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QA.md

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+# Frequently Asked Questions
+
+### 1. How to reproduce your results in the [PIRM18-SR Challenge](https://www.pirm2018.org/PIRM-SR.html) (with low perceptual index)?
+
+First, the released ESRGAN model in the GitHub (`RRDB_ESRGAN_x4.pth`) is **different** from the model we submitted in the competition.
+We found that the lower perceptual index does not always guarantee a better visual quality.
+The aims for the competition and our ESRGAN work will be a bit different.
+We think the aim for the competition is the lower perceptual index and the aim for our ESRGAN work is the better visual quality.
+> More analyses can be found in Sec 4.1 and Sec 5 in [PIRM18-SR Chanllenge report](https://arxiv.org/pdf/1809.07517.pdf).
+> It points out that PI (perceptual index) is well correlated with the human-opinion-scores on a coarse scale, but it is not always well-correlated with these scores on a finer scale. This highlights the urgent need for better perceptual quality metrics.)
+
+Therefore, in the PIRM18-SR Challenge competition, we used several tricks for the best perceptual index (see Section 4.5 in the [paper](https://arxiv.org/abs/1809.00219)).
+
+Here, we provid the models and codes used in the competition, which is able to produce the results on the `PIRM test dataset` (we use MATLAB 2016b/2017a):
+
+| Group       | Perceptual index         | RMSE  |
+| ------------- |:-------------:| -----:|
+| SuperSR     | 1.978 | 15.30 |
+
+> 1. Download the model and codes from [GoogleDrive](https://drive.google.com/file/d/1l0gBRMqhVLpL_-7R7aN-q-3hnv5ADFSM/view?usp=sharing)
+> 2. Put LR input images in the `LR` folder
+> 3. Run `python test.py`
+> 4. Run `main_reverse_filter.m` in MATLAB as a post processing
+> 5. The results on my computer are: Perceptual index: **1.9777** and RMSE: **15.304**
+
+
+### 2. How do you get the perceptual index in your ESRGAN paper?
+In our paper, we provide the perceptual index in two places.
+
+1). In the Fig. 2, the perceptual index on PIRM self validation dataset is obtained with the **model we submitted in the competition**. 
+Since the pupose of this figure is to show the perception-distortion plane. And we also use the post-precessing here same as in the competition.
+
+2). In the Fig.7, the perceptual indexs are provided as references and they are tested on the data generated by the released ESRGAN model `RRDB_ESRGAN_x4.pth` in the GiuHub.
+Also, there is **no** post-processing when testing the ESRGAN model for better visual quality.

