Repository: somanchiu/ReSwapper
Branch: main
Commit: 44474cb8d85d
Files: 15
Total size: 89.4 KB
Directory structure:
gitextract_u0251wd1/
├── FaceAttribute.py
├── Image.py
├── LICENSE
├── ModelFormat.py
├── README.md
├── StyleTransferLoss.py
├── StyleTransferModel_128.py
├── emap.npy
├── face_align.py
├── iresnet.py
├── requirements-colab.txt
├── requirements.txt
├── swap.py
├── train.py
└── weight_transfer.py
================================================
FILE CONTENTS
================================================
================================================
FILE: FaceAttribute.py
================================================
import os
import numpy as np
import swap
def create_linear_direction_dataset(dataset_dir, output_path = None):
image_paths = []
for root, _, files in os.walk(dataset_dir):
for file in files:
image_paths.append(os.path.join(root, file))
embeds_list = []
for path in image_paths:
embed = swap.create_source(path)
if embed is not None:
embeds_list.append(embed)
embeds = np.stack(embeds_list)
if output_path is not None:
np.save(output_path, embeds)
return embeds
def get_direction(dataset_a, dataset_b, output_path = None):
direction = np.mean(dataset_a, axis=0) - np.mean(dataset_b, axis=0)
if output_path is not None:
np.save(output_path, direction)
return direction
================================================
FILE: Image.py
================================================
import cv2
import numpy as np
emap = np.load("emap.npy")
input_std = 255.0
input_mean = 0.0
def postprocess_face(face_tensor):
face_tensor = face_tensor.squeeze().cpu().detach()
face_np = (face_tensor.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
face_np = cv2.cvtColor(face_np, cv2.COLOR_RGB2BGR)
return face_np
def getBlob(aimg, input_size = (128, 128)):
blob = cv2.dnn.blobFromImage(aimg, 1.0 / input_std, input_size,
(input_mean, input_mean, input_mean), swapRB=True)
return blob
def getLatent(source_face):
latent = source_face.normed_embedding.reshape((1,-1))
latent = np.dot(latent, emap)
latent /= np.linalg.norm(latent)
return latent
def blend_swapped_image(swapped_face, target_image, M):
# get image size
h, w = target_image.shape[:2]
# create inverse affine transform
M_inv = cv2.invertAffineTransform(M)
# warp swapped face back to target space
warped_face = cv2.warpAffine(
swapped_face,
M_inv,
(w, h),
borderValue=0.0
)
# create initial white mask
img_white = np.full(
(swapped_face.shape[0], swapped_face.shape[1]),
255,
dtype=np.float32
)
# warp white mask to target space
img_mask = cv2.warpAffine(
img_white,
M_inv,
(w, h),
borderValue=0.0
)
# threshold and refine mask
img_mask[img_mask > 20] = 255
# calculate mask size for kernel scaling
mask_h_inds, mask_w_inds = np.where(img_mask == 255)
if len(mask_h_inds) > 0 and len(mask_w_inds) > 0: # safety check
mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
mask_size = int(np.sqrt(mask_h * mask_w))
# erode mask
k = max(mask_size // 10, 10)
kernel = np.ones((k, k), np.uint8)
img_mask = cv2.erode(img_mask, kernel, iterations=1)
# blur mask
k = max(mask_size // 20, 5)
kernel_size = (k, k)
blur_size = tuple(2 * i + 1 for i in kernel_size)
img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
# normalize mask
img_mask = img_mask / 255.0
img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
# blend images using mask
result = img_mask * warped_face + (1 - img_mask) * target_image.astype(np.float32)
result = result.astype(np.uint8)
return result
def drawKeypoints(image, keypoints, colorBGR, keypointsRadius=2):
for kp in keypoints:
x, y = int(kp[0]), int(kp[1])
cv2.circle(image, (x, y), radius=keypointsRadius, color=colorBGR, thickness=-1) # BGR format, -1 means filled circle
================================================
FILE: LICENSE
================================================
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17. Interpretation of Sections 15 and 16.
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Program, unless a warranty or assumption of liability accompanies a
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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 Affero General Public License as published
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This program is distributed in the hope that it will be useful,
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Also add information on how to contact you by electronic and paper mail.
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get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
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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 AGPL, see
<https://www.gnu.org/licenses/>.
================================================
FILE: ModelFormat.py
================================================
import numpy as np
import onnx
import torch
from StyleTransferModel_128 import StyleTransferModel
def save_as_onnx_model(torch_model_path, save_emap=True, img_size = 128, originalInswapperClassCompatible = True):
output_path = torch_model_path.replace(".pth", ".onnx")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Initialize model with the pretrained weights
torch_model = StyleTransferModel().to(device)
torch_model.load_state_dict(torch.load(torch_model_path, map_location=device), strict=False)
# set the model to inference mode
torch_model.eval()
if originalInswapperClassCompatible:
dynamic_axes = None
else:
image_axe = {0: 'batch_size', 1: 'channels', 2: 'height', 3: 'width'}
dynamic_axes = {'target': image_axe, # variable length axes
'source': {0: 'batch_size'},
'output' : image_axe}
torch.onnx.export(torch_model, # model being run
{
'target' :torch.randn(1, 3, img_size, img_size, requires_grad=True).to(device),
'source': torch.randn(1, 512, requires_grad=True).to(device),
}, # model input (or a tuple for multiple inputs)
output_path, # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['target', "source"], # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes=dynamic_axes)
model = onnx.load(output_path)
if save_emap :
emap = np.load("emap.npy")
emap_tensor = onnx.helper.make_tensor(
name='emap',
data_type=onnx.TensorProto.FLOAT,
dims=[512, 512],
vals=emap
)
model.graph.initializer.append(emap_tensor)
onnx.save(model, output_path)
================================================
FILE: README.md
================================================
# ReSwapper
ReSwapper aims to reproduce the implementation of inswapper. This repository provides code for training, inference, and includes pretrained weights.
Here is the comparesion of the output of Inswapper and Reswapper.
| Target | Source | Inswapper Output | Reswapper Output<br>(256 resolution)<br>(Step 1399500) | Reswapper Output<br>(Step 1019500) | Reswapper Output<br>(Step 429500) |
|--------|--------|--------|--------|--------|--------|
|  | |  |  | |  |
|  | |  |  |  |  |
|  | |  |  |  |  |
## Installation
```bash
git clone https://github.com/somanchiu/ReSwapper.git
cd ReSwapper
python -m venv venv
venv\scripts\activate
pip install -r requirements.txt
pip install torch torchvision --force --index-url https://download.pytorch.org/whl/cu121
pip install onnxruntime-gpu --force --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
```
## The details of inswapper
### Model architecture
The inswapper model architecture can be visualized in [Netron](https://netron.app). You can compare with ReSwapper implementation to see architectural similarities. Exporting the model with opset_version=10 makes it easier to compare the graph in Netron. However, it will cause issue #8.
We can also use the following Python code to get more details:
```python
model = onnx.load('test.onnx')
printable_graph=onnx.helper.printable_graph(model.graph)
```
The model architectures of InSwapper and SimSwap are extremely similar and worth paying attention to.
