Update normalization function to avoid -inf when applying the log on GRD

SAR2SAR_GRD_test.ipynb

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 **[Download the notebook](https://gitlab.telecom-paris.fr/ring/sar2sar/-/raw/master/SAR2SAR_GRD_test.ipynb?inline=false) and then import it under Google Colab**
 
 <a href="https://colab.research.google.com/" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
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 # SAR2SAR: a self-supervised despeckling algorithm for SAR images
 ## Emanuele Dalsasso, Loïc Denis, Florence Tupin
 
 Please note that the training set is only composed of **GRD** SAR images, thus this testing code is specific to this data.
 
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 ## Resources
 - [Paper (ArXiv)](https://arxiv.org/abs/2006.15037)
 
 To cite the article:
 
     @article{dalsasso2020sar2sar,
         title={{SAR2SAR}: a self-supervised despeckling algorithm for {SAR} images},
         author={Emanuele Dalsasso and Loïc Denis and Florence Tupin},
         journal={arXiv preprint arXiv:2006.15037},
         year={2020}
     }
 
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 ## 0. Enable GPU and save copy on Drive to enable editing
 Runtime -> Change runtime type -> Hardware accelerator: GPU
 
 File -> Save a copy in Drive
 
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 ## 1. Download network weights and code
 
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-``` 
-from google_drive_downloader import GoogleDriveDownloader as gdd
-
-gdd.download_file_from_google_drive(file_id='1U8UT87k1AmiKhgE-fSkqSq5xmv32pOnu',
-                                    dest_path='./SAR2SAR-GRD-test.zip',
-                                    unzip=True)
+``` python
+!wget https://gitlab.telecom-paris.fr/ring/SAR2SAR/-/raw/master/network_weights/SAR2SAR-GRD-test.zip
+!unzip /content/SAR2SAR-GRD-test.zip
 ```
 
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 ## 2. Install compatible version of tensorflow
 
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-``` 
+``` python
 !pip uninstall -y tensorflow
 ```
 
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-``` 
+``` python
 !pip install tensorflow-gpu==1.13.1
 ```
 
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 ## 3. Test on real data
 Some **GRD** images in **amplitude** format can be found in the folder _/content/SAR2SAR-GRD-test/test_data/_
 
 
 To test on custom data, upload your single channel GRD images in a numpy array with shape [ydim, xdim] in the folder _/content/SAR2SAR-GRD-test/test_data/_
 
 Results are stored in _/content/test_
 
 At each time a test is run, clean the _/content/test_ directory otherwise the results will be overwritten.
 
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-``` 
+``` python
 !python /content/SAR2SAR-GRD-test/main.py
 ```
 
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 When image dimension exeeds 256, the U-Net is scanned over the image with a default stride of 64 pixels. To change it to a custom value, run the cell below (here it is set to 32, allowing more quality at the cost of a greater runtime)
 
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-``` 
+``` python
 !python /content/SAR2SAR-GRD-test/main.py --stride_size=32
 ```