, restore_fn, cond, device):
method fast_sampling (line 144) | def fast_sampling(self, restore_fn, cond, device):
method native_sampling (line 159) | def native_sampling(self, restore_fn, data, cond, device):
class GaussianDiffusion (line 169) | class GaussianDiffusion(nn.Module):
method __init__ (line 170) | def __init__(self, params):
method degrade_fn (line 183) | def degrade_fn(self, x_0, t):
method sampling (line 192) | def sampling(self, restore_fn, cond, device):
method robust_sampling (line 206) | def robust_sampling(self, restore_fn, cond, device):
method fast_sampling (line 220) | def fast_sampling(self, restore_fn, cond, device):
method native_sampling (line 232) | def native_sampling(self, restore_fn, data, cond, device):
FILE: tfdiff/eeg_model.py
function init_weight_norm (line 11) | def init_weight_norm(module):
function init_weight_zero (line 18) | def init_weight_zero(module):
function init_weight_xavier (line 25) | def init_weight_xavier(module):
function modulate (line 33) | def modulate(x, shift, scale):
class DiffusionEmbedding (line 37) | class DiffusionEmbedding(nn.Module):
method __init__ (line 38) | def __init__(self, max_step, embed_dim=256, hidden_dim=256):
method forward (line 50) | def forward(self, t):
method _lerp_embedding (line 57) | def _lerp_embedding(self, t):
method _build_embedding (line 64) | def _build_embedding(self, max_step, embed_dim):
class MLPConditionEmbedding (line 74) | class MLPConditionEmbedding(nn.Module):
method __init__ (line 75) | def __init__(self, cond_dim, hidden_dim=256):
method forward (line 86) | def forward(self, c):
class PositionEmbedding (line 89) | class PositionEmbedding(nn.Module):
method __init__ (line 90) | def __init__(self, max_len, input_dim, hidden_dim):
method forward (line 97) | def forward(self, x):
method _build_embedding (line 101) | def _build_embedding(self, max_len, hidden_dim):
class CDiTBlock (line 110) | class CDiTBlock(nn.Module):
method __init__ (line 111) | def __init__(self, hidden_dim, num_heads, dropout, mlp_ratio=4.0, **bl...
method forward (line 130) | def forward(self, x, c):
class FinalLayer (line 144) | class FinalLayer(nn.Module):
method __init__ (line 145) | def __init__(self, hidden_dim, out_dim):
method forward (line 156) | def forward(self, x, c):
class tfdiff_eeg (line 163) | class tfdiff_eeg(nn.Module):
method __init__ (line 164) | def __init__(self, params):
method forward (line 186) | def forward(self, x, t, c):
FILE: tfdiff/fmcw_model.py
function init_weight_norm (line 11) | def init_weight_norm(module):
function init_weight_zero (line 18) | def init_weight_zero(module):
function init_weight_xavier (line 25) | def init_weight_xavier(module):
function modulate (line 33) | def modulate(x, shift, scale):
class DiffusionEmbedding (line 37) | class DiffusionEmbedding(nn.Module):
method __init__ (line 38) | def __init__(self, max_step, embed_dim=256, hidden_dim=256):
method forward (line 50) | def forward(self, t):
method _lerp_embedding (line 57) | def _lerp_embedding(self, t):
method _build_embedding (line 64) | def _build_embedding(self, max_step, embed_dim):
class MLPConditionEmbedding (line 74) | class MLPConditionEmbedding(nn.Module):
method __init__ (line 75) | def __init__(self, cond_dim, hidden_dim=256):
method forward (line 86) | def forward(self, c):
class PositionEmbedding (line 90) | class PositionEmbedding(nn.Module):
method __init__ (line 91) | def __init__(self, max_len, input_dim, hidden_dim):
method forward (line 98) | def forward(self, x):
method _build_embedding (line 102) | def _build_embedding(self, max_len, hidden_dim):
class DiA (line 111) | class DiA(nn.Module):
method __init__ (line 112) | def __init__(self, hidden_dim, num_heads, dropout, mlp_ratio=4.0, **bl...