### Model inputs
- target: [1, 3, 128, 128] shape image in RGB format with face alignment, normalized to [0, 1] range
- source (latent): [1, 512] shape vector, the features of the source face, obtained using the ArcFace model.
- Calculation of latent
- The details of ArcFace model
- Architecture: IResNet50
- Input: [1, 3, 112, 112] shape image in RGB format with face alignment, normalized to [-1, 1] range
- Output: [1, 512] shape vector
- "emap" can be extracted from the original inswapper model.
```python
from numpy.linalg import norm as l2norm
input_mean = 127.5
input_std = 127.5
input_size = (112, 112)
aimg, _ = face_align.norm_crop2(img, landmark=face.kps, image_size=input_size[0])
blob = cv2.dnn.blobFromImages([aimg], 1.0 / input_std, input_size,
(input_mean, input_mean, input_mean), swapRB=True)
net_out = session.run(output_names, {input_name: blob})[0]
embedding = net_out.flatten()
normed_embedding = embedding / l2norm(embedding)
latent = normed_embedding.reshape((1,-1))
latent = np.dot(latent, emap)
latent /= np.linalg.norm(latent)
```
- It can also be used to calculate the similarity between two faces using cosine similarity.
### Model output
Model inswapper_128 not only changes facial features, but also body shape.
| Target | Source | Inswapper Output | Reswapper Output<br>(Step 429500) |
|--------|--------|--------|--------|
|  | |  |  |
### Loss Functions
There is no information released from insightface. It is an important part of the training. However, there are a lot of articles and papers that can be referenced. By reading a substantial number of articles and papers on face swapping, ID fidelity, and style transfer, you'll frequently encounter the following keywords:
- content loss
- style loss/id loss
- perceptual loss
### Face alignment
Face alignment is handled incorrectly at resolutions other than 128. To resolve this issue, add an offset to "dst" in both x and y directions in the function "face_align.estimate_norm". The offset is approximately given by the formula: Offset = (128/32768) * Resolution - 0.5
## Training
<details open>
<summary>GAN Approach</summary>
See the [GAN branch](https://github.com/somanchiu/ReSwapper/tree/GAN)
</details>
<details open>
<summary>Supervised Learning Approach</summary>
### 0. Pretrained weights (Optional)
If you don't want to train the model from scratch, you can download the pretrained weights and pass model_path into the train function in train.py.
### 1. Dataset Preparation
Download [FFHQ](https://www.kaggle.com/datasets/arnaud58/flickrfaceshq-dataset-ffhq) to use as target and source images. For the swaped face images, we can use the inswapper output.
### 2. Model Training
Optimizer: Adam
Learning rate: 0.0001
Modify the code in train.py if needed. Then, execute:
```python
python train.py
```
The model will be saved as "reswapper-\<total steps\>.pth". You can also save the model as ONNX using the ModelFormat.save_as_onnx_model function. The ONNX model can then be used with the original INSwapper class.
All losses will be logged into TensorBoard.
Using images with different resolutions simultaneously to train the model will enhance its generalization ability. To apply this strategy, you can pass "resolutions" into the train function.
Generalization ability of the model trained with resolutions of 128 and 256:
| Output<br>resolution | 128 | 160 | 256 |
|--------|--------|--------|--------|
|Output|  | | |
Enhancing data diversity will improve output quality, you can pass "enableDataAugmentation" into the train function to perform data augmentation.
| Target | Source | Inswapper Output | Reswapper Output<br>(Step 1567500) | Reswapper Output<br>(Step 1399500) |
|--------|--------|--------|--------|--------|
||  | | |  |
#### Notes
- Do not stop the training too early.
- I'm using an RTX3060 12GB for training. It takes around 12 hours for 50,000 steps.
- The optimizer may need to be changed to SGD for the final training, as many articles show that SGD can result in lower loss.
- To get inspiration for improving the model, you might want to review the commented code and unused functions in commit [c2a12e10021ecd1342b9ba50570a16b18f9634b9](https://github.com/somanchiu/ReSwapper/commit/c2a12e10021ecd1342b9ba50570a16b18f9634b9).
</details>
## Inference
```python
python swap.py
```
## Face Attribute Modification
The source embedding contains information about various facial attributes. Modifying the source enables adjustments to specific attributes.
### 1. Paired Datasets Collection
For example, modifying facial hair (Beard vs. No Beard):
```python
dataset_a = FaceAttribute.create_linear_direction_dataset("Beard or No Beared\\Train\\Beard", "beard.npy")
dataset_b = FaceAttribute.create_linear_direction_dataset("Beard or No Beared\\Train\\No Beard", "no_beard.npy")
```
### 2. Attribute Direction Calculation
```python
direction = FaceAttribute.get_direction(dataset_a, dataset_b, "direction.npy")
```
### 3. Source Embedding Modification
```python
direction = direction / np.linalg.norm(direction)
latent += direction * face_attribute_steps
```
Here is the output of Inswapper after modifying the source embedding
| face_attribute_steps | 0 (Original output) | 0.25 | 0.5 | 0.75 | 1.0 |
|--------|--------|--------|--------|--------|--------|
|beard_direction.npy||||||
## Pretrained Model
### 256 Resolution
- [reswapper_256-1567500.pth](https://huggingface.co/somanchiu/reswapper/tree/main)
- [reswapper_256-1399500.pth](https://huggingface.co/somanchiu/reswapper/tree/main)
### 128 Resolution
- [reswapper-1019500.pth](https://huggingface.co/somanchiu/reswapper/tree/main)
- [reswapper-1019500.onnx](https://huggingface.co/somanchiu/reswapper/tree/main)
- [reswapper-429500.pth](https://huggingface.co/somanchiu/reswapper/tree/main)
- [reswapper-429500.onnx](https://huggingface.co/somanchiu/reswapper/tree/main)
### Notes
If you downloaded the ONNX format model before 2024/11/25, please download the model again or export the model with opset_version=11. This is related to issue #8.