README.md

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+## ESRGAN (Enhanced SRGAN) [:rocket: [BasicSR](https://github.com/xinntao/BasicSR)] [[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)]
+
+:sparkles: **New Updates.**
+
+We have extended ESRGAN to [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN), which is a **more practical algorithm for real-world image restoration**. For example, it can also remove annoying JPEG compression artifacts. <br> You are recommended to have a try :smiley:
+
+In the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo,
+
+- You can still use the original ESRGAN model or your re-trained ESRGAN model. [The model zoo in Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN#european_castle-model-zoo).
+- We provide a more handy inference script, which supports 1) **tile** inference; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images.
+- We also provide a **Windows executable file** `RealESRGAN-ncnn-vulkan` for easier use without installing the environment. This executable file also includes the original ESRGAN model.
+- The full training codes are also released in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo.
+
+Welcome to open issues or open discussions in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo.
+
+- If you have any question, you can open an issue in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo.
+- If you have any good ideas or demands, please open an issue/discussion in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo to let me know.
+- If you have some images that Real-ESRGAN could not well restored, please also open an issue/discussion in the [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) repo. I will record it (but I cannot guarantee to resolve it😛).
+
+Here are some examples for Real-ESRGAN:
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/xinntao/Real-ESRGAN/master/assets/teaser.jpg">
+</p>
+:book: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
+
+> [[Paper](https://arxiv.org/abs/2107.10833)] <br>
+> [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
+> Applied Research Center (ARC), Tencent PCG<br>
+> Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
+
+-----
+
+As there may be some repos have dependency on this ESRGAN repo, we will not modify this ESRGAN repo (especially the codes).
+
+The following is the original README:
+
+#### The training codes are in :rocket: [BasicSR](https://github.com/xinntao/BasicSR). This repo only provides simple testing codes, pretrained models and the network interpolation demo.
+
+[BasicSR](https://github.com/xinntao/BasicSR) is an **open source** image and video super-resolution toolbox based on PyTorch (will extend to more restoration tasks in the future). <br>
+It includes methods such as **EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR**, etc. It now also supports **StyleGAN2**.
+
+### Enhanced Super-Resolution Generative Adversarial Networks
+By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/)
+
+We won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 3) and got the best perceptual index.
+The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
+
+:triangular_flag_on_post: Add [Frequently Asked Questions](https://github.com/xinntao/ESRGAN/blob/master/QA.md).
+
+> For instance,
+> 1. How to reproduce your results in the PIRM18-SR Challenge (with low perceptual index)?
+> 2. How do you get the perceptual index in your ESRGAN paper?
+
+#### BibTeX
+
+    @InProceedings{wang2018esrgan,
+        author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change},
+        title = {ESRGAN: Enhanced super-resolution generative adversarial networks},
+        booktitle = {The European Conference on Computer Vision Workshops (ECCVW)},
+        month = {September},
+        year = {2018}
+    }
+
+<p align="center">
+  <img src="figures/baboon.jpg">
+</p>
+
+The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged dataset with [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (proposed in [EDSR](https://github.com/LimBee/NTIRE2017))) is also able to achive high PSNR performance.
+
+| <sub>Method</sub> | <sub>Training dataset</sub> | <sub>Set5</sub> | <sub>Set14</sub> | <sub>BSD100</sub> | <sub>Urban100</sub> | <sub>Manga109</sub> |
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| <sub>[SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)</sub>| <sub>291</sub>| <sub>30.48/0.8628</sub> |<sub>27.50/0.7513</sub>|<sub>26.90/0.7101</sub>|<sub>24.52/0.7221</sub>|<sub>27.58/0.8555</sub>|
+| <sub>[EDSR](https://github.com/thstkdgus35/EDSR-PyTorch)</sub> | <sub>DIV2K</sub> | <sub>32.46/0.8968</sub> | <sub>28.80/0.7876</sub> | <sub>27.71/0.7420</sub> | <sub>26.64/0.8033</sub> | <sub>31.02/0.9148</sub> |
+| <sub>[RCAN](https://github.com/yulunzhang/RCAN)</sub> |  <sub>DIV2K</sub> | <sub>32.63/0.9002</sub> | <sub>28.87/0.7889</sub> | <sub>27.77/0.7436</sub> | <sub>26.82/ 0.8087</sub>| <sub>31.22/ 0.9173</sub>|
+|<sub>RRDB(ours)</sub>| <sub>DF2K</sub>| <sub>**32.73/0.9011**</sub> |<sub>**28.99/0.7917**</sub> |<sub>**27.85/0.7455**</sub> |<sub>**27.03/0.8153**</sub> |<sub>**31.66/0.9196**</sub>|
+
+## Quick Test
+#### Dependencies
+- Python 3
+- [PyTorch >= 1.0](https://pytorch.org/) (CUDA version >= 7.5 if installing with CUDA. [More details](https://pytorch.org/get-started/previous-versions/))