method forward (line 133) | def forward(self, x, c):
class FinalLayer (line 154) | class FinalLayer(nn.Module):
method __init__ (line 155) | def __init__(self, hidden_dim, out_dim):
method forward (line 166) | def forward(self, x, c):
class tfdiff_fmcw (line 320) | class tfdiff_fmcw(nn.Module):
method __init__ (line 321) | def __init__(self, params):
method forward (line 343) | def forward(self, x, t, c):
FILE: tfdiff/learner.py
class tfdiffLoss (line 11) | class tfdiffLoss(nn.Module):
method __init__ (line 12) | def __init__(self, w=0.1):
method forward (line 16) | def forward(self, target, est, target_noise=None, est_noise=None):
method complex_mse_loss (line 24) | def complex_mse_loss(self, target, est):
class tfdiffLearner (line 30) | class tfdiffLearner:
method __init__ (line 31) | def __init__(self, log_dir, model_dir, model, dataset, optimizer, para...
method state_dict (line 58) | def state_dict(self):
method load_state_dict (line 70) | def load_state_dict(self, state_dict):
method save_to_checkpoint (line 78) | def save_to_checkpoint(self, filename='weights'):
method restore_from_checkpoint (line 90) | def restore_from_checkpoint(self, filename='weights'):
method train (line 98) | def train(self, max_iter=None):
method train_iter (line 121) | def train_iter(self, features):
method _write_summary (line 140) | def _write_summary(self, iter, features, loss):
FILE: tfdiff/mimo_model.py
function init_weight_norm (line 11) | def init_weight_norm(module):
function init_weight_zero (line 18) | def init_weight_zero(module):
function init_weight_xavier (line 25) | def init_weight_xavier(module):
function modulate (line 33) | def modulate(x, shift, scale):
class DiffusionEmbedding (line 37) | class DiffusionEmbedding(nn.Module):
method __init__ (line 38) | def __init__(self, max_step, embed_dim=256, hidden_dim=256):
method forward (line 50) | def forward(self, t):
method _lerp_embedding (line 57) | def _lerp_embedding(self, t):
method _build_embedding (line 64) | def _build_embedding(self, max_step, embed_dim):
class MLPConditionEmbedding (line 74) | class MLPConditionEmbedding(nn.Module):
method __init__ (line 75) | def __init__(self, cond_dim, hidden_dim=256):
method forward (line 86) | def forward(self, c):
class PositionEmbedding (line 90) | class PositionEmbedding(nn.Module):
method __init__ (line 91) | def __init__(self, max_len, input_dim, hidden_dim):
method forward (line 98) | def forward(self, x):
method _build_embedding (line 102) | def _build_embedding(self, max_len, hidden_dim):
class DiA (line 111) | class DiA(nn.Module):
method __init__ (line 112) | def __init__(self, hidden_dim, num_heads, dropout, mlp_ratio=4.0, **bl...