## Attribute Direction
- [beard_direction.npy
](https://huggingface.co/somanchiu/reswapper/tree/main/attributeDirection)
## To Do
- Create a 512-resolution model (alternative to inswapper_512)
================================================
FILE: StyleTransferLoss.py
================================================
import cv2
import torch
import torch.nn as nn
import numpy as np
from insightface.app import FaceAnalysis
from pytorch_msssim import ssim
import Image
class StyleTransferLoss(nn.Module):
def __init__(self, device='cuda', face_analysis = None):
super(StyleTransferLoss, self).__init__()
if face_analysis is None:
self.face_analysis = FaceAnalysis(providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
self.face_analysis.prepare(ctx_id=0, det_size=(128, 128))
else:
self.face_analysis = face_analysis
self.device = device
self.cosine_similarity = nn.CosineSimilarity(dim=0)
# Content loss
self.content_loss = nn.MSELoss()
def extract_face_latent(self, image):
# Convert torch tensor to numpy array
face_tensor = image.squeeze().cpu().detach()
face_np = (face_tensor.permute(1, 2, 0).numpy() * 255).astype(np.uint8)
face_np = cv2.cvtColor(face_np, cv2.COLOR_RGB2BGR)
# Extract face embedding
faces = self.face_analysis.get(face_np)
if len(faces) == 0:
return None
return torch.tensor(Image.getLatent(faces[0])[0]).to(self.device)
def forward(self, output_image, target_content):
# Content loss
# content_loss = self.content_loss(output_image, target_content)
content_loss = 1 - ssim(output_image, target_content, data_range=1.0)
output_embedding = self.extract_face_latent(output_image)
target_embedding = self.extract_face_latent(target_content)
identity_loss = None
if output_embedding is not None and target_embedding is not None:
similarity = self.cosine_similarity(output_embedding, target_embedding)
identity_loss = 1-((similarity + 1) / 2)
identity_loss = identity_loss ** 2 * 10
return content_loss, identity_loss
================================================
FILE: StyleTransferModel_128.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
class StyleTransferModel(nn.Module):
def __init__(self):
super(StyleTransferModel, self).__init__()
# self.pad = nn.ReflectionPad2d(3)
# Encoder for target face
self.target_encoder = nn.Sequential(
# self.pad,
nn.Conv2d(3, 128, kernel_size=7, stride=1, padding=0),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2),
)
# for style_block in self.target_encoder:
# for param in style_block.parameters():
# param.requires_grad = False
# Style blocks
self.style_blocks = nn.ModuleList([
StyleBlock(1024, 1024, blockIndex) for blockIndex in range(6)
])
# Decoder (upsampling)
self.decoder = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2)
)
self.decoderPart1 = nn.Sequential(
nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2)
)
self.decoderPart2 = nn.Sequential(
# self.pad,
nn.Conv2d(128, 3, kernel_size=7, stride=1, padding=0),
nn.Tanh()
)
def forward(self, target, source):
# Encode target face
target = F.pad(target, pad=(3, 3, 3, 3), mode='reflect')
target_features = self.target_encoder(target)
# Apply style blocks
x = target_features
for style_block in self.style_blocks:
x = style_block(x, source)
# Decode
# x = F.interpolate(x, scale_factor=2, mode='linear')
x = F.upsample(
x,
scale_factor=2, # specify the desired height and width
mode='bilinear', # 'linear' in 2D is called 'bilinear'
align_corners=False # this is typically False for ONNX compatibility
)
output = self.decoder(x)
output = F.upsample(
output,
scale_factor=2, # specify the desired height and width
mode='bilinear', # 'linear' in 2D is called 'bilinear'
align_corners=False # this is typically False for ONNX compatibility
)
output = self.decoderPart1(output)
output = F.pad(output, pad=(3, 3, 3, 3), mode='reflect')
output = self.decoderPart2(output)
return (output + 1) / 2
class StyleBlock(nn.Module):
def __init__(self, in_channels, out_channels, blockIndex):
super(StyleBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=0)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=0)
self.style1 = nn.Linear(512, 2048)
self.style2 = nn.Linear(512, 2048)
self.style = [self.style1, self.style2]
self.blockIndex = blockIndex
def normalizeConvRMS(self, conv):
x = conv - torch.mean(conv, dim=[2, 3], keepdim=True) # centeredConv
squareX = x * x
meanSquaredX = torch.mean(squareX, dim=[2, 3], keepdim=True)
rms = torch.sqrt(meanSquaredX + 0.00000001)
return (1 / rms) * x
def forward(self, residual, style):
# print(f'Forward: {self.blockIndex}')
style1024 = []
for index in range(2):
style1 = self.style[index](style)
style1 = torch.unsqueeze(style1, 2)
style1 = torch.unsqueeze(style1, 3)
first_half = style1[:, :1024, :, :]
second_half = style1[:, 1024:, :, :]
style1024.append([first_half, second_half])
conv1 = self.normalizeConvRMS(self.conv1(F.pad(residual, pad=(1, 1, 1, 1), mode='reflect')))
out = F.relu(conv1 * style1024[0][0] + style1024[0][1])
out = F.pad(out, pad=(1, 1, 1, 1), mode='reflect')
conv2 = self.normalizeConvRMS(self.conv2(out))
out = conv2 * style1024[1][0] + style1024[1][1]
return residual + out
================================================
FILE: face_align.py
================================================
import cv2
import numpy as np
from skimage import transform as trans
arcface_dst = np.array(
[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
[41.5493, 92.3655], [70.7299, 92.2041]],
dtype=np.float32)
def estimate_norm(lmk, image_size=112,mode='arcface'):
# assert lmk.shape == (5, 2)
# assert image_size%112==0 or image_size%128==0
if image_size%112==0:
ratio = float(image_size)/112.0
diff_x = 0
else:
ratio = float(image_size)/128.0
diff_x = 8.0*ratio
dst = arcface_dst * ratio
dst[:,0] += diff_x
if image_size != 128:
offset = (128/32768)*image_size-0.5
dst[:,0] += offset
dst[:,1] += offset
tform = trans.SimilarityTransform()
tform.estimate(lmk, dst)
M = tform.params[0:2, :]
return M
def norm_crop(img, landmark, image_size=112, mode='arcface'):
M = estimate_norm(landmark, image_size, mode)
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
return warped
def norm_crop2(img, landmark, image_size=112, mode='arcface'):
M = estimate_norm(landmark, image_size, mode)
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
return warped, M
def square_crop(im, S):
if im.shape[0] > im.shape[1]:
height = S
width = int(float(im.shape[1]) / im.shape[0] * S)
scale = float(S) / im.shape[0]
else:
width = S
height = int(float(im.shape[0]) / im.shape[1] * S)
scale = float(S) / im.shape[1]
resized_im = cv2.resize(im, (width, height))
det_im = np.zeros((S, S, 3), dtype=np.uint8)
det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im
return det_im, scale