+- Python packages:  `pip install numpy opencv-python`
+
+### Test models
+1. Clone this github repo.
+```
+git clone https://github.com/xinntao/ESRGAN
+cd ESRGAN
+```
+2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic).
+3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)).
+4. Run test. We provide ESRGAN model and RRDB_PSNR model and you can config in the `test.py`.
+```
+python test.py
+```
+5. The results are in `./results` folder.
+### Network interpolation demo
+You can interpolate the RRDB_ESRGAN and RRDB_PSNR models with alpha in [0, 1].
+
+1. Run `python net_interp.py 0.8`, where *0.8* is the interpolation parameter and you can change it to any value in [0,1].
+2. Run `python test.py models/interp_08.pth`, where *models/interp_08.pth* is the model path.
+
+<p align="center">
+  <img height="400" src="figures/43074.gif">
+</p>
+
+## Perceptual-driven SR Results
+
+You can download all the resutls from [Google Drive](https://drive.google.com/drive/folders/1iaM-c6EgT1FNoJAOKmDrK7YhEhtlKcLx?usp=sharing). (:heavy_check_mark: included;  :heavy_minus_sign: not included; :o: TODO)
+
+HR images can be downloaed from [BasicSR-Datasets](https://github.com/xinntao/BasicSR#datasets).
+
+| Datasets |LR | [*ESRGAN*](https://arxiv.org/abs/1809.00219) | [SRGAN](https://arxiv.org/abs/1609.04802) | [EnhanceNet](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf) | [CX](https://arxiv.org/abs/1803.04626) |
+|:---:|:---:|:---:|:---:|:---:|:---:|
+| Set5 |:heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| Set14 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| BSDS100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark:| :o: |
+| [PIRM](https://pirm.github.io/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :heavy_check_mark: |
+| [OST300](https://arxiv.org/pdf/1804.02815.pdf) |:heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+| urban100 | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+| [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) <br><sup>(val, test)</sup> | :heavy_check_mark: | :heavy_check_mark: | :heavy_minus_sign: | :heavy_check_mark:| :o: |
+
+## ESRGAN
+We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects:
+1. adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers.
+2. employ [Relativistic average GAN](https://ajolicoeur.wordpress.com/relativisticgan/) instead of the vanilla GAN.
+3. improve the perceptual loss by using the features before activation.
+
+In contrast to SRGAN, which claimed that **deeper models are increasingly difficult to train**, our deeper ESRGAN model shows its superior performance with easy training.
+
+<p align="center">
+  <img height="120" src="figures/architecture.jpg">
+</p>
+<p align="center">
+  <img height="180" src="figures/RRDB.png">
+</p>
+
+## Network Interpolation
+We propose the **network interpolation strategy** to balance the visual quality and PSNR.
+
+<p align="center">
+  <img height="500" src="figures/net_interp.jpg">
+</p>
+
+We show the smooth animation with the interpolation parameters changing from 0 to 1.
+Interestingly, it is observed that the network interpolation strategy provides a smooth control of the RRDB_PSNR model and the fine-tuned ESRGAN model.
+
+<p align="center">
+  <img height="480" src="figures/81.gif">
+  &nbsp &nbsp
+  <img height="480" src="figures/102061.gif">
+</p>
+
+## Qualitative Results
+PSNR (evaluated on the Y channel) and the perceptual index used in the PIRM-SR challenge are also provided for reference.
+
+<p align="center">
+  <img src="figures/qualitative_cmp_01.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_02.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_03.jpg">
+</p>
+<p align="center">
+  <img src="figures/qualitative_cmp_04.jpg">
+</p>
+
+## Ablation Study
+Overall visual comparisons for showing the effects of each component in
+ESRGAN. Each column represents a model with its configurations in the top.
+The red sign indicates the main improvement compared with the previous model.
+<p align="center">
+  <img src="figures/abalation_study.png">
+</p>
+
+## BN artifacts
+We empirically observe that BN layers tend to bring artifacts. These artifacts,
+namely BN artifacts, occasionally appear among iterations and different settings,
+violating the needs for a stable performance over training. We find that
+the network depth, BN position, training dataset and training loss
+have impact on the occurrence of BN artifacts.
+<p align="center">
+  <img src="figures/BN_artifacts.jpg">
+</p>
+
+## Useful techniques to train a very deep network
+We find that residual scaling and smaller initialization can help to train a very deep network. More details are in the Supplementary File attached in our [paper](https://arxiv.org/abs/1809.00219).
+
+<p align="center">
+  <img height="250" src="figures/train_deeper_neta.png">
+  <img height="250" src="figures/train_deeper_netb.png">
+</p>
+
+## The influence of training patch size
+We observe that training a deeper network benefits from a larger patch size. Moreover, the deeper model achieves more improvement (∼0.12dB) than the shallower one (∼0.04dB) since larger model capacity is capable of taking full advantage of
+larger training patch size. (Evaluated on Set5 dataset with RGB channels.)
+<p align="center">
+  <img height="250" src="figures/patch_a.png">
+  <img height="250" src="figures/patch_b.png">
+</p>

