Add gitignore, data, model class

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

siamese_nn.py

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+#%%
+"""
+This module is responsible for architecturally defining Siamese Neural Network using PyTorch Framework.
+
+Module consists of class definition for mentioned Neural Network.
+
+Siamese Neural Network bases its functionality on having two images processed at the same time to produce
+two vectors of similarity which then in turn are being passed to Contrastive Loss function, which in the end
+indicates whether or not the samples were similar or dissimilar. 
+"""
+import torch.nn as nn
+from torchsummary import summary
+
+class Siamese_nn(nn.Module):
+    """
+    Siamese_nn class inherits nn.Module from PyTorch framework to create Sequential model.
+ 
+    Class consists of following methods:
+ 
+        - __init__(): responsible for creating featureExtractor that retrieves fingerprint features and
+                      fc which has Fully Connected layer to create similarity vectors,
+        - forwardOne(x): responsible for single action of forward pass in neural network,
+        - forward(input1, input2): responsible for forward pass of two samples at the same time, due to nature of
+                                   Siamese Network. 
+    """
+
+    def __init__(self):
+        """
+        Initializes two important components of Siamese Neural Network:
+            - featureExtractor: responsible for analysing input images to extract features of fingerprint,
+                                it is made of several Convolutional Layers with rising amount of features
+                                extracted for increased precision,
+            - fc: responsible for receiving vector of features and shrinks them to reasonably sized vector.
+        """
+        super(Siamese_nn, self).__init__()
+
+        self.featureExtractor = nn.Sequential(
+            nn.Conv2d(1, 32, kernel_size=5),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, kernel_size=5),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(64, 128, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 128, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Conv2d(128, 128, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.Flatten()
+        )
+        self.fc = nn.Sequential(
+            nn.Linear(28672, 256),
+            nn.ReLU(inplace=True)
+        )
+        
+    def forwardOne(self, x):
+        """
+        This method performs action of forward pass of single image. 
+        
+        It starts from extraction of features and then turning it to
+        vector of features, which is returned as an output.
+        """
+        x = self.featureExtractor(x)
+        x = self.fc(x)
+        return x
+
+    def forward(self, input1, input2):
+        """
+        This method performs forward pass for dataset sample which includes a pair of images.
+        
+        It performs forward pass for both of the images at the same time and returns them as
+        outputs for further training. 
+        """
+        output1 = self.forwardOne(input1)
+        output2 = self.forwardOne(input2)
+        return output1, output2
+
+if __name__ == '__main__':
+    model = Siamese_nn().to('cuda')
+    summary(model, [(1, 153, 136), (1, 153, 136)], 1)
+# %%