def transform(data, center, output_size, scale, rotation):
scale_ratio = scale
rot = float(rotation) * np.pi / 180.0
#translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
t1 = trans.SimilarityTransform(scale=scale_ratio)
cx = center[0] * scale_ratio
cy = center[1] * scale_ratio
t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy))
t3 = trans.SimilarityTransform(rotation=rot)
t4 = trans.SimilarityTransform(translation=(output_size / 2,
output_size / 2))
t = t1 + t2 + t3 + t4
M = t.params[0:2]
cropped = cv2.warpAffine(data,
M, (output_size, output_size),
borderValue=0.0)
return cropped, M
def trans_points2d(pts, M):
new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
for i in range(pts.shape[0]):
pt = pts[i]
new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
new_pt = np.dot(M, new_pt)
#print('new_pt', new_pt.shape, new_pt)
new_pts[i] = new_pt[0:2]
return new_pts
def trans_points3d(pts, M):
scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
#print(scale)
new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
for i in range(pts.shape[0]):
pt = pts[i]
new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32)
new_pt = np.dot(M, new_pt)
#print('new_pt', new_pt.shape, new_pt)
new_pts[i][0:2] = new_pt[0:2]
new_pts[i][2] = pts[i][2] * scale
return new_pts
def trans_points(pts, M):
if pts.shape[1] == 2:
return trans_points2d(pts, M)
else:
return trans_points3d(pts, M)
================================================
FILE: iresnet.py
================================================
# a modified version of https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/backbones/iresnet.py
import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
using_ckpt = False
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=True,
dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=True)
class IBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,
groups=1, base_width=64, dilation=1):
super(IBasicBlock, self).__init__()
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
self.prelu = nn.PReLU(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
self.downsample = downsample
self.stride = stride
def forward_impl(self, x):
identity = x
out = self.bn1(x)
out = self.conv1(out)
out = self.bn2(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
return out
def forward(self, x):
if self.training and using_ckpt:
return checkpoint(self.forward_impl, x)
else:
return self.forward_impl(x)
class IResNet(nn.Module):
fc_scale = 7 * 7
def __init__(self,
block, layers, dropout=0, num_features=512, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
super(IResNet, self).__init__()
self.extra_gflops = 0.0
self.fp16 = fp16
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=True)
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
self.prelu = nn.PReLU(self.inplanes)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
self.dropout = nn.Dropout(p=dropout, inplace=True)
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
nn.init.constant_(self.features.weight, 1.0)
self.features.weight.requires_grad = False
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.normal_(m.weight, 0, 0.1)
# elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
# if zero_init_residual:
# for m in self.modules():
# if isinstance(m, IBasicBlock):
# nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
)
layers = []
layers.append(
block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation))
return nn.Sequential(*layers)
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn2(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x.float() if self.fp16 else x)
x = self.features(x)
return x
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
model = IResNet(block, layers, **kwargs)
if pretrained:
raise ValueError()
return model
def iresnet18(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
progress, **kwargs)
def iresnet34(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
progress, **kwargs)
def iresnet50(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
progress, **kwargs)
def iresnet100(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
progress, **kwargs)
def iresnet200(pretrained=False, progress=True, **kwargs):
return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
progress, **kwargs)
================================================
FILE: requirements-colab.txt
================================================
absl-py==2.1.0
addict==2.4.0
albucore==0.0.17
albumentations==1.4.17
annotated-types==0.7.0
certifi==2024.8.30
chardet==3.0.4
charset-normalizer==3.3.2
colorama==0.4.6
coloredlogs==15.0.1
contourpy==1.3.0
cycler==0.12.1
Cython==3.0.11
easydict==1.13
einops==0.8.0
eval_type_backport==0.2.0
facexlib==0.3.0
filelock==3.13.1
filterpy==1.4.5
flatbuffers==24.3.25
fonttools==4.54.1
fsspec==2024.2.0
ftfy==6.2.3
future==1.0.0
grpcio==1.66.1
huggingface-hub==0.25.0
humanfriendly==10.0
idna==3.10
imageio==2.35.1
importlib_metadata==8.5.0
insightface==0.7.3
Jinja2==3.1.3
joblib==1.4.2
kiwisolver==1.4.7
lazy_loader==0.4
llvmlite==0.43.0
lmdb==1.5.1
Markdown==3.7
MarkupSafe==2.1.5
matplotlib==3.9.2
mpmath==1.3.0
networkx==3.3
numba==0.60.0
numpy==1.26.4
onnx==1.17.0
onnxruntime==1.18.1
onnxruntime-gpu==1.19.2
opencv-python==4.10.0.84
opencv-python-headless==4.10.0.84
packaging==24.1
pillow==10.4.0
platformdirs==4.3.6
prettytable==3.11.0
protobuf==5.28.2
pydantic==2.9.2
pydantic_core==2.23.4
pyparsing==3.1.4
pyreadline3==3.4.1
python-dateutil==2.9.0.post0
pytorch-msssim==1.0.0
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
safetensors==0.4.5
scikit-image==0.24.0
scikit-learn==1.5.2
scipy==1.14.1
six==1.16.0
sympy==1.13.2
tensorboard==2.17.1
tensorboard-data-server==0.7.2
threadpoolctl==3.5.0
tifffile==2024.9.20
timm==1.0.9
tokenizers==0.15.2
tomli==2.0.1
torch==2.4.1
tqdm==4.66.5
transformers==4.36.2
typing_extensions==4.12.2
urllib3==2.2.3
wcwidth==0.2.13
Werkzeug==3.0.4
yapf==0.40.2
zipp==3.20.2
================================================
FILE: requirements.txt
================================================
absl-py==2.1.0
addict==2.4.0
albucore==0.0.17
albumentations==1.4.17
annotated-types==0.7.0
certifi==2024.8.30
chardet==3.0.4
charset-normalizer==3.3.2
colorama==0.4.6
coloredlogs==15.0.1
contourpy==1.3.0
cycler==0.12.1
Cython==3.0.11
easydict==1.13
einops==0.8.0
eval_type_backport==0.2.0
facexlib==0.3.0
filelock==3.13.1
filterpy==1.4.5
flatbuffers==24.3.25
fonttools==4.54.1
fsspec==2024.2.0
ftfy==6.2.3
future==1.0.0
grpcio==1.66.1
huggingface-hub==0.25.0
humanfriendly==10.0
idna==3.10
imageio==2.35.1
importlib_metadata==8.5.0
insightface==0.7.3
Jinja2==3.1.3
joblib==1.4.2
kiwisolver==1.4.7
lazy_loader==0.4
llvmlite==0.43.0
lmdb==1.5.1
Markdown==3.7
MarkupSafe==2.1.5