RRDBNet_arch.py

@@ -0,0 +1,78 @@
+import functools
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def make_layer(block, n_layers):
+    layers = []
+    for _ in range(n_layers):
+        layers.append(block())
+    return nn.Sequential(*layers)
+
+
+class ResidualDenseBlock_5C(nn.Module):
+    def __init__(self, nf=64, gc=32, bias=True):
+        super(ResidualDenseBlock_5C, self).__init__()
+        # gc: growth channel, i.e. intermediate channels
+        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
+        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
+        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
+        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+        # initialization
+        # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    def forward(self, x):
+        x1 = self.lrelu(self.conv1(x))
+        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
+        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
+        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
+        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+        return x5 * 0.2 + x
+
+
+class RRDB(nn.Module):
+    '''Residual in Residual Dense Block'''
+
+    def __init__(self, nf, gc=32):
+        super(RRDB, self).__init__()
+        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
+        self.RDB3 = ResidualDenseBlock_5C(nf, gc)
+
+    def forward(self, x):
+        out = self.RDB1(x)
+        out = self.RDB2(out)
+        out = self.RDB3(out)
+        return out * 0.2 + x
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
+        super(RRDBNet, self).__init__()
+        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
+
+        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
+        self.RRDB_trunk = make_layer(RRDB_block_f, nb)
+        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        #### upsampling
+        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
+
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        fea = self.conv_first(x)
+        trunk = self.trunk_conv(self.RRDB_trunk(fea))
+        fea = fea + trunk
+
+        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
+        out = self.conv_last(self.lrelu(self.HRconv(fea)))
+
+        return out

net_interp.py

@@ -0,0 +1,21 @@
+import sys
+import torch
+from collections import OrderedDict
+
+alpha = float(sys.argv[1])
+
+net_PSNR_path = './models/RRDB_PSNR_x4.pth'
+net_ESRGAN_path = './models/RRDB_ESRGAN_x4.pth'
+net_interp_path = './models/interp_{:02d}.pth'.format(int(alpha*10))
+
+net_PSNR = torch.load(net_PSNR_path)
+net_ESRGAN = torch.load(net_ESRGAN_path)
+net_interp = OrderedDict()
+
+print('Interpolating with alpha = ', alpha)
+
+for k, v_PSNR in net_PSNR.items():
+    v_ESRGAN = net_ESRGAN[k]
+    net_interp[k] = (1 - alpha) * v_PSNR + alpha * v_ESRGAN
+
+torch.save(net_interp, net_interp_path)

test.py

@@ -0,0 +1,37 @@
+import os.path as osp
+import glob
+import cv2
+import numpy as np
+import torch
+import RRDBNet_arch as arch
+
+model_path = 'models/RRDB_ESRGAN_x4.pth'  # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth
+device = torch.device('cpu')  # if you want to run on CPU, change 'cuda' -> cpu
+# device = torch.device('cpu')
+
+test_img_folder = 'LR/*'
+
+model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+model.load_state_dict(torch.load(model_path), strict=True)
+model.eval()
+model = model.to(device)
+
+print('Model path {:s}. \nTesting...'.format(model_path))
+
+idx = 0
+for path in glob.glob(test_img_folder):
+    idx += 1
+    base = osp.splitext(osp.basename(path))[0]
+    print(idx, base)
+    # read images
+    img = cv2.imread(path, cv2.IMREAD_COLOR)
+    img = img * 1.0 / 255
+    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
+    img_LR = img.unsqueeze(0)
+    img_LR = img_LR.to(device)
+
+    with torch.no_grad():
+        output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
+    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
+    output = (output * 255.0).round()
+    cv2.imwrite('results/{:s}_rlt.png'.format(base), output)

transer_RRDB_models.py

@@ -0,0 +1,55 @@
+import os
+import torch
+import RRDBNet_arch as arch
+
+pretrained_net = torch.load('./models/RRDB_ESRGAN_x4.pth')
+save_path = './models/RRDB_ESRGAN_x4.pth'
+
+crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
+crt_net = crt_model.state_dict()
+
+load_net_clean = {}
+for k, v in pretrained_net.items():
+    if k.startswith('module.'):
+        load_net_clean[k[7:]] = v
+    else:
+        load_net_clean[k] = v
+pretrained_net = load_net_clean
+
+print('###################################\n')
+tbd = []
+for k, v in crt_net.items():
+    tbd.append(k)
+
+# directly copy
+for k, v in crt_net.items():
+    if k in pretrained_net and pretrained_net[k].size() == v.size():
+        crt_net[k] = pretrained_net[k]
+        tbd.remove(k)
+
+crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
+crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
+
+for k in tbd.copy():
+    if 'RDB' in k:
+        ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+        if '.weight' in k:
+            ori_k = ori_k.replace('.weight', '.0.weight')
+        elif '.bias' in k:
+            ori_k = ori_k.replace('.bias', '.0.bias')
+        crt_net[k] = pretrained_net[ori_k]
+        tbd.remove(k)
+
+crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
+crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
+crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
+crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
+crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
+crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
+crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
+crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
+crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
+crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
+
+torch.save(crt_net, save_path)
+print('Saving to ', save_path)