Merge pull request #11 from pedrodiamel/master

python interface

.gitignore

@@ -1,3 +1,85 @@
 *.aux
 *.log
 *.gz
+
+__pycache__
+books
+project
+
+# 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/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# 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/

README.md

@@ -1,7 +1,51 @@
 # PlotNeuralNet
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2526396.svg)](https://doi.org/10.5281/zenodo.2526396)
 
-Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code. 
+Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, lets consolidate any improvements that you make and fix any bugs to help more people with this code.
+
+## TODO
+
+- [X] Python interfaz
+- [ ] Generate Pytorch graph architecture
+- [ ] Add easy legend functionality
+- [ ] Add more layer shapes like TruncatedPyramid, 2DSheet etc
+
+## Usage
+
+    mkdir my_project
+    cd my_project
+    vim my_arch.py
+
+        import sys
+        sys.path.append('../')
+        from core.tikzeng import *
+
+        # defined your arch
+        arch = [
+            to_head( '..' ),
+            to_cor(),
+            to_begin(),
+            to_Conv("conv1", 512, 64, offset="(0,0,0)", to="(0,0,0)", height=64, depth=64, width=2 ),
+            to_Pool("pool1", offset="(0,0,0)", to="(conv1-east)"),
+            to_Conv("conv2", 128, 64, offset="(1,0,0)", to="(pool1-east)", height=32, depth=32, width=2 ),
+            to_connection( "pool1", "conv2"),
+            to_Pool("pool2", offset="(0,0,0)", to="(conv2-east)", height=28, depth=28, width=1),
+            to_SoftMax("soft1", 10 ,"(3,0,0)", "(pool1-east)", caption="SOFT"  ),
+            to_connection("pool2", "soft1"),
+            to_end()
+            ]
+
+        def main():
+            namefile = str(sys.argv[0]).split('.')[0]
+            to_generate(arch, namefile + '.tex' )
+
+        if __name__ == '__main__':
+            main()
+
+    bash ../tikzmake.sh my_arch
+
+## Examples
+
 Following are some network representations:
 
 <p align="center"><img  src="https://user-images.githubusercontent.com/17570785/50308846-c2231880-049c-11e9-8763-3daa1024de78.png" width="85%" height="85%"></p>

core/blocks.py

@@ -0,0 +1,75 @@
+
+from .tikzeng import *
+
+#define new block
+def block_2ConvPool( name, botton, top, s_filer=256, n_filer=64, offset="(1,0,0)", size=(32,32,3.5), opacity=0.5 ):
+    return [
+    to_ConvConvRelu( 
+        name="ccr_{}".format( name ),
+        s_filer=str(s_filer), 
+        n_filer=(n_filer,n_filer), 
+        offset=offset, 
+        to="({}-east)".format( botton ), 
+        width=(size[2],size[2]), 
+        height=size[0], 
+        depth=size[1],   
+        ),    
+    to_Pool(         
+        name="{}".format( top ), 
+        offset="(0,0,0)", 
+        to="(ccr_{}-east)".format( name ),  
+        width=1,         
+        height=size[0] - int(size[0]/4), 
+        depth=size[1] - int(size[0]/4), 
+        opacity=opacity, ),
+    to_connection( 
+        "{}".format( botton ), 
+        "ccr_{}".format( name )
+        )
+    ]
+
+
+def block_Unconv( name, botton, top, s_filer=256, n_filer=64, offset="(1,0,0)", size=(32,32,3.5), opacity=0.5 ):
+    return [
+        to_UnPool(  name='unpool_{}'.format(name),    offset=offset,    to="({}-east)".format(botton),         width=1,              height=size[0],       depth=size[1], opacity=opacity ),
+        to_ConvRes( name='ccr_res_{}'.format(name),   offset="(0,0,0)", to="(unpool_{}-east)".format(name),    s_filer=str(s_filer), n_filer=str(n_filer), width=size[2], height=size[0], depth=size[1], opacity=opacity ),       
+        to_Conv(    name='ccr_{}'.format(name),       offset="(0,0,0)", to="(ccr_res_{}-east)".format(name),   s_filer=str(s_filer), n_filer=str(n_filer), width=size[2], height=size[0], depth=size[1] ),
+        to_ConvRes( name='ccr_res_c_{}'.format(name), offset="(0,0,0)", to="(ccr_{}-east)".format(name),       s_filer=str(s_filer), n_filer=str(n_filer), width=size[2], height=size[0], depth=size[1], opacity=opacity ),       
+        to_Conv(    name='{}'.format(top),            offset="(0,0,0)", to="(ccr_res_c_{}-east)".format(name), s_filer=str(s_filer), n_filer=str(n_filer), width=size[2], height=size[0], depth=size[1] ),
+        to_connection( 
+            "{}".format( botton ), 
+            "unpool_{}".format( name ) 
+            )
+    ]
+
+
+
+
+def block_Res( num, name, botton, top, s_filer=256, n_filer=64, offset="(0,0,0)", size=(32,32,3.5), opacity=0.5 ):
+    lys = []
+    layers = [ *[ '{}_{}'.format(name,i) for i in range(num-1) ], top]
+    for name in layers:        
+        ly = [ to_Conv( 
+            name='{}'.format(name),       
+            offset=offset, 
+            to="({}-east)".format( botton ),   
+            s_filer=str(s_filer), 
+            n_filer=str(n_filer), 
+            width=size[2],
+            height=size[0],
+            depth=size[1]
+            ),
+            to_connection( 
+                "{}".format( botton  ), 
+                "{}".format( name ) 
+                )
+            ]
+        botton = name
+        lys+=ly
+
+    lys += [
+        to_skip( of=layers[1], to=layers[-2], pos=1.25),
+    ]
+    return lys
+
+