matplotlib==3.9.2
mpmath==1.3.0
networkx==3.3
numba==0.60.0
numpy==1.26.4
onnx==1.17.0
onnxruntime==1.18.1
onnxruntime-gpu==1.19.2
opencv-python==4.10.0.84
opencv-python-headless==4.10.0.84
packaging==24.1
pillow==10.4.0
platformdirs==4.3.6
prettytable==3.11.0
protobuf==5.28.2
pydantic==2.9.2
pydantic_core==2.23.4
pyparsing==3.1.4
pyreadline3==3.4.1
python-dateutil==2.9.0.post0
pytorch-msssim==1.0.0
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
safetensors==0.4.5
scikit-image==0.24.0
scikit-learn==1.5.2
scipy==1.14.1
six==1.16.0
sympy==1.13.2
tensorboard==2.17.1
tensorboard-data-server==0.7.2
threadpoolctl==3.5.0
tifffile==2024.9.20
timm==1.0.9
tokenizers==0.15.2
tomli==2.0.1
torch==2.4.1+cu121
torchvision==0.19.1+cu121
tqdm==4.66.5
transformers==4.36.2
typing_extensions==4.12.2
urllib3==2.2.3
wcwidth==0.2.13
Werkzeug==3.0.4
yapf==0.40.2
zipp==3.20.2
================================================
FILE: swap.py
================================================
import argparse
import os
import cv2
import numpy as np
import torch
import Image
from insightface.app import FaceAnalysis
import face_align
faceAnalysis = FaceAnalysis(name='buffalo_l')
faceAnalysis.prepare(ctx_id=0, det_size=(512, 512))
from StyleTransferModel_128 import StyleTransferModel
def parse_arguments():
parser = argparse.ArgumentParser(description='Process command line arguments')
parser.add_argument('--target', required=True, help='Target path')
parser.add_argument('--source', required=True, help='Source path')
parser.add_argument('--outputPath', required=True, help='Output path')
parser.add_argument('--modelPath', required=True, help='Model path')
parser.add_argument('--no-paste-back', action='store_true', help='Disable pasting back the swapped face onto the original image')
parser.add_argument('--resolution', type=int, default=128, help='Resolution')
parser.add_argument('--face_attribute_direction', default=None, help='Path of direction.npy')
parser.add_argument('--face_attribute_steps', type=float, default=0, help='face_attribute_steps < 0 or face_attribute_steps > 0')
return parser.parse_args()
def get_device():
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def load_model(model_path):
device = get_device()
model = StyleTransferModel().to(device)
model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
model.eval()
return model
def swap_face(model, target_face, source_face_latent):
device = get_device()
target_tensor = torch.from_numpy(target_face).to(device)
source_tensor = torch.from_numpy(source_face_latent).to(device)
with torch.no_grad():
swapped_tensor = model(target_tensor, source_tensor)
swapped_face = Image.postprocess_face(swapped_tensor)
return swapped_face, swapped_tensor
def create_target(target_image, resolution):
if isinstance(target_image, str):
target_image = cv2.imread(target_image)
target_face = faceAnalysis.get(target_image)[0]
aligned_target_face, M = face_align.norm_crop2(target_image, target_face.kps, resolution)
target_face_blob = Image.getBlob(aligned_target_face, (resolution, resolution))
return target_face_blob, M
def create_source(source_img_path):
source_image = cv2.imread(source_img_path)
faces = faceAnalysis.get(source_image)
if(len(faces) == 0): return None
source_face = faces[0]
source_latent = Image.getLatent(source_face)
return source_latent
def main():
args = parse_arguments()
# Access the arguments
target_image_path = args.target
source = args.source
output_path = args.outputPath
model_path = args.modelPath
face_attribute_direction = args.face_attribute_direction
face_attribute_steps = args.face_attribute_steps
model = load_model(model_path)
target_img = cv2.imread(target_image_path)
target_face_blob, M = create_target(target_img, args.resolution)
source_latent = create_source(source)
if face_attribute_direction is not None:
direction = np.load(face_attribute_direction)
direction = direction / np.linalg.norm(direction)
source_latent += direction * face_attribute_steps
swapped_face, _ = swap_face(model, target_face_blob, source_latent)
if not args.no_paste_back:
swapped_face = Image.blend_swapped_image(swapped_face, target_img, M)
output_folder = os.path.dirname(output_path)
os.makedirs(output_folder, exist_ok=True)
cv2.imwrite(output_path, swapped_face)
if __name__ == "__main__":
main()
================================================
FILE: train.py
================================================
from datetime import datetime
import os
import random
import torch
import torch.optim as optim
import torch.nn.functional as F
import Image
import ModelFormat
from StyleTransferLoss import StyleTransferLoss
import onnxruntime as rt
import cv2
from insightface.data import get_image as ins_get_image
from insightface.app import FaceAnalysis
import face_align
from StyleTransferModel_128 import StyleTransferModel
from torch.utils.tensorboard import SummaryWriter
inswapper_128_path = 'inswapper_128.onnx'
img_size = 128
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
inswapperInferenceSession = rt.InferenceSession(inswapper_128_path, providers=providers)
faceAnalysis = FaceAnalysis(name='buffalo_l')
faceAnalysis.prepare(ctx_id=0, det_size=(512, 512))
def get_device():
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
style_loss_fn = StyleTransferLoss().to(get_device())
def train(datasetDir, learning_rate=0.0001, model_path=None, outputModelFolder='', saveModelEachSteps = 1, stopAtSteps=None, logDir=None, previewDir=None, saveAs_onnx = False, resolutions = [128], enableDataAugmentation = False):
device = get_device()
print(f"Using device: {device}")
model = StyleTransferModel().to(device)
if model_path is not None:
model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
print(f"Loaded model from {model_path}")
lastSteps = int(model_path.split('-')[-1].split('.')[0])
print(f"Resuming training from step {lastSteps}")
else:
lastSteps = 0
model.train()
model = model.to(device)
# Initialize optimizer
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Initialize TensorBoard writer
if logDir is not None:
train_writer = SummaryWriter(os.path.join(logDir, "training"))
val_writer = SummaryWriter(os.path.join(logDir, "validation"))
steps = 0
image = os.listdir(datasetDir)
resolutionIndex = 0
# Training loop
while True:
start_time = datetime.now()
resolution = resolutions[resolutionIndex%len(resolutions)]
targetFaceIndex = random.randint(0, len(image)-1)
sourceFaceIndex = random.randint(0, len(image)-1)
target_img=cv2.imread(f"{datasetDir}/{image[targetFaceIndex]}")
if enableDataAugmentation and steps % 2 == 0:
target_img = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)
target_img = cv2.cvtColor(target_img, cv2.COLOR_GRAY2BGR)
faces = faceAnalysis.get(target_img)