method forward (line 139) | def forward(self, x, t, c):
class FinalLayer (line 162) | class FinalLayer(nn.Module):
method __init__ (line 163) | def __init__(self, hidden_dim, out_dim):
method forward (line 174) | def forward(self, x, t):
class SpatialDiffusion (line 181) | class SpatialDiffusion(nn.Module):
method __init__ (line 193) | def __init__(self, params):
method forward (line 226) | def forward(self, x, t, c):
class TimeFrequencyDiffusion (line 236) | class TimeFrequencyDiffusion(nn.Module):
method __init__ (line 248) | def __init__(self, params):
method forward (line 274) | def forward(self, x, t, c):
class tfdiff_mimo (line 285) | class tfdiff_mimo(nn.Module):
method __init__ (line 297) | def __init__(self, params):
method forward (line 309) | def forward(self, x, t, c):
FILE: tfdiff/params.py
class AttrDict (line 4) | class AttrDict(dict):
method __init__ (line 5) | def __init__(self, *args, **kwargs):
method override (line 9) | def override(self, attrs):
FILE: tfdiff/wifi_model.py
function init_weight_norm (line 11) | def init_weight_norm(module):
function init_weight_zero (line 18) | def init_weight_zero(module):
function init_weight_xavier (line 25) | def init_weight_xavier(module):
function modulate (line 33) | def modulate(x, shift, scale):
class DiffusionEmbedding (line 37) | class DiffusionEmbedding(nn.Module):
method __init__ (line 38) | def __init__(self, max_step, embed_dim=256, hidden_dim=256):
method forward (line 50) | def forward(self, t):
method _lerp_embedding (line 57) | def _lerp_embedding(self, t):
method _build_embedding (line 64) | def _build_embedding(self, max_step, embed_dim):
class MLPConditionEmbedding (line 74) | class MLPConditionEmbedding(nn.Module):
method __init__ (line 75) | def __init__(self, cond_dim, hidden_dim=256):
method forward (line 86) | def forward(self, c):
class PositionEmbedding (line 90) | class PositionEmbedding(nn.Module):
method __init__ (line 91) | def __init__(self, max_len, input_dim, hidden_dim):
method forward (line 98) | def forward(self, x):
method _build_embedding (line 102) | def _build_embedding(self, max_len, hidden_dim):
class DiA (line 111) | class DiA(nn.Module):
method __init__ (line 112) | def __init__(self, hidden_dim, num_heads, dropout, mlp_ratio=4.0, **bl...
method forward (line 133) | def forward(self, x, c):
class FinalLayer (line 154) | class FinalLayer(nn.Module):
method __init__ (line 155) | def __init__(self, hidden_dim, out_dim):
method forward (line 166) | def forward(self, x, c):
class tfdiff_WiFi (line 320) | class tfdiff_WiFi(nn.Module):
method __init__ (line 321) | def __init__(self, params):
method forward (line 343) | def forward(self, x, t, c):
FILE: train.py
function _get_free_port (line 18) | def _get_free_port():
function _train_impl (line 23) | def _train_impl(replica_id, model, dataset, params):
function train (line 31) | def train(params):
function train_distributed (line 42) | def train_distributed(replica_id, replica_count, port, params):
function main (line 64) | def main(args):
Condensed preview — 44 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (259K chars).
[
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 12569,
"preview": "# Artifact for MobiCom'24: RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion\r\n[![License: GPL v3](https"
},
{
"path": "complex/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "complex/complex_functions.py",
"chars": 9259,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: spopoff\n\"\"\"\nimport torch\nfrom torch.nn.functional import re"
},
{
"path": "complex/complex_layers.py",
"chars": 21686,
"preview": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 19 10:30:02 2019\n\n@author: Sebastien M. Popoff\n\n\nB"
},
{
"path": "complex/complex_module.py",
"chars": 13792,
"preview": "import torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nimport numpy as np\nimport math\n\n\ndef apply_comp"
},
{
"path": "inference.py",
"chars": 16562,
"preview": "import math\nimport numpy as np\nimport os\nimport torch\nimport scipy.io as scio\nimport matplotlib.pyplot as plt\nimport num"
},
{
"path": "plots/code/Fig10-Scalability-analysis.py",
"chars": 4759,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nfrom matplotlib.backends.backend_pdf import PdfP"
},
{
"path": "plots/code/Fig11(a)-Cross-domain-Performance-of-augmented-Wi-Fi-sensing.py",