if targetFaceIndex != sourceFaceIndex:
source_img = cv2.imread(f"{datasetDir}/{image[sourceFaceIndex]}")
faces2 = faceAnalysis.get(source_img)
else:
faces2 = faces
if len(faces) > 0 and len(faces2) > 0:
new_aligned_face, _ = face_align.norm_crop2(target_img, faces[0].kps, img_size)
blob = Image.getBlob(new_aligned_face)
latent = Image.getLatent(faces2[0])
else:
continue
if targetFaceIndex != sourceFaceIndex:
input = {inswapperInferenceSession.get_inputs()[0].name: blob,
inswapperInferenceSession.get_inputs()[1].name: latent}
expected_output = inswapperInferenceSession.run([inswapperInferenceSession.get_outputs()[0].name], input)[0]
else:
expected_output = blob
expected_output_tensor = torch.from_numpy(expected_output).to(device)
if resolution != 128:
new_aligned_face, _ = face_align.norm_crop2(target_img, faces[0].kps, resolution)
blob = Image.getBlob(new_aligned_face, (resolution, resolution))
latent_tensor = torch.from_numpy(latent).to(device)
target_input_tensor = torch.from_numpy(blob).to(device)
optimizer.zero_grad()
output = model(target_input_tensor, latent_tensor)
if (resolution != 128):
output = F.interpolate(output, size=(128, 128), mode='bilinear', align_corners=False)
content_loss, identity_loss = style_loss_fn(output, expected_output_tensor)
loss = content_loss
if identity_loss is not None:
loss +=identity_loss
loss.backward()
optimizer.step()
steps += 1
totalSteps = steps + lastSteps
if logDir is not None:
train_writer.add_scalar("Loss/total", loss.item(), totalSteps)
train_writer.add_scalar("Loss/content_loss", content_loss.item(), totalSteps)
if identity_loss is not None:
train_writer.add_scalar("Loss/identity_loss", identity_loss.item(), totalSteps)
elapsed_time = datetime.now() - start_time
print(f"Total Steps: {totalSteps}, Step: {steps}, Loss: {loss.item():.4f}, Elapsed time: {elapsed_time}")
if steps % saveModelEachSteps == 0:
outputModelPath = f"reswapper-{totalSteps}.pth"
if outputModelFolder != '':
outputModelPath = f"{outputModelFolder}/{outputModelPath}"
saveModel(model, outputModelPath)
validation_total_loss, validation_content_loss, validation_identity_loss, swapped_face, swapped_face_256 = validate(outputModelPath)
if previewDir is not None:
cv2.imwrite(f"{previewDir}/{totalSteps}.jpg", swapped_face)
cv2.imwrite(f"{previewDir}/{totalSteps}_256.jpg", swapped_face_256)
if logDir is not None:
val_writer.add_scalar("Loss/total", validation_total_loss.item(), totalSteps)
val_writer.add_scalar("Loss/content_loss", validation_content_loss.item(), totalSteps)
if validation_identity_loss is not None:
val_writer.add_scalar("Loss/identity_loss", validation_identity_loss.item(), totalSteps)
if saveAs_onnx :
ModelFormat.save_as_onnx_model(outputModelPath)
if stopAtSteps is not None and steps == stopAtSteps:
exit()
resolutionIndex += 1
def saveModel(model, outputModelPath):
torch.save(model.state_dict(), outputModelPath)
def load_model(model_path):
device = get_device()
model = StyleTransferModel().to(device)
model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
model.eval()
return model
def swap_face(model, target_face, source_face_latent):
device = get_device()
target_tensor = torch.from_numpy(target_face).to(device)
source_tensor = torch.from_numpy(source_face_latent).to(device)
with torch.no_grad():
swapped_tensor = model(target_tensor, source_tensor)
swapped_face = Image.postprocess_face(swapped_tensor)
return swapped_face, swapped_tensor
# test image
test_img = ins_get_image('t1')
test_faces = faceAnalysis.get(test_img)
test_faces = sorted(test_faces, key = lambda x : x.bbox[0])
test_target_face, _ = face_align.norm_crop2(test_img, test_faces[0].kps, img_size)
test_target_face = Image.getBlob(test_target_face)
test_l = Image.getLatent(test_faces[2])
test_target_face_256, _ = face_align.norm_crop2(test_img, test_faces[0].kps, 256)
test_target_face_256 = Image.getBlob(test_target_face_256, (256, 256))
test_input = {inswapperInferenceSession.get_inputs()[0].name: test_target_face,
inswapperInferenceSession.get_inputs()[1].name: test_l}
test_inswapperOutput = inswapperInferenceSession.run([inswapperInferenceSession.get_outputs()[0].name], test_input)[0]
def validate(modelPath):
model = load_model(modelPath)
swapped_face, swapped_tensor= swap_face(model, test_target_face, test_l)
swapped_face_256, _= swap_face(model, test_target_face_256, test_l)
validation_content_loss, validation_identity_loss = style_loss_fn(swapped_tensor, torch.from_numpy(test_inswapperOutput).to(get_device()))
validation_total_loss = validation_content_loss
if validation_identity_loss is not None:
validation_total_loss += validation_identity_loss
return validation_total_loss, validation_content_loss, validation_identity_loss, swapped_face, swapped_face_256
def main():
outputModelFolder = "model"
modelPath = None
# modelPath = f"{outputModelFolder}/reswapper-<step>.pth"
logDir = "training/log"
previewDir = "training/preview"
datasetDir = "FFHQ"
os.makedirs(outputModelFolder, exist_ok=True)
os.makedirs(previewDir, exist_ok=True)
train(
datasetDir=datasetDir,
model_path=modelPath,
learning_rate=0.0001,
# resolutions = [128, 256],
# enableDataAugmentation=True,
outputModelFolder=outputModelFolder,
saveModelEachSteps = 1000,
stopAtSteps = 70000,
logDir=f"{logDir}/{datetime.now().strftime('%Y%m%d %H%M%S')}",
previewDir=previewDir)
if __name__ == "__main__":
main()
================================================
FILE: weight_transfer.py
================================================
import onnx
from onnx import numpy_helper
import torch
# Referring to PR #10. Thanks, @blend-er
def arcface_onnx_to_pth(arcface_onnx_path="~/.insightface/buffalo_l/w600k_r50.onnx", output_model_path="arcface_w600k_r50.pth"):
import iresnet
arcface = iresnet.iresnet50()
onnx_model = onnx.load(arcface_onnx_path)
INTIALIZERS = onnx_model.graph.initializer
transfer_weights = {}
for initializer in INTIALIZERS:
W = numpy_helper.to_array(initializer)
transfer_weights[initializer.name] = W
weight_shapes = {}
for n, p in arcface.named_parameters():
weight_shapes[n] = '-'.join([str(x) for x in list(p.shape)])
print(f'To:')
for k, v in arcface.state_dict().items():
print(k)
print(v.shape, '\n')
print(f'From:')
for k, v in transfer_weights.items():
print(k)
print(v.shape, '\n')
renamed_weights = {}
total_weight_count = len(transfer_weights)
replacement_dict = {
'685':'conv1.weight',
'686':'conv1.bias',
'688':'layer1.0.conv1.weight',
'689':'layer1.0.conv1.bias',
'691':'layer1.0.conv2.weight',
'692':'layer1.0.conv2.bias',
'694':'layer1.0.downsample.0.weight',
'695':'layer1.0.downsample.0.bias',
'697':'layer1.1.conv1.weight',
'698':'layer1.1.conv1.bias',
'700':'layer1.1.conv2.weight',
'701':'layer1.1.conv2.bias',
'703':'layer1.2.conv1.weight',
'704':'layer1.2.conv1.bias',
'706':'layer1.2.conv2.weight',
'707':'layer1.2.conv2.bias',
'709':'layer2.0.conv1.weight',
'710':'layer2.0.conv1.bias',
'712':'layer2.0.conv2.weight',
'713':'layer2.0.conv2.bias',
'715':'layer2.0.downsample.0.weight',
'716':'layer2.0.downsample.0.bias',
'718':'layer2.1.conv1.weight',
'719':'layer2.1.conv1.bias',
'721':'layer2.1.conv2.weight',
'722':'layer2.1.conv2.bias',
'724':'layer2.2.conv1.weight',
'725':'layer2.2.conv1.bias',
'727':'layer2.2.conv2.weight',
'728':'layer2.2.conv2.bias',
'730':'layer2.3.conv1.weight',
'731':'layer2.3.conv1.bias',
'733':'layer2.3.conv2.weight',
'734':'layer2.3.conv2.bias',
# layer 3
# JS
# x="";
# index=751;
# for(let n=2;n<=13;n++){
# x+=`'${index}':'layer3.${n}.conv1.weight',
# '${index+1}':'layer3.${n}.conv1.bias',
# '${index+3}':'layer3.${n}.conv2.weight',
# '${index+4}':'layer3.${n}.conv2.bias',`
# index+=6
# }
'736':'layer3.0.conv1.weight',
'737':'layer3.0.conv1.bias',
'739':'layer3.0.conv2.weight',
'740':'layer3.0.conv2.bias',
'742':'layer3.0.downsample.0.weight',
'743':'layer3.0.downsample.0.bias',
'745':'layer3.1.conv1.weight',
'746':'layer3.1.conv1.bias',
'748':'layer3.1.conv2.weight',
'749':'layer3.1.conv2.bias',
'751':'layer3.2.conv1.weight',
'752':'layer3.2.conv1.bias',
'754':'layer3.2.conv2.weight',
'755':'layer3.2.conv2.bias',
'757':'layer3.3.conv1.weight',
'758':'layer3.3.conv1.bias',
'760':'layer3.3.conv2.weight',
'761':'layer3.3.conv2.bias',
'763':'layer3.4.conv1.weight',
'764':'layer3.4.conv1.bias',
'766':'layer3.4.conv2.weight',
'767':'layer3.4.conv2.bias',
'769':'layer3.5.conv1.weight',
'770':'layer3.5.conv1.bias',
'772':'layer3.5.conv2.weight',
'773':'layer3.5.conv2.bias',
'775':'layer3.6.conv1.weight',
'776':'layer3.6.conv1.bias',
'778':'layer3.6.conv2.weight',
'779':'layer3.6.conv2.bias',
'781':'layer3.7.conv1.weight',
'782':'layer3.7.conv1.bias',
'784':'layer3.7.conv2.weight',
'785':'layer3.7.conv2.bias',
'787':'layer3.8.conv1.weight',
'788':'layer3.8.conv1.bias',
'790':'layer3.8.conv2.weight',
'791':'layer3.8.conv2.bias',
'793':'layer3.9.conv1.weight',
'794':'layer3.9.conv1.bias',
'796':'layer3.9.conv2.weight',
'797':'layer3.9.conv2.bias',
'799':'layer3.10.conv1.weight',
'800':'layer3.10.conv1.bias',
'802':'layer3.10.conv2.weight',
'803':'layer3.10.conv2.bias',
'805':'layer3.11.conv1.weight',
'806':'layer3.11.conv1.bias',
'808':'layer3.11.conv2.weight',
'809':'layer3.11.conv2.bias',
'811':'layer3.12.conv1.weight',
'812':'layer3.12.conv1.bias',
'814':'layer3.12.conv2.weight',
'815':'layer3.12.conv2.bias',
'817':'layer3.13.conv1.weight',
'818':'layer3.13.conv1.bias',
'820':'layer3.13.conv2.weight',
'821':'layer3.13.conv2.bias',
#layer 4
'823':'layer4.0.conv1.weight',
'824':'layer4.0.conv1.bias',
'826':'layer4.0.conv2.weight',
'827':'layer4.0.conv2.bias',
'829':'layer4.0.downsample.0.weight',
'830':'layer4.0.downsample.0.bias',
'832':'layer4.1.conv1.weight',
'833':'layer4.1.conv1.bias',
'835':'layer4.1.conv2.weight',
'836':'layer4.1.conv2.bias',
'838':'layer4.2.conv1.weight',
'839':'layer4.2.conv1.bias',
'841':'layer4.2.conv2.weight',
'842':'layer4.2.conv2.bias',
'843':'prelu.weight',
'844':'layer1.0.prelu.weight',
'845':'layer1.1.prelu.weight',
'846':'layer1.2.prelu.weight',
'847':'layer2.0.prelu.weight',
'848':'layer2.1.prelu.weight',
'849':'layer2.2.prelu.weight',
'850':'layer2.3.prelu.weight',
'851':'layer3.0.prelu.weight',
'852':'layer3.1.prelu.weight',
'853':'layer3.2.prelu.weight',
'854':'layer3.3.prelu.weight',
'855':'layer3.4.prelu.weight',
'856':'layer3.5.prelu.weight',
'857':'layer3.6.prelu.weight',
'858':'layer3.7.prelu.weight',
'859':'layer3.8.prelu.weight',
'860':'layer3.9.prelu.weight',
'861':'layer3.10.prelu.weight',
'862':'layer3.11.prelu.weight',
'863':'layer3.12.prelu.weight',
'864':'layer3.13.prelu.weight',
'865':'layer4.0.prelu.weight',
'866':'layer4.1.prelu.weight',
'867':'layer4.2.prelu.weight'
}
renamed_weights = {}
#rename
for fromName, toName in replacement_dict.items():
renamed_weights[toName] = transfer_weights[fromName]
if 'prelu' in toName:
renamed_weights[toName] = renamed_weights[toName].reshape(list(transfer_weights[fromName].shape)[0])
renamed_weights[toName] = torch.tensor(renamed_weights[toName])
del weight_shapes[toName]
del transfer_weights[fromName]
#same name
for k, v in transfer_weights.copy().items():
if 'bn1.weight' in k or 'bn1.bias' in k or 'features.weight' in k or 'features.bias' in k or 'fc.' in k or 'bn2.weight' in k or 'bn2.bias' in k:
renamed_weights[k] = transfer_weights[k]
renamed_weights[k] = torch.tensor(transfer_weights[k])
del weight_shapes[k]
del transfer_weights[k]
from torch import nn
#load bn running_mean & running_var
for name, layer in arcface.named_modules():
if isinstance(layer, nn.BatchNorm2d) or isinstance(layer, nn.BatchNorm1d):
if '.bn1' in name or name == 'bn2' or name == 'features':
layer.running_mean = torch.tensor(transfer_weights[f'{name}.running_mean'])
layer.running_var = torch.tensor(transfer_weights[f'{name}.running_var'])
del transfer_weights[f'{name}.running_mean']
del transfer_weights[f'{name}.running_var']
# print(f"Found BatchNorm layer: {name}")
arcface.load_state_dict(renamed_weights, strict=False)
arcface.eval()
tgt = torch.randn(1, 3, 112, 112)
torch.onnx.export(
arcface,
(tgt),
"arcface.onnx",
export_params=True,
opset_version=11,
input_names=['input'],
output_names=['output'],
dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
)
print(f'Complete: {total_weight_count-len(transfer_weights)}/{total_weight_count}\n')
torch.save(arcface.state_dict(), output_model_path)
gitextract_u0251wd1/ ├── FaceAttribute.py ├── Image.py ├── LICENSE ├── ModelFormat.py ├── README.md ├── StyleTransferLoss.py ├── StyleTransferModel_128.py ├── emap.npy ├── face_align.py ├── iresnet.py ├── requirements-colab.txt ├── requirements.txt ├── swap.py ├── train.py └── weight_transfer.py
SYMBOL INDEX (58 symbols across 10 files)
FILE: FaceAttribute.py
function create_linear_direction_dataset (line 6) | def create_linear_direction_dataset(dataset_dir, output_path = None):
function get_direction (line 25) | def get_direction(dataset_a, dataset_b, output_path = None):
FILE: Image.py
function postprocess_face (line 9) | def postprocess_face(face_tensor):
function getBlob (line 16) | def getBlob(aimg, input_size = (128, 128)):
function getLatent (line 21) | def getLatent(source_face):
function blend_swapped_image (line 28) | def blend_swapped_image(swapped_face, target_image, M):
function drawKeypoints (line 89) | def drawKeypoints(image, keypoints, colorBGR, keypointsRadius=2):
FILE: ModelFormat.py
function save_as_onnx_model (line 7) | def save_as_onnx_model(torch_model_path, save_emap=True, img_size = 128,...