"chars": 3579,
"preview": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mtick\r\nfrom matplotlib.ticker import Ma"
},
{
"path": "plots/code/Fig11(b)-In-domain-Performance-of-augmented-Wi-Fi-sensing.py",
"chars": 3577,
"preview": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mtick\r\nfrom matplotlib.ticker import Ma"
},
{
"path": "plots/code/Fig12-Impact-of-synthetic-data-volume.py",
"chars": 2399,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nfrom matplotlib.backends.backend_pdf import PdfP"
},
{
"path": "plots/code/Fig13(a)-Performance-of-channel-estimation-amplitude-phase.py",
"chars": 2199,
"preview": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io as scio\nfrom matplotlib.font_manager import FontPrope"
},
{
"path": "plots/code/Fig13(b)-Performance-of-channel-estimation-SNR.py",
"chars": 4307,
"preview": "import matplotlib.pyplot as plt\r\nimport matplotlib.font_manager as fm\r\nimport numpy as np\r\nfrom matplotlib.font_manager "
},
{
"path": "plots/code/Fig6(a)-exp-overall-wifi-ssim.py",
"chars": 2572,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib"
},
{
"path": "plots/code/Fig6(b)-exp-overall-wifi-fid.py",
"chars": 2566,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib"
},
{
"path": "plots/code/Fig7(a)-exp-overall-fmcw-ssim.py",
"chars": 2570,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib"
},
{
"path": "plots/code/Fig7(b)-exp-overall-fmcw-fid.py",
"chars": 2791,
"preview": "import numpy as np\r\nfrom matplotlib.font_manager import FontProperties\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib"
},
{
"path": "plots/code/Fig8-Impact-of-diffusion-method.py",
"chars": 2555,
"preview": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mtick\r\nfrom matplotlib.ticker import Ma"
},
{
"path": "plots/code/Fig9-Impact-of-network-design.py",
"chars": 2535,
"preview": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mtick\r\nfrom matplotlib.ticker import Ma"
},
{
"path": "plots/data/untitled.asv",
"chars": 202,
"preview": "ssim_mean = [0.8064, 0.7135, 0.4391];\nssim_std = [0.1128, 0.1547, 0.1958];\n\nfsd_mean = [4.417, 7.534, 14.72];\nfsd_std = "
},
{
"path": "plots/data/untitled.m",
"chars": 18845,
"preview": "mimo = [28.203256130218506,29.796597957611084,26.367104053497314,27.574849128723145,28.815438747406006,28.87958526611328"
},
{
"path": "plots/requirements.txt",
"chars": 22,
"preview": "matplotlib\nscipy\nnumpy"
},
{
"path": "tfdiff/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "tfdiff/dataset.py",
"chars": 8259,
"preview": "import numpy as np\nimport os\nimport random\nimport torch\nimport torch.nn.functional as F\nimport scipy.io as scio\nfrom tfd"
},
{
"path": "tfdiff/diffusion.py",
"chars": 15759,
"preview": "import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass SignalDiffusion(nn.Modu"
},
{
"path": "tfdiff/eeg_model.py",
"chars": 7180,
"preview": "import math\nfrom math import sqrt\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional a"
},
{
"path": "tfdiff/fmcw_model.py",
"chars": 12915,
"preview": "import math\nfrom math import sqrt\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional a"
},
{
"path": "tfdiff/learner.py",
"chars": 6258,
"preview": "import numpy as np\nimport os\nimport torch\nimport torch.nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\nfrom t"
},
{
"path": "tfdiff/mimo_model.py",
"chars": 11535,
"preview": "import math\nfrom math import sqrt\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional a"
},
{
"path": "tfdiff/params.py",
"chars": 5346,
"preview": "import numpy as np\n\n\nclass AttrDict(dict):\n def __init__(self, *args, **kwargs):\n super(AttrDict, self).__init"
},
{
"path": "tfdiff/wifi_model.py",
"chars": 12914,
"preview": "import math\nfrom math import sqrt\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional a"
},
{
"path": "train.py",
"chars": 4168,
"preview": "import os\n\nimport torch\nfrom torch.cuda import device_count\nfrom torch.multiprocessing import spawn\nfrom torch.nn.parall"
}
]
// ... and 12 more files (download for full content)
About this extraction
This page contains the full source code of the mobicom24/RF-Diffusion GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 44 files (243.0 KB), approximately 69.0k tokens, and a symbol index with 339 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.