FILE: StyleTransferLoss.py
class StyleTransferLoss (line 10) | class StyleTransferLoss(nn.Module):
method __init__ (line 11) | def __init__(self, device='cuda', face_analysis = None):
method extract_face_latent (line 24) | def extract_face_latent(self, image):
method forward (line 36) | def forward(self, output_image, target_content):
FILE: StyleTransferModel_128.py
class StyleTransferModel (line 5) | class StyleTransferModel(nn.Module):
method __init__ (line 6) | def __init__(self):
method forward (line 51) | def forward(self, target, source):
class StyleBlock (line 87) | class StyleBlock(nn.Module):
method __init__ (line 88) | def __init__(self, in_channels, out_channels, blockIndex):
method normalizeConvRMS (line 98) | def normalizeConvRMS(self, conv):
method forward (line 105) | def forward(self, residual, style):
FILE: face_align.py
function estimate_norm (line 11) | def estimate_norm(lmk, image_size=112,mode='arcface'):
function norm_crop (line 33) | def norm_crop(img, landmark, image_size=112, mode='arcface'):
function norm_crop2 (line 38) | def norm_crop2(img, landmark, image_size=112, mode='arcface'):
function square_crop (line 43) | def square_crop(im, S):
function transform (line 58) | def transform(data, center, output_size, scale, rotation):
function trans_points2d (line 77) | def trans_points2d(pts, M):
function trans_points3d (line 89) | def trans_points3d(pts, M):
function trans_points (line 104) | def trans_points(pts, M):
FILE: iresnet.py
function conv3x3 (line 10) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
function conv1x1 (line 22) | def conv1x1(in_planes, out_planes, stride=1):
class IBasicBlock (line 31) | class IBasicBlock(nn.Module):
method __init__ (line 33) | def __init__(self, inplanes, planes, stride=1, downsample=None,
method forward_impl (line 49) | def forward_impl(self, x):
method forward (line 62) | def forward(self, x):
class IResNet (line 69) | class IResNet(nn.Module):
method __init__ (line 71) | def __init__(self,
method _make_layer (line 124) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
method forward (line 150) | def forward(self, x):
function _iresnet (line 167) | def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
function iresnet18 (line 174) | def iresnet18(pretrained=False, progress=True, **kwargs):
function iresnet34 (line 179) | def iresnet34(pretrained=False, progress=True, **kwargs):
function iresnet50 (line 184) | def iresnet50(pretrained=False, progress=True, **kwargs):
function iresnet100 (line 189) | def iresnet100(pretrained=False, progress=True, **kwargs):
function iresnet200 (line 194) | def iresnet200(pretrained=False, progress=True, **kwargs):
FILE: swap.py
function parse_arguments (line 16) | def parse_arguments():
function get_device (line 30) | def get_device():
function load_model (line 33) | def load_model(model_path):
function swap_face (line 40) | def swap_face(model, target_face, source_face_latent):
function create_target (line 53) | def create_target(target_image, resolution):
function create_source (line 63) | def create_source(source_img_path):
function main (line 75) | def main():
FILE: train.py
function get_device (line 31) | def get_device():
function train (line 35) | def train(datasetDir, learning_rate=0.0001, model_path=None, outputModel...
function saveModel (line 168) | def saveModel(model, outputModelPath):
function load_model (line 171) | def load_model(model_path):
function swap_face (line 179) | def swap_face(model, target_face, source_face_latent):
function validate (line 209) | def validate(modelPath):
function main (line 222) | def main():
FILE: weight_transfer.py
function arcface_onnx_to_pth (line 6) | def arcface_onnx_to_pth(arcface_onnx_path="~/.insightface/buffalo_l/w600...
Condensed preview — 15 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (95K chars).
[
{
"path": "FaceAttribute.py",
"chars": 779,
"preview": "import os\nimport numpy as np\n\nimport swap\n\ndef create_linear_direction_dataset(dataset_dir, output_path = None):\n ima"
},
{
"path": "Image.py",
"chars": 2772,
"preview": "\nimport cv2\nimport numpy as np\n\nemap = np.load(\"emap.npy\")\ninput_std = 255.0\ninput_mean = 0.0\n\ndef postprocess_face(face"
},
{
"path": "LICENSE",
"chars": 34523,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\n Version 3, 19 November 2007\n\n Copyright (C)"
},
{
"path": "ModelFormat.py",
"chars": 2274,
"preview": "import numpy as np\nimport onnx\nimport torch\n\nfrom StyleTransferModel_128 import StyleTransferModel\n\ndef save_as_onnx_mod"
},
{
"path": "README.md",
"chars": 9613,
"preview": "# ReSwapper\n\nReSwapper aims to reproduce the implementation of inswapper. This repository provides code for training, in"
},
{
"path": "StyleTransferLoss.py",
"chars": 1942,
"preview": "import cv2\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom insightface.app import FaceAnalysis\nfrom pytorch_m"
},
{
"path": "StyleTransferModel_128.py",
"chars": 4430,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass StyleTransferModel(nn.Module):\n def __init_"
},
{
"path": "face_align.py",
"chars": 3486,
"preview": "import cv2\nimport numpy as np\nfrom skimage import transform as trans\n\n\narcface_dst = np.array(\n [[38.2946, 51.6963], "
},
{
"path": "iresnet.py",
"chars": 7574,
"preview": "# a modified version of https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/backbones/iresn"
},
{
"path": "requirements-colab.txt",
"chars": 1509,
"preview": "absl-py==2.1.0\naddict==2.4.0\nalbucore==0.0.17\nalbumentations==1.4.17\nannotated-types==0.7.0\ncertifi==2024.8.30\nchardet=="
},
{
"path": "requirements.txt",
"chars": 1541,
"preview": "absl-py==2.1.0\naddict==2.4.0\nalbucore==0.0.17\nalbumentations==1.4.17\nannotated-types==0.7.0\ncertifi==2024.8.30\nchardet=="
},
{
"path": "swap.py",
"chars": 3644,
"preview": "import argparse\nimport os\n\nimport cv2\nimport numpy as np\nimport torch\nimport Image\nfrom insightface.app import FaceAnaly"
},
{
"path": "train.py",
"chars": 8933,
"preview": "from datetime import datetime\nimport os\nimport random\nimport torch\nimport torch.optim as optim\nimport torch.nn.functiona"
},
{
"path": "weight_transfer.py",
"chars": 8502,
"preview": "import onnx\nfrom onnx import numpy_helper\nimport torch\n\n# Referring to PR #10. Thanks, @blend-er\ndef arcface_onnx_to_pth"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the somanchiu/ReSwapper GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 15 files (89.4 KB), approximately 23.1k tokens, and a symbol index with 58 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.